Patent Application
20220151539
This application claims the priority of Korean Patent Application No. 10-2020-0155063 filed on Nov. 19, 2020, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein by reference.
The present disclosure relates to biological classification device and method for Alzheimer's disease using a brain image, and more particularly, to a device and a method of performing biological classification of Alzheimer's disease by analyzing an MRI image, an amyloid PET image, and a tau PET image related to the brain.
Alzheimer's disease which accounts for 50 to 60% of cases of dementia is the most widely known neurodegenerative disease. According to a recent report, approximately 50 million people suffer from dementia worldwide and are expected to increase to approximately 152 million by 2050.
Alzheimer's disease begins 20 years ago but changes in the brain may not be easily noticed until symptoms appear. The noticeable symptoms such as memory loss or speech impairment appear on the outside only after some changes in the brain have occurred. These symptoms are caused by the damage or destruction of nerve cells in the brain which involve in thinking, learning, and memory (cognitive function). As the disease progresses, other neurons in the brain are also damaged and destroyed, which eventually affects basic physical activities such as walking and swallowing.
Accordingly, it is very important to accurately diagnose Alzheimer's disease.
As a typical biological change of Alzheimer's disease, beta-amyloid (Aβ) which is a protein fragment outside the neurons and tau proteins which are abnormal proteins in the neurons are accumulated. This change disrupts the communication between neurons in the synapse to affect the damage and the death of the neurons.
In the past, Alzheimer's disease was diagnosed by performing various tests such as medical examination, neuropsychological test, and blood test and then collecting the test results and was confirmed only by post-mortem autopsy. However, in many cases, Alzheimer's disease may not be accurately diagnosed with only these tests.
Recently, in accordance with the development of technologies, PET which may test amyloid and hyperphosphorylated tau in the brain has been developed and deposition of beta-amyloid and abnormally phosphorylated tau neurofibrillary tangles which are typical features of Alzheimer's disease may be observed from living people.
Further, brain atrophy may be observed through MRI.
Currently, Alzheimer's disease is diagnosed when Alzheimer's disease related biomarkers which have been mentioned above are positive, regardless of a decline in the cognitive function.
That is, the certainty of unbiased diagnosis of Alzheimer's disease may be increased using amyloid PET, tau PET, and MRI.
An object of the present disclosure is to provide a device and a method for performing biological classification related to Alzheimer's disease by determining and combining whether it is normal or abnormal with respect to a predetermined biomarker after acquiring a neurodegeneration feature related to the brain of a patient from an MRI image and acquiring standardized uptake value ratio (SUVR) images from an amyloid PET image and a tau PET image.
Specifically, an object of the present disclosure is to provide a device and a method for performing first determination of whether it is normal or abnormal with respect to a neurodegeneration feature biomarker, second determination of whether it is normal or abnormal with respect to an amyloid PET image biomarker, and determination of whether it is normal or abnormal with respect to a tau PET image biomarker, and performing biological classification related to Alzheimer's disease based on a combination of three determination results.
An object of the present disclosure is to provide a biological classification device and method including first classification indicating that a patient is a normal stage, second classification indicating that the patient corresponds to an early stage of Alzheimer's disease, third classification indicating that the patient corresponds to Alzheimer's disease, fourth classification indicating that the patient has another pathology as well as Alzheimer's disease, and fifth classification indicating that the patient has a pathology other than Alzheimer's disease to a user.
Further, an object of the present disclosure is to provide a device and a method for classifying an entire region of the brain into a plurality of regions based on an MRI image, acquiring a neurodegeneration feature from the plurality of classified brain regions and acquiring an SUVR image from an amyloid PET image and an SUVR image from a tau PET image based on the plurality of classified brain regions to a user.
Further, an object of the present disclosure is to provide a device and a method for classifying and analyzing a brain of a patient into a plurality of regions based on an MRI image by applying a deep neural network module trained using at least one of a first model trained with a brain image in an axial direction and labelling data, a second model trained with a brain image in a coronal direction and the labelling data, and a third model trained with a brain image in a sagittal direction and the labelling data to a user.
Further, an object of the present disclosure is to provide a device and a method for acquiring an SUVR image from an amyloid PET image and a tau PET image based on region of interest (ROI) information acquired from an operation of a device of processing an MRI image in a plurality of classified brain regions to a user.
Further, an object of the present disclosure is to provide a device and a method for performing pre-processing such as partial volume correction (PVC) processing and co-registration processing, with regard to an amyloid PET image and a tau PET image to a user.
Further, an object of the present disclosure is to provide a device, a system, and a method which increase a probability of successful clinical trials by utilizing biological classification of Alzheimer's disease using a brain image to screen a patient group and a normal group.
In the meantime, technical objects to be achieved in the present invention are not limited to the aforementioned technical objects, and another not-mentioned technical object will be obviously understood by those skilled in the art from the description below.
In order to achieve the above-described technical objects, according to an aspect of the present disclosure, a biological classification device for Alzheimer's disease using a brain image may include an image receiving unit which receives a plurality of images obtained by capturing a brain of a subject; an image processing unit which acquires neurodegeneration feature related to the brain of the subject and SUVR information from the plurality of images; an image analysis unit which performs first determination of whether it is normal or abnormal with respect to cranial nerves based on the neurodegeneration feature and second determination and third determination of whether it is normal or abnormal with respect to beta amyloid protein and tau protein based on the SUVR information; and a classifying unit which performs biological classification of the subject related to the Alzheimer's disease using the first determination, the second determination, and the third determination together.
