Patent Application
20230104045
The present disclosure relates generally to use of an ultrasound apparatus, and more specifically, to exemplary embodiments of an exemplary system, method and computer-accessible medium for ultrasound analysis.
Echocardiogram, or ultrasound of the heart, is a common clinical tool for assessing a heart condition, and for identifying and diagnosing certain heart diseases. Currently, a significant amount of the analysis and processing of the input ultrasound movie clips is performed by a technician or a physician (“user”). Such manual analysis has several drawbacks, for example: (i) it increases the chance of error, (ii) it needs a skilled user, (iii) it limits the throughput by the analyzing speed and skill of the user and (iv) due to time complexity, only several frames from the clips are fully analyzed while information in other frames is left unused.
Cardiac ultrasound can be the preferred modality for the assessment of cardiac anatomy, function and structural anomalies. Currently, routine cardiac ultrasound examination lasts between about 30 and about 40 minutes, and can include: (i) acquisition of the data by ultrasound and Doppler procedures, (ii) analysis of ventricular function and multiple measurements of the different parts of the cardiac structure, and (iii) a report that can be incorporated directly in the electronic medical record.
Thus, it may be beneficial to provide an exemplary system, method and computer-accessible medium for ultrasound analysis, and which can overcome at least some of the deficiencies described herein above.
An exemplary system, method and computer-accessible medium for detecting an anomaly(ies) in an anatomical structure(s) of a patient(s) can be provided, which can include, for example, receiving imaging information related to the anatomical structure(s) of the patient(s), classifying a feature(s) of the anatomical structure(s) based on the imaging information using a neural network(s), and detecting the anomaly(ies) based on data generated using the classification procedure. The imaging information can include at least three images of the anatomical structure(s).
In some exemplary embodiments of the present disclosure, the imaging information can include ultrasound imaging information. The ultrasound imaging information can be generated using, e.g., an ultrasound arrangement. The anatomical structure(s) can be a heart. In certain exemplary embodiments of the present disclosure, the state(s) of the anatomical structure(s) can include (i) a systole state of a heart of the patient(s), (ii) a diastole state of the heart of the patient(s), (iii) an inflation state of the heart of the patient(s) or (iv) a deflation state of the heart of the patient(s).
In some exemplary embodiments of the present disclosure, the feature(s) can be classified using a view detection procedure, which can include detecting a view of a particular imaging frame in the imaging information. The anatomical structure(s) can be segmented, e.g., using a part segmentation procedure and a localization procedure before the detection of the anomaly(ies). The part segmentation procedure can be utilized to segment a left ventricle of the heart of the patient(s) from a background. The localization procedure can be a valve localization procedure, which can include marking, a single pixel per frame in the imaging information to place a Doppler measuring point(s).
In certain exemplary embodiments of the present disclosure, the imaging information can include a plurality of images, and neural network(s) can include a plurality of neural networks each one of which can be associated with one of the images. Each neural network can be used to classify the feature(s) in its associated one of the images. An output produced by each of the neural networks can be concatenated (e.g., in a depth). The imaging information can be upsampled.
These and other objects, features and advantages of the exemplary embodiments of the present disclosure will become apparent upon reading the following detailed description of the exemplary embodiments of the present disclosure, when taken in conjunction with the appended claims.
Further objects, features and advantages of the present disclosure will become apparent from the following detailed description taken in conjunction with the accompanying figures showing illustrative embodiments of the present disclosure, in which:
for part segmentation according to an exemplary embodiment of the present disclosure;
for valve localization according to an exemplary embodiment of the present disclosure;
Throughout the drawings, the same reference numerals and characters, unless otherwise stated, are used to denote like features, elements, components or portions of the illustrated embodiments. Moreover, while the present disclosure will now be described in detail with reference to the figures, it is done so in connection with the illustrative embodiments and is not limited by the particular embodiments illustrated in the figures and the appended claims.
The exemplary embodiments of the present disclosure may be further understood with reference to the following description and the related appended drawings. The exemplary embodiments are described with reference to cardiovascular imaging (e.g., using ultrasound). However, those having ordinary skill in the art will understand that the exemplary embodiments of the present disclosure may be implemented for imaging other tissues or organs (e.g., other than the heart) and can be used in other imaging modalities (e.g., other than ultrasound, including but not limited to MRI, CT, OCT, OFDR, etc.).
