Patent Application
20100082506
The invention relates generally to the field of medical data processing and, more specifically, to techniques for training and using learning machines.
In the medical field, many different tools are available for learning about and treating patient conditions. Traditionally, physicians would physically examine patients and draw upon a vast array of personal knowledge gleaned from years of study and experience to identify problems and conditions experienced by patients, and to determine appropriate treatments. Sources of support information traditionally included other practitioners, reference books and manuals, relatively straightforward examination results and analyses, and so forth. Over the past decades, and particularly in recent years, a wide array of further reference materials and decision support tools have become available to the practitioner that greatly expand the resources available and enhance and improve patient care.
For instance, vast amounts of information related to a patient, such as identifying information, medical history, test results, image data, and the like, may be collected and stored in electronic form in an electronic medical record (EMR) for that patient. Such EMRs may improve the decision-making process of a clinician by providing all, or a substantial portion, of relevant patient data to the clinician in an efficient manner, rather than requiring the clinician to collect data from multiple locations and sources. Further, it may be appreciated that the collection of relevant patient data in a central location, such as an EMR, may facilitate the development of decision-support tools to aid the clinician in diagnosing and treating a patient. An “active” EMR, for instance, uses the data in the EMR in a processing algorithm to provide support to the clinician in a decision-making process.
One exemplary processing algorithm can be a learning algorithm for classifying objects based on their features to solve problems of interest. It will be appreciated, however, that the development of such a learning algorithm, including the training and testing of the learning algorithm, is typically a lengthy process. Moreover, such learning algorithms often depend on data characteristics particular to the data acquisition system with which the data was obtained. Consequently, learning algorithms are seldom used in medical applications due to the fact that medical technology rapidly evolves and that learning algorithms trained and tested based on data previously gathered may no longer be applicable to current data acquired with newer or different technologies.
Certain aspects commensurate in scope with the originally claimed invention are set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of certain forms the invention might take and that these aspects are not intended to limit the scope of the invention. Indeed, the invention may encompass a variety of aspects that may not be set forth below.
Embodiments of the present invention may generally relate to techniques for training a learning algorithm or machine and for processing data with such an algorithm or machine. In one embodiment, a learning machine is trained, tested, and validated through a data driven process. In another embodiment, data is received from one or more data acquisition systems, and acquisition source-invariant features are derived from the data and subsequently processed by a learning algorithm to provide decision-making support to a user. Particularly, in one embodiment, the process provides decision-making support to a clinician in diagnosing a patient.
Various refinements of the features noted above may exist in relation to various aspects of the present invention. Further features may also be incorporated in these various aspects as well. These refinements and additional features may exist individually or in any combination. For instance, various features discussed below in relation to one or more of the illustrated embodiments may be incorporated into any of the above-described aspects of the present invention alone or in any combination. Again, the brief summary presented above is intended only to familiarize the reader with certain aspects and contexts of the present invention without limitation to the claimed subject matter.
These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
One or more specific embodiments of the present invention will be described below. In an effort to provide a concise description of these embodiments, all features of an actual implementation may not be described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure.
When introducing elements of various embodiments of the present invention, the articles “a,” “an,” “the,” and “said” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. Moreover, while the term “exemplary” may be used herein in connection to certain examples of aspects or embodiments of the presently disclosed technique, it will be appreciated that these examples are illustrative in nature and that the term “exemplary” is not used herein to denote any preference or requirement with respect to a disclosed aspect or embodiment. Further, any use of the terms “top,” “bottom,” “above,” “below,” other positional terms, and variations of these terms is made for convenience, but does not require any particular orientation of the described components.
Turning now to the drawings, and referring first to
In general, the exemplary processor-based system 10 includes a microcontroller or microprocessor 12, such as a central processing unit (CPU), which executes various routines and processing functions of the system 10. For example, the microprocessor 12 may execute various operating system instructions as well as software routines configured to effect certain processes and stored in or provided by a manufacture including a computer readable-medium, such as a memory 14 (e.g., a random access memory (RAM) of a personal computer) or one or more mass storage devices 16 (e.g., an internal or external hard drive, a solid-state storage device, CD-ROM, DVD, or other storage device). In addition, the microprocessor 12 processes data provided as inputs for various routines or software programs, such as data provided as part of the present technique in computer-based implementations.