Further, the plurality of images may include an MRI image related to a brain of the subject and an amyloid PET image and a tau PET image related to the brain of the subject.
Further, the SUVR information includes a first SUVR image related to the amyloid PET image and a second SUVR image related to the tau PET image, and the image processing unit may classify the entire region of the brain of the subject into a plurality of regions and acquire the neurodegeneration feature, the first SUVR image, and the second SUVR image from the plurality of classified brain regions.
Further, the image analysis unit may include a first image analysis unit which performs first determination of whether it is normal or abnormal with regard to the cranial nerves based on the acquired neurodegeneration feature; a second image analysis unit which performs second determination of whether it is normal or abnormal with regard to beta amyloid protein based on the first SUVR image; and a third image analysis unit which performs third determination of whether it is normal or abnormal with regard to tau protein based on the second SUVR image.
Further, the biological classification performed by the classifying unit may include first classification indicating that a subject is a normal stage, second classification indicating that the subject is in an early stage of Alzheimer's disease, third classification indicating that the subject corresponds to Alzheimer's disease, fourth classification indicating that the subject has another pathology as well as Alzheimer's disease, and fifth classification indicating that the subject has a pathology other than Alzheimer's disease.
Further, the classifying unit may perform first classification of the subject when the first determination is normal, the second determination is normal, and the third determination is normal, second classification of the subject when the first determination is normal, the second determination is abnormal, and the third determination is normal, third classification of the subject when the first determination is normal, the second determination is abnormal, and the third determination is abnormal and when the first determination is abnormal, the second determination is abnormal, and the third determination is abnormal, fourth classification of the subject when the first determination is abnormal, the second determination is abnormal, and the third determination is normal, and fifth classification of the subject when the first determination is normal, the second determination is normal, and the third determination is abnormal, when the first determination is abnormal, the second determination is normal, and the third determination is normal, and when the first determination is abnormal, the second determination is normal, and the third determination is abnormal.
Further, the image processing unit may include: a first image processing unit which classifies the entire region of the brain of the subject into a plurality of regions based on the MRI image related to the brain of the subject and acquires the neurodegeneration feature from the plurality of classified brain regions; a second image processing unit which acquires the first SUVR image from the amyloid PET image related to the brain of the subject, based on the plurality of classified brain regions; and a third image processing unit which acquires the second SUVR image from the tau PET image related to the brain of the subject, based on the plurality of classified brain regions.
Further, the first image processing unit may include: a deep neural network module which is trained using at least one of a first model trained with a brain image in an axial direction and labelling data, a second model trained with a brain image in a coronal direction and the labelling data, and a third model trained with a brain image in a sagittal direction and the labelling data; a classification module which classifies the entire region of the brain of the subject into a plurality of regions based on the MRI image; and an analysis module which acquires neurodegeneration feature related to the brain of the subject based on the plurality of classified brain region.
Further, the analysis module may generate a neurodegeneration feature map based on the classified brain regions and acquires the neurodegeneration information from the neurodegeneration feature map and the neurodegeneration feature may include a cortical thickness, a volume, a surface area, and a gyrification index.
Further, the classification module may classify the entire region of the brain of the subject into a plurality of regions using any one of the first MRI image classified with respect to the axial direction by the first model, the second MRI image classified with respect to the coronal direction by the second model, and the third MRI image classified with respect to the sagittal direction by the third model.
Further, the deep neural network module may three-dimensionally reconstruct the MRI image using all the first MRI image classified with respect to the axial direction by the first model, the second MRI image classified with respect to the coronal direction by the second model, and the third MRI image classified with respect to the sagittal direction by the third model.
Further, the classification module may classify the entire region of the brain of the subject into 95 classes based on the MRI image which is three-dimensionally reconstructed and classify a hippocampus region among the 95 classes into 13 sub regions again.
Further, the classification module may reclassify the region which is classified into 95 classes and the hippocampus region which is classified into 13 sub regions again into a composite region by at least one of a task of additionally classifying into two or more regions and a task of composing two or more of the classified regions.
Further, the classification module may select and reclassify only regions related to a predetermined brain disease excluding regions which are not related to the predetermined brain disease from the region which is classified into 95 classes and the hippocampus region which is classified into 13 sub regions again.
Further, the analysis module may calculate a regional volume from the region, which is classified into 95 classes, a subfield volume from the hippocampus region which is classified into 13 sub regions again, and a composite region volume from the composite region.
Further, the second image processing unit and the third image processing unit may additionally apply region of interest (ROI) information used for the region classifying operation and the neurodegeneration feature operation of the first image processing unit to acquire the first SUVR image and the second SUVR image.
Further, the ROI may include a cerebellum grey matter region, a cerebellum white matter region, a whole cerebellum region, a pons region, and a brainstem region and the ROI used in the second image processing unit and the third image processing unit may vary depending on a tracer of the amyloid PET image and the tau PET image.
Further, the second image processing unit and the third image processing unit may perform a predetermined pre-processing process, and the pre-processing process may include partial volume correction (PVC) processing and co-registration processing.