The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can include a neural network core that can aid a healthcare provider in diagnosing and making clinical decisions more accurately, be of better quality, and have increased safety. For example, the exemplary neural network core can receive images from multiple imaging modalities including ultrasound, magnetic resonance imaging, positron emission scanners, computer tomography and nuclear scanners. The exemplary neural network core can be used for the examination of multiple organs, and is not limited to a specific organ system. The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can be incorporated into, or connected to, online and/or offline medical diagnostic devices, thus facilitating the operator to become more accurate and efficient.
As shown in the diagram of
The first exemplary procedure (e.g., View detection 125) can be performed to detect the view of a given frame out of several potential views. The views currently handled can be ones used in a standard adult echocardiogram examination: (i) apical 2-chamber view, (ii) apical 3-chamber view, (iii) apical 4-chamber view, (iv) apical 5-chamber view, (v) parasternal long axis view and (vi) parasternal short axis view. The second exemplary procedure (e.g., Systole/diastole 130) can be performed to identify the systole/diastole in the cardiac cycle. For example, each frame can be labeled using one of the four temporal states of the left ventricle: (i) diastole, (ii) systole, (iii) inflating and (iv) deflating. The third exemplary procedure (e.g., Part segmentation 135) can be performed to segment regions in ultrasound images such as the four chambers of the heart, the heart valves and walls, and the pericardium. The fourth exemplary procedure (e.g., Valve localization 140) can be performed to identify the locations of valves for Doppler analysis of in/out flow through these valves. The fifth exemplary procedure (e.g., Anomaly detection 145) can be performed to detect and locate heart anomalies, such as pericardial effusion.
An exemplary observation can be that the Core NN that extracts high-level semantic features from a sequence of ultrasound images can be the same for all the procedures, and the exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can be trained by optimizing (e.g., simultaneously) all procedures and the Core NN. This exemplary approach can provide the following benefits. First, building an ultrasound feature system that can be trained and used for different complementary procedures can provide versatile features that can corroborate and improve individual procedure performance. The intuition can be similar to human learning; for example, to better detect the view of an ultrasound image, it can be beneficial to detect and segment the different visible parts of the heart, and vice-versa. Second, having a versatile feature system captured by the Core NN, an ultrasound analysis procedure can be added with rather low amounts of data. This can be because the main part of the exemplary system, the Core NN, can already be trained to produce generic features, and all that can be left to train can be the procedure specific part. This can facilitate adaptation of the exemplary system, method, and computer-accessible medium to new or different procedures with rather low computational complexity and time. Third, since it can usually be more difficult to achieve and/or produce data for the more difficult procedures, starting by training the system, method, and computer-accessible medium on the easier procedures provides a good starting point for more elaborate procedures.
The echocardiogram can often be used to extract high level quantitative information regarding the heart condition and function. An exemplary archetypical example is the Ejection Fraction (“EF”). The EF can be computed from various exemplary measurements including and combining view detection, systole/diastole detection, and part segmentation. The exemplary system, method and computer-accessible medium can utilize an automatic pipeline for determining the EF, including producing a 3D surface reconstruction of the LV, or other parts using the UT data (e.g., only the UT data).
The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can include the creation of data. An exemplary database consisting of 3000 ultrasound clips (e.g., approximately 40 frames each) was used. Each ultrasound frame Xi was an n×n gray-scale image. The database was divided into three disjoint sets: (i) training, (ii) validation and (iii) test data.
An exemplary input to the exemplary system can include sequences (e.g., triplets) of consecutive ultrasound frames (e.g., l=3). Additionally, Xi=(xi−d, xi, xi+d) (e.g., element 105 from
The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can support two types of procedures: (i) classification and (ii) segmentation. For each exemplary procedure, a ground-truth label yi was generated for a subset of the ultrasound frames xi in the exemplary database; yi can either be a single label (e.g., for classification procedures) or can consist of a label for each pixel in xi (e.g., for segmentation procedures). Thus, either yi∈={
1,
2, . . .
k} can be a single label, or yj∈
n×n can be a per-pixel label (e.g., corresponding to each pixel of xj).