Such data may be stored in, or provided by, the memory 14 or mass storage device 16. Alternatively, such data may be provided to the microprocessor 12 via one or more input devices 18. As will be appreciated by those of ordinary skill in the art, the input devices 18 may include manual input devices, such as a keyboard, a mouse, or the like. In addition, the input devices 18 may include a network device, such as a wired or wireless Ethernet card, a wireless network adapter, or any of various ports or devices configured to facilitate communication with other devices via any suitable communications network 24, such as a local area network or the Internet. Through such a network device, the system 10 may exchange data and communicate with other networked electronic systems, whether proximate to or remote from the system 10. It will be appreciated that the network 24 may include various components that facilitate communication, including switches, routers, servers or other computers, network adapters, communications cables, and so forth.
Results generated by the microprocessor 12, such as the results obtained by processing data in accordance with one or more stored routines, may be provided to an operator via one or more output devices, such as a display 20 and/or a printer 22. Based on the displayed or printed output, an operator may request additional or alternative processing or provide additional or alternative data, such as via the input device 18. As will be appreciated by those of ordinary skill in the art, communication between the various components of the processor-based system 10 may typically be accomplished via a chipset and one or more busses or interconnects which electrically connect the components of the system 10. Notably, in certain embodiments of the present technique, the exemplary processor-based system 10 may be configured to process data and to classify objects in the data with a learning algorithm, as discussed in greater detail below.
An exemplary system 30 for acquiring and processing data in accordance with one embodiment of the present invention is illustrated in
It will be appreciated that the data 34 may be stored in a database 40, and that the data processing system 38 may receive the data 34 directly from the data acquisition systems 32, from the database 40, or in any other suitable fashion. Further, the data processing system 38 may also receive additional data from the database 40 for processing. As discussed in greater detail below, the processing performed by the data processing system 38 may include organizing the data 34 or additional data into multiple objects based on a problem of interest, deriving source-invariant features from the organized data, classifying the objects based on the source-invariant features, organizing the results to facilitate solving of the problem of interest, and outputting some indication of the results, as generally indicated by the report 42 in
While additional details of the operation of a data processing system 38 in accordance with certain embodiments are provided below, it is first noted that the presently disclosed techniques are applicable to data obtained from a wide array of data sources (e.g., data acquisition systems 32) and having varying characteristics and formats that may depend on the type of data source from which the data is obtained. In some embodiments, an exemplary data acquisition system 50 may include certain typical modules or components as indicated generally in
The sensors generate signals or data representative of the sensed parameters. Such raw data may be transmitted to a data acquisition module 54. The data acquisition module may acquire sampled or analog data, and may perform various initial operations on the data, such as filtering, multiplexing, and so forth. The data may then be transmitted to a signal conditioning module 56 where further processing is performed, such as for additional filtering, analog-to-digital conversion, and so forth. A processing module 58 then receives the data and performs processing functions, which may include simple or detailed analysis of the data. A display/user interface 60 permits the data to be manipulated, viewed, and output in a user-desired format, such as in traces on screen displays, hardcopy, and so forth. The processing module 58 may also mark or analyze the data for marking such that annotations, delimiting or labeling axes or arrows, and other indicia may appear on the output produced via interface 60. Finally, an archive module 62 serves to store the data either locally within the resource, or remotely. The archive module may also permit reformatting or reconstruction of the data, compression of the data, decompression of the data, and so forth. The particular configuration of the various modules and components illustrated in
It will be appreciated that the data acquisition systems 32 may include a number of non-imaging systems capable of collecting desired data from a patient. For instance, the data acquisition systems 32 may include, among others, an electroencephalography (EEG) system, an electrocardiography (ECG or EKG) system, an electromyography (EMG) system, an electrical impedance tomography (EIT) system, an electronystagmography (ENG) system, a system adapted to collect nerve conduction data, or some combination of these systems. The data acquisition systems may also or instead include various imaging resources, as discussed below with respect to
It will be appreciated that such imaging resources may be employed to diagnose medical events and conditions in both soft and hard tissue, and for analyzing structures and function of specific anatomies. Moreover, imaging systems are available which can be used during surgical interventions, such as to assist in guiding surgical components through areas which are difficult to access or impossible to visualize.
Referring to
The imager operates under the control of system control circuitry 74. The system control circuitry may include a wide range of circuits, such as radiation source control circuits, timing circuits, circuits for coordinating data acquisition in conjunction with patient or table of movements, circuits for controlling the position of radiation or other sources and of detectors, and so forth. The imager 72, following acquisition of the image data or signals, may process the signals, such as for conversion to digital values, and forwards the image data to data acquisition circuitry 76. In the case of analog media, such as photographic film, the data acquisition system may generally include supports for the film, as well as equipment for developing the film and producing hard copies that may be subsequently digitized. For digital systems, the data acquisition circuitry 76 may perform a wide range of initial processing functions, such as adjustment of digital dynamic ranges, smoothing or sharpening of data, as well as compiling of data streams and files, where desired. The data is then transferred to data processing circuitry 78 where additional processing and analysis are performed. For conventional media such as photographic film, the data processing system may apply textual information to films, as well as attach certain notes or patient-identifying information. For the various digital imaging systems available, the data processing circuitry perform substantial analyses of data, ordering of data, sharpening, smoothing, feature recognition, and so forth.