The partial volume correction (PVC) processing is performed to correct a spill-out phenomenon that an image is blurred due to a resolution lower than a predetermined reference by the influence of a partial volume effect so that a concentration is measured to be low and a spill-in phenomenon that when the concentration around the region of interest is high, the concentration is measured to be higher than an actual concentration in the region of interest and the partial volume correction (PVC) processing method may include a geometric transfer matrix method and a Muller-Gartner method.
In order to achieve the above-described technical objects, according to another aspect of the present disclosure, a biological classification method for Alzheimer's disease using a brain image may include a first step of receiving a plurality of images obtained by capturing a brain of a subject, by an image receiving unit; a second step of acquiring a neurodegeneration feature related to the brain of the subject and SUVR information from the plurality of images, by an image processing unit; a third step of performing first determination of whether it is normal or abnormal with respect to cranial nerves based on the neurodegeneration feature and second determination and third determination of whether it is normal or abnormal with respect to beta amyloid protein and tau protein based on the SUVR information, by an image analysis unit; and a fourth step of performing biological classification of the subject related to the Alzheimer's disease using the first determination, the second determination, and the third determination together, by the classifying unit.
Further, the plurality of images may include an MRI image related to a brain of the subject and an amyloid PET image and a tau PET image related to the brain of the subject.
Further, the SUVR information includes a first SUVR image related to the amyloid PET image and a second SUVR image related to the tau PET image, and the image processing unit may classify the entire region of the brain of the subject into a plurality of regions and acquire the neurodegeneration feature, the first SUVR image, and the second SUVR image from the plurality of classified brain regions.
Further, the third step may include: a 3-1 step of performing first determination of whether it is normal or abnormal with regard to the cranial nerves based on the acquired neurodegeneration feature, by a first image analysis unit of the image analysis unit; a 3-2 step of performing second determination of whether it is normal or abnormal with regard to the beta amyloid protein based on the first SUVR image, by a second image analysis unit of the image analysis unit; and a 3-3 step of performing third determination of whether it is normal or abnormal with regard to the tau protein based on the second SUVR image, by a third image analysis unit of the image analysis unit.
In the fourth step, the biological classification performed by the classifying unit may include first classification indicating that a subject is a normal stage, second classification indicating that the subject is in an early stage of Alzheimer's disease, third classification indicating that the subject corresponds to Alzheimer's disease, fourth classification indicating that the subject has another pathology as well as Alzheimer's disease, and fifth classification indicating that the subject has a pathology other than Alzheimer's disease.
Further, in the fourth step, the classifying unit may perform first classification of the subject when the first determination is normal, the second determination is normal, and the third determination is normal, second classification of the subject when the first determination is normal, the second determination is abnormal, and the third determination is normal, third classification of the subject when the first determination is normal, the second determination is abnormal, and the third determination is abnormal and when the first determination is abnormal, the second determination is abnormal, and the third determination is abnormal, fourth classification of the subject when the first determination is abnormal, the second determination is abnormal, and the third determination is normal, and fifth classification of the subject when the first determination is normal, the second determination is normal, and the third determination is abnormal, when the first determination is abnormal, the second determination is normal, and the third determination is normal, and when the first determination is abnormal, the second determination is normal, and the third determination is abnormal.
Further, the second step may include a 2-1 step of classifying the entire region of the brain of the subject into a plurality of regions based on the MRI image related to the brain of the subject and acquiring the neurodegeneration feature from the plurality of classified brain regions, by a first image processing unit of the image processing unit; a 2-2 step of acquiring the first SUVR image from the amyloid PET image related to the brain of the subject, based on the plurality of classified brain regions, by a second image processing unit of the image processing unit; and a 2-3 step of acquiring the second SUVR image from the tau PET image related to the brain of the subject, based on the plurality of classified brain regions, by a third image processing unit of the image processing unit.
Further, the 2-1 step may include training a deep neural network module of the first image processing unit using at least one of a first model trained with a brain image in an axial direction and labelling data, a second model trained with a brain image in a coronal direction and the labelling data, and a third model trained with a brain image in a sagittal direction and the labelling data; classifying an entire region of a brain of the subject based on the MRI image into a plurality of regions, by a classification module of the first image processing unit; and acquiring a neurodegeneration feature related to the brain of the subject, based on the plurality of classified brain regions, by an analysis module of the first image processing unit.
Further, the analysis module may generate a neurodegeneration feature map based on the classified brain regions and acquires the neurodegeneration information from the neurodegeneration feature map and the neurodegeneration feature may include a cortical thickness, a volume, a surface area, and a gyrification index.
Further, the classification module may classify the entire region of the brain of the subject into a plurality of regions using any one of the first MRI image classified with respect to the axial direction by the first model, the second MRI image classified with respect to the coronal direction by the second model, and the third MRI image classified with respect to the sagittal direction by the third model.
Further, the deep neural network module may three-dimensionally reconstruct the MRI image using all the first MRI image classified with respect to the axial direction by the first model, the second MRI image classified with respect to the coronal direction by the second model, and the third MRI image classified with respect to the sagittal direction by the third model.
Further, the second image processing unit and the third image processing unit may additionally apply region of interest (ROI) information used for the region classifying operation and the neurodegeneration feature operation of the first image processing unit to acquire the first SUVR image and the second SUVR image.