The first two exemplary procedures can be classification procedures. For the first exemplary procedure of View detection, yi∈ where
={SA, LA, 5C, 4C, 3C, 2C} can correspond to Short-Axis, Long-Axis, 5 Chamber, 4 Chamber, 3 Chamber, and 2 Chamber views. For the procedure of Diastole/systole detection,
={DI, SY, IN, DE}, can correspond to Dlastole, SYstole, INflating, and DEflating of the left ventricle. Since DI and SY can be instantaneous configurations, only two labels
={IN, DE} were utilized, and these labels were assigned to all frames between peaks (e.g., strictly between diastole and systole and systole and diastole).
The next three exemplary procedures can be segmentation procedures. The exemplary part segmentation procedure implemented can use labels yi∈n×n where
={LV, BA},LV can stand for Left-Ventricle and BA for Background. Given an input sequence of frames Xi the exemplary goal can be to decide, for each pixel in the middle frame xi, if it can be part of the left-ventricle or not. Manually labeling each pixel in xi, can be tedious and impractical. Therefore, an exemplary software tool that can facilitate the generation of labels yi for a collection of frames xi as produced can be used. An exemplary screenshot from a labeling session is shown in the image in
i∈
. For example, as shown in the image in
The exemplary Valve Localization procedure can utilize marking a single pixel per frame to place the Doppler measuring point. Three valves were implemented; ={MI, TR, AO, BA}, Mitral (“MI”), Tricuspidal (“TR”), Aortic (“AO”) and background (“BA”). To generate data, an exemplary software tool, similar to the one used for part segmentation, was produced, which can facilitate the user to select a pixel in each image to indicate the location of the relevant valve. (See, e.g.,
In the exemplary anomaly detection, pericardial effusion and detect fluid accumulation in the pericardial cavity can be taken care of. In this exemplary segmentation procedure, the labels can discriminate pericardial fluid and background, ={PE, BA}. Similar software to the previous two segmentation procedures was built where the user can annotate the fluid areas.
The exemplary procedures can be divided into two groups: (i) classification procedures, and (ii) segmentation procedures. The exemplary classification procedure can request to assign a label zi∈ from a set of possible labels
={
1, . . . ,
k} for an input sequence Xi. The exemplary classification procedure can include View detection and Systole/diastole detection. The exemplary segmentation procedure can ask for a given input sequence Xi to generate a label per-pixel for the middle frame xi, that can be zi∈
n×n, where xi can be n×n image, and as before
={
1, . . . ,
k}. The exemplary segmentation procedure can include part segmentation, valve localization and anomaly detection. An anomaly detection can also have instantiation as a classification procedure. Each procedure can have its own network with suitable architecture based on its type (e.g., classification of segmentation).
The two groups of the exemplary procedures can use the same concatenated features produced by the exemplary Core NN. The exemplary classification procedures can use an exemplary classification framework (e.g., element 420 shown in k (e.g.,
can be the number of classes in
) from which a prediction zi can be made.
During the exemplary segmentation (see, e.g., element 425 shown in
The exemplary segmentation of a particular part/section of the heart, for example, the left-ventricle, can include a multi-scale task. For example, a rough estimate of the part location in the image can be provided, and the exemplary prediction can be gradually refined, or modified, based on local features of the image. The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can include two exemplary architectures for segmenting anatomical parts from medical images. (See e.g., diagrams shown in
For example, the diagram shown in
The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can also consider the addition of a total variation regularizer directly to the network loss function in order to encourage segmentation with a shorter boundary curve. The total variation energy can be defined directly on the output of the segmentation network as λΣp∥[∇F(Xi)]p∥2, where F(X) can be the difference of the network response for a certain label (e.g., LV) and the background label BA when applied to the input sequence Xi, the sum can be over all pixels p, and λ can be the amount of regularization.