Ultimately, the image data is forwarded to some type of operator interface 80 for viewing and analysis. While operations may be performed on the image data prior to viewing, the operator interface 80 is at some point useful for viewing reconstructed images based upon the image data collected. It should be noted that in the case of photographic film, images are typically posted on light boxes or similar displays to permit radiologists and attending physicians to more easily read and annotate image sequences. The images may also be stored in short or long term storage devices, for the present purposes generally considered as included within the interface 80, such as picture archiving communication systems. The image data can also be transferred to remote locations, such as a remote data processing system 38, via the network 24. It should also be noted that, from a general standpoint, the operator interface 80 affords control of the imaging system, typically through interface with the system control circuitry 74. Moreover, it should also be noted that more than a single operator interface 80 may be provided. Accordingly, an imaging scanner or station may include an interface which permits regulation of the parameters involved in the image data acquisition procedure, whereas a different operator interface may be provided for manipulating, enhancing, and viewing resulting reconstructed images.
Turning to more detailed examples of imaging systems that may be employed in conjunction with the present technique, a digital X-ray system 84 is generally depicted in
System 84 illustrated in
Detector 92, which typically includes a matrix of pixels, encodes intensities of radiation impacting various locations in the matrix. A scintillator converts the high energy X-ray radiation to lower energy photons which are detected by photodiodes within the detector. The X-ray radiation is attenuated by tissues within the patient, such that the pixels identify various levels of attenuation resulting in various intensity levels which will form the basis for an ultimate reconstructed image.
Control circuitry and data acquisition circuitry are provided for regulating the image acquisition process and for detecting and processing the resulting signals. In particular, in the illustration of
The data processing circuitry 98 may perform a range of operations, including adjustment for offsets, gains, and the like in the digital data, as well as various imaging enhancement functions. The resulting data is then forwarded to an operator interface, the data processing system 38, or a storage device for short or long-term storage. The images reconstructed based upon the data may be displayed on the operator interface, or may be forwarded to other locations, such as via a network 24 for viewing or additional processing. Also, digital data may be used as the basis for exposure and printing of reconstructed images on a conventional hard copy medium such as photographic film.
The scanner 104 is coupled to gradient coil control circuitry 106 and to RF coil control circuitry 108. The gradient coil control circuitry permits regulation of various pulse sequences which define imaging or examination methodologies used to generate the image data. Pulse sequence descriptions implemented via the gradient coil control circuitry 106 are designed to image specific slices, anatomies, as well as to permit specific imaging of moving tissue, such as blood, and defusing materials. The pulse sequences may allow for imaging of multiple slices sequentially, such as for analysis of various organs or features, as well as for three-dimensional image reconstruction. The RF coil control circuitry 108 permits application of pulses to the RF excitation coil, and serves to receive and partially process the resulting detected MR signals. It should also be noted that a range of RF coil structures may be employed for specific anatomies and purposes. In addition, a single RF coil may be used for transmission of the RF pulses, with a different coil serving to receive the resulting signals.
The gradient and RF coil control circuitry function under the direction of a system controller 110. The system controller implements pulse sequence descriptions which define the image data acquisition process. The system controller will generally permit some amount of adaptation or configuration of the examination sequence by means of an operator interface 80.
Data processing circuitry 112 receives the detected MR signals and processes the signals to obtain data for reconstruction. In general, the data processing circuitry 112 digitizes the received signals, and performs a two-dimensional fast Fourier transform on the signals to decode specific locations in the selected slice from which the MR signals originated. The resulting information provides an indication of the intensity of MR signals originating at various locations or volume elements (voxels) in the slice. Each voxel may then be converted to a pixel intensity in image data for reconstruction. The data processing circuitry 112 may perform a wide range of other functions, such as for image enhancement, dynamic range adjustment, intensity adjustments, smoothing, sharpening, and so forth. The resulting processed image data is typically forwarded to an operator interface for viewing, as well as to short or long-term storage, or may be forwarded to a data processing system for additional processing. As in the case of foregoing imaging systems, MR image data may be viewed locally at a scanner location, or may be transmitted to remote locations both within an institution and remote from an institution such as via the network 24.