Further, the ROI includes a cerebellum grey matter region, a cerebellum white matter region, a whole cerebellum region, a pons region, and a brainstem region, and the ROI used in the second image processing unit and the third image processing unit may vary depending on a tracer of the amyloid PET image and the tau PET image.
Further, the second image processing unit and the third image processing unit may perform a predetermined pre-processing process, and the pre-processing process may include partial volume correction (PVC) processing and co-registration processing.
In the meantime, according to still another aspect of the present disclosure, a method of increasing a probability of successful clinical trials by screening a subject group using a biological classification device for Alzheimer's disease using a brain image which includes an image receiving unit, an image processing unit, an image analysis unit, a classifying unit, and a central control unit may include a first step of receiving a plurality of images obtained by capturing brains of a plurality of subjects which is a candidate group of a clinical trial for proving a drug efficacy, by the image receiving unit; a second step of acquiring a neurodegeneration feature related to the brain of the plurality of subjects and SUVR information from the plurality of images, by the image processing unit; a third step of performing first determination of whether it is normal or abnormal with respect to cranial nerves based on the neurodegeneration feature and second determination and third determination of whether it is normal or abnormal with respect to beta amyloid protein and tau protein based on the SUVR information, by the image analysis unit; a fourth step of performing biological classification of the plurality of subjects related to the Alzheimer's disease using the first determination, the second determination, and the third determination together, by the classifying unit; a fifth step of providing the biological classification information of the plurality of subjects from the classifying unit to the central control unit; and a sixth step of screening a first subject for the clinical trial based on the biological classification information of the plurality of subjects, by the central control unit.
In the meantime, according to still another aspect of the present disclosure, a method of increasing a probability of successful clinical trials by screening a subject group using a biological classification device for Alzheimer's disease using a brain image which includes an image receiving unit, an image processing unit, an image analysis unit, a classifying unit, and a server which communicates with the biological classification device for Alzheimer's disease may include a first step of receiving a plurality of images obtained by capturing brains of a plurality of subjects which is a candidate group of a clinical trial for proving a drug efficacy, by the image receiving unit; a second step of acquiring a neurodegeneration feature related to the brain of the plurality of subjects and SUVR information from the plurality of images, by the image processing unit; a third step of performing first determination of whether it is normal or abnormal with respect to cranial nerves based on the neurodegeneration feature and second determination and third determination of whether it is normal or abnormal with respect to beta amyloid protein and tau protein based on the SUVR information, by the image analysis unit; a fourth step of performing biological classification of the plurality of subjects related to the Alzheimer's disease using the first determination, the second determination, and the third determination together, by the classifying unit; a fifth step of providing the biological classification information of the plurality of subjects from the classifying unit to the server; and a sixth step of screening a first subject for the clinical trial based on the biological classification information of the plurality of subjects, by the server.
As described above, according to the present disclosure, it is possible to provide a device and a method of performing biological classification related to Alzheimer's disease by determining and combining whether it is normal or abnormal with respect to a predetermined biomarker after acquiring a neurodegeneration feature related to the brain of a patient from an MRI image and acquiring a standardized uptake value ratio (SUVR) image from an amyloid PET image and a tau PET image.
Specifically, the present disclosure may provide a device and a method for performing first determination of whether it is normal or abnormal with respect to a neurodegeneration feature biomarker, second determination of whether it is normal or abnormal with respect to an amyloid PET image biomarker, and determination of whether it is normal or abnormal with respect to a tau PET image biomarker, and performing biological classification regard to Alzheimer's disease based on a combination of three determination results.
Further, the present disclosure may provide a biological classification device and method including first classification indicating that a patient is a normal stage, second classification indicating that the patient is in an early stage of Alzheimer's disease, third classification indicating that the patient corresponds to Alzheimer's disease, fourth classification indicating that the patient has another pathology as well as Alzheimer's disease, and fifth classification indicating that the patient has a pathology other than Alzheimer's disease to a user.
Further, the present disclosure may provide a device and a method for classifying an entire region of the brain into a plurality of regions based on an MRI image, acquiring a neurodegeneration feature from the plurality of classified brain regions and, at the same time, acquiring an SUVR image from an amyloid PET image and an SUVR image from a tau PET image based on the plurality of classified brain regions to a user.
Further, the present disclosure may provide a device and a method for classifying and analyzing a brain of a patient into a plurality of regions based on an MRI image by applying a deep neural network module trained using at least one of a first model trained with a brain image in an axial direction and labelling data, a second model trained with a brain image in a coronal direction and the labelling data, and a third model trained with a brain image in a sagittal direction and the labelling data to a user.
Further, the present disclosure may provide a device and a method for acquiring an SUVR image from an amyloid PET image and a tau PET image based on region of interest (ROI) information acquired from an operation of a device of processing an MRI image in a plurality of classified brain regions to a user.
Further, the present disclosure may provide a device and a method for performing pre-processing such as partial volume correction (PVC) processing and co-registration processing, with regard to an amyloid PET image and a tau PET image to a user.
Further, the present disclosure may provide a device, a system, and a method which increase a probability of successful clinical trials by utilizing biological classification of Alzheimer's disease using a brain image to screen a patient group and a normal group.
In the meantime, a technical object to be achieved in the present disclosure is not limited to the aforementioned effects, and other not-mentioned effects will be obviously understood by those skilled in the art from the description below.