The exemplary Core NN 415 illustrated in
The exemplary system and method, according to an exemplary embodiment of the present disclosure, can be utilized to improve the segmentation and can involve L1 cost functions and Generative Adversarial Networks. For example, L1 regularization for segmentation can be used as a loss function, and can provide a more accurate boundary detection than standard crossentropy loss. Alternatively or in addition, an exemplary loss function can be trained using, for example, GANs. As an example, given some segmentation network, a discriminator network can be trained to distinguish real segmentations and segmentations created by the segmentation network. This discriminator, in combination with some other loss or on its own to further train the segmentation network, can be used. The input to the discriminator network can include (Xi, Zi) for real examples, and (Xi, f (Xi)) , where f (Xi) can be the output of the segmentation network.
For training the exemplary system, an interleaving approach can be employed. For example, the first exemplary procedure (e.g., view detection) can be used, which can train its NN (see, e.g., element 420 shown in
The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, was evaluated on test data that has not been used or validated in the training phase; the test data consisted of 322 clips (e.g., 10000 frames) for classification procedures, 42 clips (e.g., 530 frames) for part segmentation procedures and 108 clips (e.g., 2250 frames) for the Valve localization procedures.
Exemplary View detection
The view of a given frame xi can be used for the exemplary classification. For example,
The exemplary diastole/systole detection procedure can utilize identifying the cardiac cycle stage of a given input frame xi; where the labels can be ={DI, SY, IN, DE}. The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can generate probabilities, for example, only with respect to the non-instantaneous states, IN, DE.
The exemplary part segmentation procedure can utilize labeling each pixel in an input image xi according to the parts labels. For example, only left ventricle (“LV”) segmentation can be implemented.
To produce a baseline for the results, the exemplary user was asked to repeat the LV annotation of the test set after waiting a period of several weeks, and to measure these new segmentations versus the original GT segmentations.
This exemplary procedure can utilize placing a point at a certain location for flow calculation during Doppler analysis.
The exemplary Core NN was tested to determine how it can adapt to new procedures after different levels of training and using a “warm start” initialization.
The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can be used as an add-on to any clinical commercial imaging device, (e.g., ultrasound). The exemplary system, method and computer-accessible medium can be used for both the Echocardiography (e.g., Ultrasound of the Heart) in the Cardiology Department and the Emergency Department (“ED”).
Multiple technicians were observed while performing a routine and complete Echocardiographic examination. The total duration of the examination and the distribution of time for each of the three tasks were recorded. (See, e.g., diagram of
The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can be used to accurately identify the left ventricle (e.g., out of the 4 chambers), and automatically apply and provide a complete cardiac function analysis that can be incorporated directly into the final study report. Currently, the technician has to identify two or three points, or trace the left ventricle in different views (e.g., out of the six views acquired), and then activate the calculation packages available on an exemplary machine. The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can automatically detect the different viewing windows and segments, and can identify the various parts of the heart, such as the left ventricle.
The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can use segmentation, peak systolic and/or peak diastolic frames, which can now be determined automatically. In the past, this has been performed manually by the technician by carefully scanning frame by frame, and identifying the peak systolic and peak diastolic frame. The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can utilize a neural core to determine these two events during the cardiac cycles, and can then perform an assessment of the left ventricular function. Thus, the manual labor can be eliminated completely, and all other measurements, including dimension of the left ventricle in systole and diastole, Right ventricular assessment, LA size, measurement of the aortic valve annulus, the aortic sinuses, the ascending aorta, the pulmonary valve, the mitral valve annulus and the tricuspid valve annulus, can be automatically measured.
An important part of the cardiac examination by a skilled echocardiographer can be to perform a thorough Doppler examination. In the past, the technician identified the ideal location and angle in which the sample volume of the Doppler can be located to receive the best signal-to-noise ratio. This can also be a manually laborious task that requires expertise in order to assess all cardiac valves. Such task is time consuming. The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can be used to identify the exact area where Doppler samples can be located. After the appropriate image can be achieved, a Doppler sample location can be identified, and the exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can automatically activate the Doppler modality, thus achieving appropriate Doppler tracing across the cardiac valves. When Doppler tracings is achieved and displayed, calculation packages can be applied, and the mitral and tricuspid valve inflow, as well as the aorta and pulmonary outflow tract Doppler tracing, can be calculated automatically.