The above and other aspects, features and other advantages of the present disclosure will be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings, in which:
Hereinafter, an exemplary embodiment of the present disclosure will be described with reference to the accompanying drawings. The exemplary embodiments which will be described below do not unduly limit the contents of the present disclosure as set forth in the claims and the entire configuration described in the present embodiment may not be said to be essential as a solution for the present disclosure.
Hereinafter, a device and a method of performing biological classification of Alzheimer's disease using a brain image according to an exemplary embodiment of the present invention will be described in detail with reference to the accompanying drawings.
Further,
Referring to
Here, the image receiving unit 10 receives a plurality of images obtained by capturing a brain of a patient.
Referring to
Returning to
The image processing unit 20 according to the present disclosure may include an MRI image processing unit 21 which acquires neurodegeneration feature related to a brain of a patient from a plurality of images and A and T PET image processing units 22 and 23 which acquire standardized uptake value ratio (SUVR) images from an amyloid PET image and a tau PET image related to the brain of the patient.
Further, the image analysis unit 30 may determine whether it is normal or abnormal with respect to a plurality of predetermined biomarkers.
Specifically, the image analysis unit 30 according to the present disclosure may include an MRI image analysis unit 31, an amyloid PET image analysis unit 32, and a tau PET image analysis unit 33.
The MRI image analysis unit 31 makes first determination of whether it is normal or abnormal with respect to a predetermined neurodegeneration feature biomarker.
Further, the amyloid PET image analysis unit 32 makes second determination of whether it is normal or abnormal with respect to a predetermined amyloid PET image biomarker.
Further, the tau PET image analysis unit 33 makes third determination of whether it is normal or abnormal with respect to a predetermined tau PET image biomarker.
Next, the classifying unit 40 performs biological classification of a patient related to Alzheimer's disease using the first determination, the second determination, and the third determination of the image analysis unit 30.
Referring to
A biological classification method for Alzheimer's disease proposed by the present disclosure will be described based on a configuration of the biological classification device for Alzheimer's disease 1 using a brain image which has been described with respect to
Referring to
In the step S1, the plurality of images 11 received by the image receiving unit 10 may include an MRI image related to the brain of the patient, an amyloid PET image and a tau PET image related to the brain of the patient.
Next, a step S2 of acquiring a neurodegeneration feature with regard to the brain of the patient and the standardized uptake value ratio (SUVR) image from the plurality of images by the image processing unit 20 is performed.
In the step S2, the MRI image processing unit 21 acquires the neurodegeneration feature related to the brain of the patient from the plurality of images and the A and T PET image processing units 22 and 23 may acquire SUVR images from the amyloid PET image and the tau PET image related to the brain of the patient, respectively.
Next, a step S3 of performing first determination of whether it is normal or abnormal with respect to a predetermined neurodegeneration feature biomarker, second determination of whether it is normal or abnormal with respect to a predetermined amyloid PET image biomarker, and third determination of whether it is normal or abnormal with respect to a predetermined tau PET image biomarker by the image analysis unit 30 is performed.
In the step S3, the MRI image analysis unit 31 makes first determination of whether it is normal or abnormal with respect to a predetermined neurodegeneration feature biomarker, second determination of whether it is normal or abnormal with respect to a predetermined amyloid PET image biomarker, and third determination of whether it is normal or abnormal with respect to a predetermined tau PET image biomarker.
After the step S3, a step S4 of performing biological classification of the patient related to Alzheimer's disease using the first determination, the second determination, and the third determination by the classifying unit 40 is performed.
In the step S4, the biological classification determined by the classifying unit 40 includes first classification indicating that a patient is in a normal stage, second classification indicating that the patient corresponds to an early stage of Alzheimer's disease, third classification indicating that the patient corresponds to Alzheimer's disease, fourth classification indicating that the patient has another pathology as well as Alzheimer's disease, and fifth classification indicating that the patient has a pathology other than Alzheimer's disease.
Hereinafter, the image receiving unit 10, the image processing unit 20, the image analysis unit 30, and the classifying unit 40 which have been described based on
Referring to
Referring to
First, the MRI image processing unit 21 may acquire neurodegeneration feature related to the brain of the patient from a plurality of images.
Next, the amyloid PET image processing unit 22 may acquire a first SUVR image from the amyloid PET image related to the brain of the patient.
Further, the tau PET image processing unit 23 may acquire a second SUVR image from the tau PET image related to the brain of the patient.
In this case, the MRI image processing unit 21 classifies the entire region of the brain into a plurality of regions based on the MRI image related to the brain of the patient and the amyloid PET image processing unit 22 and the tau PET image processing unit 23 acquire the first SUVR image and the second SUVR image, as well as the neurodegeneration feature, from the plurality of classified brain regions.
With regard to
Referring to
Specifically,
Referring to
Further,
Referring to
Specifically, referring to
Further, an image which is three-dimensionally reconstructed may be used by using the results of all the first model, the second model, and the third model.
That is, according to the present disclosure, the deep neural network module 21a may three-dimensionally reconstruct the MRI image using all a first MRI image classified with respect to the axial direction by the first model, a second MRI image classified with respect to the coronal direction by the second model, and a third MRI image classified with respect to the sagittal direction by the third model.
Further, the classification of the entire region of the brain of the patient into a plurality of regions based on the MRI image transmitted from the deep neural network module 21a by the classification module 21 will be described in more detail.
The classification module 21b may classify the entire region of the brain of the patient into 95 classes based on the three-dimensionally reconstructed MRI image by means of the deep neural network module 21a.
Further, the classification module 21b may reclassify a hippocampus region among 95 classes into 13 sub regions again.
Moreover, the classification module 21b may reclassify the region which is classified into 95 classes and the hippocampus region which is classified into 13 sub regions again into a composite region by at least one of a task of additionally classifying the regions into two or more regions and a task of composing two or more of the classified regions.
As another way, the classification module 21b may select and reclassify only regions related to a predetermined brain disease excluding regions which are not related to the predetermined brain disease from the region which is classified into 95 classes and the hippocampus region which is classified into 13 sub regions again.
Finally, the analysis module 21c acquires the neurodegeneration feature related to the brain of the patient based on the plurality of classified brain regions.
Here, the analysis module 21c may generate a neurodegeneration feature map based on the classified brain region.
Further, referring to
In the meantime, the analysis module 21c may use at least one of the region, which is classified into 95 classes and the hippocampus region, which is classified into 13 sub regions again by the classification module 21b.
That is, the analysis module 21c may calculate a regional volume from the region, which is classified into 95 classes, a subfield volume from the hippocampus region, which is classified into 13 sub regions again, and a composite region volume from the composite region.
As described above, the plurality of input images 11 may include the MRI image 12 related to the brain of the patient, the amyloid PET image 13 and the tau PET image 14 related to the brain of the patient.
Further, the SUVR image may include a first SUVR image 68 acquired by the amyloid PET image processing unit 22 and a second SUVR image 69 acquired by the tau PET image processing unit 23.
As described above, the MRI image processing unit 21 classifies the entire region of the brain of the patient into a plurality of regions and the amyloid PET image processing unit 22 and the tau PET image processing unit 23 may acquire the first SUVR image 68 and the second SUVR image 69 from the plurality of classified brain regions.
Further,
Referring to
The ROI (Region of Interest) information 70 is utilized during a process of acquiring specific information 67 such as a cortical thickness, a volume, a surface area, a gyrification index, a volume for every region from the classified region, hippocampus subfield volume, or a composite region volume by the MRI image processing unit 21.
Typically, the ROI 70 applied in
Further, in
In the meantime, the amyloid PET image processing unit and the tau PET image processing unit 23 may perform a predetermined pre-processing process.
Here, the pre-processing process may include partial volume correction (PVC) processing and co-registration processing.
Here, the partial volume correction (PVC) processing is performed to correct a spill-out phenomenon that an image is blurred due to a resolution lower than a predetermined reference by the influence of a partial volume effect so that a concentration is measured to be low and a spill-in phenomenon that when the concentration around the region of interest is high, the concentration is measured to be higher than an actual concentration in the region of interest.
Further, the partial volume correction (PVC) processing method may include a geometric transfer matrix method and a Muller-Gartner method.
The image analysis unit 30 may determine whether it is normal or abnormal with respect to a plurality of predetermined biomarkers.
Specifically, the image analysis unit 30 according to the present disclosure may include an MRI image analysis unit 31, an amyloid PET image analysis unit 32, and a tau PET image analysis unit 33.
The MRI image analysis unit 31 makes first determination of whether it is normal or abnormal with respect to a predetermined neurodegeneration feature biomarker.
Further, the amyloid PET image analysis unit 32 makes second determination of whether it is normal or abnormal with respect to a predetermined amyloid PET image biomarker.
Further, the tau PET image analysis unit 33 makes third determination of whether it is normal or abnormal with respect to a predetermined tau PET image biomarker.
Next, the classifying unit 40 performs biological classification of the patient related to Alzheimer's disease using the first determination, the second determination, and the third determination.
The biological classification performed by the classifying unit includes first classification indicating that a patient is a normal stage, second classification indicating that the patient is in an early stage of Alzheimer's disease, third classification indicating that the patient corresponds to Alzheimer's disease, fourth classification indicating that the patient has another pathology as well as Alzheimer's disease, and fifth classification indicating that the patient has a pathology other than Alzheimer's disease.
Specifically,
Referring to
Further, the classifying unit 40 performs second classification indicating that the patient corresponds to an early stage of Alzheimer's disease when the first determination is normal with respect to the neurodegeneration feature biomarker, the second determination is abnormal with respect to the amyloid PET image biomarker, and the third determination is normal with respect to the tau PET image biomarker (82).
Further, the classifying unit 40 performs third classification indicating that the patient corresponds to Alzheimer's disease when the first determination is normal with respect to the neurodegeneration feature biomarker, the second determination is abnormal with respect to the amyloid PET image biomarker, and the third determination is abnormal with respect to the tau PET image biomarker (83).
Further, the classifying unit 40 performs third classification indicating that the patient corresponds to Alzheimer's disease when the first determination is abnormal with respect to the neurodegeneration feature biomarker, the second determination is abnormal with respect to the amyloid PET image biomarker, and the third determination is abnormal with respect to the tau PET image biomarker (84).
Further, the classifying unit 40 performs fourth classification indicating that the patient has another pathology as well as a pathology of Alzheimer's disease when the first determination is abnormal with respect to the neurodegeneration feature biomarker, the second determination is abnormal with respect to the amyloid PET image biomarker, and the third determination is normal with respect to the tau PET image biomarker (85).