About 10%-15% of the total duration of the examination can be dedicated to incorporate all measurements in the final report before it can be sent to the specialist, for example, the cardiologist who can finalize the report and send it to emergency medical records. This can be a significantly manual process that can be avoided by automatically measuring, using the exemplary system, method and computer-accessible medium, all the different variables needed to assess cardiac function as well as valve abnormalities. (See e.g., diagram shown in
It is estimated that the exemplary system, method and computer-accessible medium, can save up to 40% of the time as compared to a manual exam (e.g., the type of examinations currently being performed). Additionally, the exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can improve efficiency as well as quality as compared to currently-performed manual exams.
The exemplary system, method, and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can, after the appropriate image is acquired by the technician/physician/nurses, automatically display cardiac function as normal, mild, moderate or severe left ventricular dysfunction. If needed, the exact number of Left Ventricular Ejection Fraction can also be displayed.
Pericardial effusion represents one of the most dangerous cardiac abnormalities that can lead to death if not diagnosed in a timely fashion. The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can be used to automatically detect the existence of pericardial effusion by ultrasound examination. For example, the exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can (e.g., detect immediately) any pericardial effusion, and alert the operator by the existence or nonexistence of pericardial effusion. If needed, the severity of the accumulated pericardial effusion can be displayed.
Cardiac segmental abnormalities can be used as a screening tool for the diagnosis of ischemia of the cardiac muscle. This can be a very subjective task, and can be operator-dependent even in the hands of an expert cardiologist. The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can automatically notify the user that there may be segmental abnormalities, for example, Hypokynesis, diskynesia or paradoxical motion of any part of the left ventricular wall and septum. Currently, the heart is divided into 17 segments and the exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can detect very subtle wall motion abnormalities across all segments.
The exemplary system, method and computer-accessible medium can use the exemplary neural core to identify and separate the left and the right ventricles. The exemplary neural core can assess the relative and absolute areas and volumes of these ventricles, and can quickly calculate the ratio between them (e.g., in a fraction of a second). This can be beneficial in order to raise the suspicion level of a critical condition called a pulmonary embolism. In this condition there can be a major strain on the right ventricle, and as a result, the ventricle tends to enlarge, and the ratio between right ventricle and left ventricular area can be dramatically altered. This can be used as an exemplary screening tool to notify the clinician that there can be a possible pulmonary embolism based on the RV-to-LV ratio. (See e.g., diagram shown in
For example, as illustrated in an exemplary diagram of
The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can be used to compute or otherwise determine the EF from the raw UT input. The user can be provided with, for example, the indication of one of two possibilities: (i) a drive and (ii) a halt. In drive (see, e.g.,
The exemplary determination of the EF can include the following exemplary procedures:
The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can utilize the above exemplary procedures to produce a 3D reconstruction of the entire cardiac cycle. (See, e.g.,
To validate the automatic EF procedure described above, a test of 114 anonymous cases was performed, and compared to 4 expert cardiologists and 2 two expert technicians to assess/compute the EF. The ground truth for each case was defined as the median of these assessments. The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, was compared to the experts in terms of a mean deviation to ground truth and a standard deviation. As can be seen in Table 1 below, the exemplary system, method and computer-accessible medium produced comparable results to the top experts, and compares favorably to most experts.
The use of ultrasound in the emergency department is commonplace. The emergency department of a hospital or a medical center can be one of the busiest, stressed and scary places in the healthcare system. The need for a fast and reliable diagnosis can be crucial in order to improve patient outcome. Ultrasound currently has an important role in the following exemplary areas: Pulmonary, cardiac, abdominal scanning and OB-GYN. Additionally, acute scanning of orthopedic abnormalities, including fractures, has been introduced and incorporated into the ultrasound examination in the emergency department. The evolution of handheld devices that facilitate the clinician to scan without searching for equipment in the emergency department facilitates the process for critical decision while also gaining procedural guidance with high-quality ultrasound imaging.
The attraction of immediate bedside sonographic examination in the evaluation of specific emergent complaints can make it an ideal tool for the emergency physician. The increasing pressure to triage, diagnose, and rapidly treat the patient has fueled ultrasound use as the primary screening tool in the emergency department. The major areas currently being used in the ED are abdominal, pelvic, cardiac, and trauma.