Further, the classifying unit 40 performs fifth classification indicating that the patient has a pathology other than Alzheimer's disease when the first determination is normal with respect to the neurodegeneration feature biomarker, the second determination is normal with respect to the amyloid PET image biomarker, and the third determination is abnormal with respect to the tau PET image biomarker (86).
Further, the classifying unit 40 performs fifth classification indicating that the patient has a pathology other than Alzheimer's disease when the first determination is abnormal with respect to the neurodegeneration feature biomarker, the second determination is normal with respect to the amyloid PET image biomarker, and the third determination is normal with respect to the tau PET image biomarker (87).
Further, the classifying unit 40 performs fifth classification indicating that the patient has a pathology other than Alzheimer's disease when the first determination is abnormal with respect to the neurodegeneration feature biomarker, the second determination is normal with respect to the amyloid PET image biomarker, and the third determination is abnormal with respect to the tau PET image biomarker (88).
Referring to
Further, based on these results, the classifying unit performs biological classification including the first classification indicating that the patient is a normal stage, the second classification indicating that the patient corresponds to an early stage of Alzheimer's disease, the third classification indicating that the patient corresponds to Alzheimer's disease, the fourth classification indicating that the patient has another pathology as well as a pathology of Alzheimer's disease, and the fifth classification indicating that the patient is a pathology other than Alzheimer's disease, as illustrated in
Based on this, it is possible to identify an exact current state of the patient and provide a step in accordance with the current state and a management step in accordance with the possibility of Alzheimer's disease in the future to the patient.
The above-described biological classification device and method for Alzheimer's disease using a brain image according to the present disclosure are utilized to screen the patient group and the normal group to increase a probability of successful clinical trials.
That is, the present disclosure may provide a device, a system, and a method which increase a probability of successful clinical trials by utilizing the biological classification device and method for Alzheimer's disease using a brain image to screen a patient group and a normal group.
A result of clinical trials for demonstration of drug efficacy is determined by showing a statistical significance indicating whether to achieve a predicted expected effect for clinical trial participants. However, when the biological classification device and method for Alzheimer's disease using a brain image according to the present disclosure are applied, only Alzheimer's disease patients exactly targeted by new drugs are included as clinical trial subjects so that the probability of successful clinical trials may be increased as much as possible.
First, problems of existing new drug clinical trials will be described in advance.
A result of clinical trials for demonstration of drug efficacy is determined by showing a statistical significance indicating whether to achieve a predicted expected effect for clinical trial participants.
Therefore, in order to prove the statistical significance, a numerical value of an evaluation scale needs to be statistically significantly increased before and after medication or as compared to a placebo group. The higher the predicted increase value, the smaller the number of target subjects and the higher the probability of achieving statistical significance.
In this case, if the predicted increase value is small, the number of target subjects increases as well and the difficulty of statistical proof is increased.
As a result, it is very difficult to increase one step of evaluation scale of the Alzheimer's disease, so that there is a problem in that a possibility of passing the clinical trial is very low.
In the present disclosure, in order to solve the above-described problem, only Alzheimer's disease patients who are exactly targeted by the new drug are included as subjects of the clinical trials to increase a probability of successful clinical trials as much as possible.
One of important failure factors in a new drug development process for central nervous system drugs is the difficulty of screening the correct subjects and screening a drug response group.
Since a response rate to the placebo for the central nervous system drugs is particularly high, an important strategy of increasing the success rate is to reduce the heterogeneity of the subject group and setting a biomarker capable of predicting a drug reactivity.
Further, since it takes a long time to confirm the Alzheimer's disease, a screening test is difficult so that there is a problem in that it is very difficult to include only the Alzheimer's disease patients targeted by new drugs as subjects of clinical trials.
The biological classification device and method for Alzheimer's disease using a brain image proposed by the present disclosure are utilized to screen the patient group and the normal group to increase a probability of successful clinical trials.
In
Referring to
Next, a step S12 of acquiring a neurodegeneration feature with regard to the brains of the plurality of patients and the standardized uptake value ratio (SUVR) image from the plurality of images by the image processing unit 20 is performed.
After the step S12, a step S13 of performing first determination of whether it is normal or abnormal with respect to a predetermined neurodegeneration feature biomarker, second determination of whether it is normal or abnormal with respect to a predetermined amyloid PET image biomarker, and third determination of whether it is normal or abnormal with respect to a predetermined tau PET image biomarker is performed by the image analysis unit 30.
Further, the classifying unit 40 performs biological classification of the plurality of patient related to Alzheimer's disease using the first determination, the second determination, and the third determination (S14).
Next, a step S15 of providing the biological classification information of the plurality of patients from the classifying unit 40 to the central control unit (not illustrates) is performed.
In this case, the central control unit may screen a first patient for the clinical trial based on the biological classification information of the plurality of patients (S16).
After the step S16, the clinical trial is performed on the screened patient group to increase the probability of successful clinical trials (S17).
Accordingly, only the Alzheimer's disease patients who are exactly targeted by the new drugs are included as a clinical trial subject so that the probability of successful clinical trials may be increased as much as possible.
As a result, the biological classification device and method for Alzheimer's disease using a brain image according to the present disclosure are utilized to screen the patient group and the normal group to increase a probability of successful clinical trials.
The above-described steps S1 to S4 may be independently performed by the biological classification device for Alzheimer's disease 1 using a brain image or may be applied by providing a separate server (not illustrated) or a separate central control device (not illustrated) to perform the entire operations together with the biological classification device for Alzheimer's disease 1.
The second method explains a method of using a separate server (not illustrated).
Steps S21 to S24 of
After the step S24, a step S25 of providing biological classification information of the plurality of patients from the classifying unit 40 to a server (not illustrated) by wireless or wired communication is performed.
Thereafter, the server may screen a first patient for the clinical trial based on the biological classification information of the plurality of patients (S26).
After the step S26, the clinical trials are performed on the screened patient group to increase the probability of successful clinical trials (S27) and only the Alzheimer's disease patients who are exactly targeted by the new drugs are included as clinical trial subjects to increase the probability of successful clinical trials as much as possible.
As described above, according to the present disclosure, it is possible to provide a device and a method of performing biological classification related to Alzheimer's disease by determining and combining whether it is normal or abnormal with respect to a predetermined biomarker after acquiring a neurodegeneration feature related to the brain of a patient from an MRI image and acquiring a standardized uptake value ratio (SUVR) from an amyloid PET image and a tau PET image.
Specifically, the present disclosure provides a device and a method for performing determination of whether it is normal or abnormal with respect to a neurodegeneration feature biomarker, second determination of whether it is normal or abnormal with respect to an amyloid PET image biomarker, and determination of whether it is normal or abnormal with respect to a tau PET image biomarker, and performing biological classification with respect to Alzheimer's disease based on a combination of three determination results.
Further, the present disclosure provides a biological classification device and method including first classification indicating that a patient is a normal stage, second classification indicating that the patient corresponds to an early stage of Alzheimer's disease, third classification indicating that the patient corresponds to Alzheimer's disease, fourth classification indicating that the patient has another pathology as well as Alzheimer's disease, and fifth classification indicating that the patient has a pathology other than Alzheimer's disease to a user.
Further, the present disclosure provides a device and a method of classifying an entire region of the brain into a plurality of regions based on an MRI image, acquiring a neurodegeneration feature from the plurality of classified brain regions and, at the same time, acquiring an SUVR image from an amyloid PET image and an SUVR image from a tau PET image based on the plurality of classified brain regions to a user.
Further, the present disclosure provides a device and a method of classifying and analyzing a brain of a patient into a plurality of regions based on an MRI image by applying a deep neural network module trained using at least one of a first model trained with a brain image in an axial direction and labelling data, a second model trained with a brain image in a coronal direction and the labelling data, and a third model trained with a brain image in a sagittal direction and the labelling data to a user.
Further, the present disclosure provides a device and a method of acquiring an SUVR image from an amyloid PET image and a tau PET image based on region of interest (ROI) information acquired from an operation of a device of processing an MRI image in a plurality of classified brain regions to a user.
Further, the present disclosure provides a device and a method of performing pre-processing such as partial volume correction (PVC) processing and co-registration processing, with regard to an amyloid PET image and a tau PET image to a user.
Further, the present disclosure provides a device, a system, and a method which increase a probability of successful clinical trials by utilizing biological classification of Alzheimer's disease using a brain image to screen a patient group and a normal group.
A technical object to be achieved in the present disclosure is not limited to the aforementioned effects, and another not-mentioned effects will be obviously understood by those skilled in the art from the description below.
The above-described exemplary embodiments of the present invention may be implemented through various methods. For example, the exemplary embodiments of the present disclosure may be implemented by a hardware, a firm ware, a software, and a combination thereof.
When the exemplary embodiment is implemented by the hardware, the method according to the exemplary embodiment of the present disclosure may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), a processor, a controller, a microcontroller, or a microprocessor.
When the exemplary embodiment is implemented by the firmware or the software, the method according to the exemplary embodiment of the present disclosure may be implemented by a module, a procedure, or a function which performs a function or operations described above. The software code is stored in the memory unit to be driven by the processor. The memory unit is located inside or outside the processor and exchanges data with the processor, by various known units.
As described above, the detailed description of the exemplary embodiments of the disclosed present invention is provided such that those skilled in the art implement and carry out the present invention. While the invention has been described with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes and modifications of the present invention may be made without departing from the spirit and scope of the invention. For example, those skilled in the art may use configurations disclosed in the above-described exemplary embodiments by combining them with each other. Therefore, the present invention is not intended to be limited to the above-described exemplary embodiments but to assign the widest scope consistent with disclosed principles and novel features.
The present invention may be implemented in another specific form within the scope without departing from the spirit and essential feature of the present invention. Therefore, the detailed description should not restrictively be analyzed in all aspects and should be exemplarily considered. The scope of the present invention should be determined by rational interpretation of the appended claims and all changes are included in the scope of the present invention within the equivalent scope of the present invention. The present invention is not intended to be limited to the above-described exemplary embodiments but to assign the widest scope consistent with disclosed principles and novel features. Further, claims having no clear quoting relation in the claims are combined to configure the embodiment or may be included as new claims by correction after application.
| Number | Date | Country | Kind |
|---|---|---|---|
| 10-2020-0155063 | Nov 2020 | KR | national |