Currently, for example, a minimum of 12 months of specific training is needed in order to train an emergency department physician to become an expert in ultrasound. There is an official ultrasound fellowship for emergency department physicians that takes a full 12 months of training. Not every emergency department in the country is currently staffed by an expert ultrasonographer. Thus, the exemplary system, method and computer-accessible medium, can assist the physician in the emergency room quickly identify abnormalities in different body systems.
Emergent cardiac ultrasound can be used to assess for pericardial effusion and tamponade, cardiac activity, infarction, a global assessment of contractility, and the detection of central venous volume status, as well as a suspected pulmonary embolism. Ultrasound also has been incorporated into resuscitation of the critically ill and the at-risk patients. In the assessment of a patient with undifferentiated hypotension, emergent cardiac ultrasound can also be expanded for the use in heart failure and dyspnea. The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can perform the following exemplary functions:
Exemplary Abnormal Segmental Movement. The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can also alert the operator if there can be abnormal motion including akinesia, hypokinesia, dyskinesia and paradoxical movement of any part of the ventricular wall and septum, indicating potential ventricular ischemia or infarction. Left Ventricular Volume can be displayed in situations of Hypovolumia. (See e.g., diagram shown in
The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can be used in an emergency department for the following exemplary examinations:
The exemplary uses of ultrasound in the emergency department have the potential to use deep learning for a faster and more acute diagnosis.
Emergency physicians' use of ultrasound can provide timely and cost-effective means to accurately diagnose emergency conditions during illness and injury in order to provide a higher-quality, lower-cost, care. ED ultrasound use can often reduce the need for more expensive studies such as CT or MRIs and can reduce unnecessary admissions for more comprehensive diagnostic workup. Additionally, the moving of the patient from one lab to the other requires manpower and complex queue scheduling and monitoring. Thus, the exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can provide a new way of using ultrasound in the emergency department. (See e.g., diagram shown in
For example, as shown in an exemplary diagram of
An exemplary assessment of the examination can result in improving point of care. Using established analytic tools can facilitate much faster and reliable diagnosis. There can be no need for major requirements for special training. The exemplary system, method and computer-accessible medium, according to an exemplary embodiment of the present disclosure, can be used in other fields including military arena, telemedicine applications, urgent care facilities and in-home healthcare. (See e.g., diagram shown in
As shown in
Further, the exemplary processing arrangement 2505 can be provided with or include an input/output arrangement 2535, which can include, for example a wired network, a wireless network, the internet, an intranet, a data collection probe, a sensor, etc. As shown in
The foregoing merely illustrates the principles of the disclosure. Various modifications and alterations to the described embodiments will be apparent to those skilled in the art in view of the teachings herein. It will thus be appreciated that those skilled in the art will be able to devise numerous systems, arrangements, and procedures which, although not explicitly shown or described herein, embody the principles of the disclosure and can be thus within the spirit and scope of the disclosure. Various different exemplary embodiments can be used together with one another, as well as interchangeably therewith, as should be understood by those having ordinary skill in the art. In addition, certain terms used in the present disclosure, including the specification, drawings and claims thereof, can be used synonymously in certain instances, including, but not limited to, for example, data and information. It should be understood that, while these words, and/or other words that can be synonymous to one another, can be used synonymously herein, that there can be instances when such words can be intended to not be used synonymously. Further, to the extent that the prior art knowledge has not been explicitly incorporated by reference herein above, it is explicitly incorporated herein in its entirety. All publications referenced are incorporated herein by reference in their entireties.
The following references are hereby incorporated by reference in their entireties:
[1] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio, Generative adversarial nets, Advances in neural information processing systems, 2014, pp. 2672-2680.
[2] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros, Image-to-image translation with conditional adversarial networks, arXiv preprint arXiv:1611.07004 (2016).
[3] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton, Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, 2012, pp. 1097-1105.
This application is a continuation application of U.S. patent application Ser. No. 16/478,507, filed on Jul. 17, 2019, which is a national phase patent application of International patent application No. PCT/US2018/014536, filed Jan. 19, 2018, which relates to and claims priority from U.S. patent application Ser. No. 62/448,061, filed on Jan. 19, 2017, the entire disclosures of which are incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| 62448061 | Jan 2017 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 16478507 | Jul 2019 | US |
| Child | 18048873 | US |