During an examination sequence, as the source and detector are rotated, a series of view frames are generated at angularly-displaced locations around a patient 36 positioned within the gantry. A number of view frames (e.g. between 500 and 1000) are collected for each rotation, and a number of rotations may be made, such as in a helical pattern as the patient is slowly moved along the axial direction of the system. For each view frame, data is collected from individual pixel locations of the detector to generate a large volume of discrete data. A source controller 128 regulates operation of the radiation source 118, while a gantry/table controller 130 regulates rotation of the gantry and control of movement of the patient.
Data collected by the detector is digitized and forwarded to a data acquisition circuitry 132. The data acquisition circuitry may perform initial processing of the data, such as for generation of a data file. The data file may incorporate other useful information, such as relating to cardiac cycles, positions within the system at specific times, and so forth. Data processing circuitry 134 then receives the data and performs a wide range of data manipulation and computations.
In general, data from the CT scanner can be reconstructed in a range of manners. For example, view frames for a full 360° of rotation may be used to construct an image of a slice or slab through the patient. However, because some of the information is typically redundant (imaging the same anatomies on opposite sides of a patient), reduced data sets comprising information for view frames acquired over 180° plus the angle of the radiation fan may be constructed. Alternatively, multi-sector reconstructions are utilized in which the same number of view frames may be acquired from portions of multiple rotational cycles around the patient. Reconstruction of the data into useful images then includes computations of projections of radiation on the detector and identification of relative attenuations of the data by specific locations in the patient. The raw, the partially processed, and the fully processed data may be forwarded for post-processing, storage and image reconstruction. The data may be available immediately to an operator, such as at an operator interface 80, and may be transmitted remotely via a network connection 24.
The scanner 146 operates under the control of scanner control circuitry 148, itself regulated by an operator interface 80. In most PET scans, the entire body of the patient is scanned, and signals detected from the gamma radiation are forwarded to data acquisition circuitry 150. The particular intensity and location of the radiation can be identified by data processing circuitry 152, and reconstructed images may be formulated and viewed on operator interface 80, or the raw or processed data may be stored for later image enhancement, analysis, and viewing. The images, or image data, may also be transmitted to remote locations via a link to the network 24.
PET scans are typically used to detect cancers and to examine the effects of cancer therapy. The scans may also be used to determine blood flow, such as to the heart, and may be used to evaluate signs of coronary artery disease. Combined with a myocardial metabolism study, PET scans may be used to differentiate non-functioning heart muscle from heart muscle that would benefit from a procedure, such as angioplasty or coronary artery bypass surgery, to establish adequate blood flow. PET scans of the brain may also be used to evaluate patients with memory disorders of undetermined causes, to evaluate the potential for the presence of brain tumors, and to analyze potential causes for seizure disorders. In these various procedures, the PET image is generated based upon the differential uptake of the tagged materials by different types of tissue.
Although certain imaging systems have been described above for the sake of explanation, it should be noted that the presently disclosed data processing system 38 may process data from additional and/or special-purpose imaging systems, such as a fluorography system, a mammography system, a sonography system, a thermography system, other nuclear medicine systems, or a thermoacoustic system, to name but a few possibilities. Additionally, as noted above, the data processing system 38 may also receive and process additional data obtained from other non-imaging data sources, including that obtained from a database or computer workstation, in full accordance with the present technique.
One embodiment of the presently disclosed technique may be better understood with reference to
The method 160 also includes a step 168 of identifying source-invariant features in the organized data. As noted above, data collected from a plurality of different acquisition systems may be of different types or have different formats based on the type of acquisition system generating the data. Further, learning machines and learning algorithms are often adapted to receive specific types of data in a specific format, such as that acquired by a single type of data acquisition system (e.g., a CT system, an MRI system, or the like). In various embodiments of the present invention, however, a data processing system may advantageously pre-process the data to identify features in the data that describe objects of interest (e.g., a nodule) in a source-invariant manner. Such features may include, but are not limited to, geometric (i.e., shape) features, textural features, object density, or the like. For instance, in a scenario where the problem of interest is tumor identification and one of the features of a learning algorithm is a sphere having a diameter within a certain range, the data processing system may receive image data from two different data acquisition systems having differing image resolution capabilities, and may be processed differently to derive source-invariant data features.
Once source-invariant features of an object of interest are identified, the exemplary method 160 continues with classification of the objects in step 170. In some embodiments, the objects are classified through use of any suitable learning algorithm or machine. An example of a learning algorithm for classification is a support vector machine. As may be appreciated, support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression and belong to a family of generalized linear classifiers. SVMs can also be considered a special case of Tikhonov regularization. SVMs may simultaneously minimize the empirical classification error and maximize the geometric margin and, consequently, may also be known as maximum margin classifiers.
It is noted again, however, that such learning algorithms and machines are typically trained, tested, and validated based on specific types of data, such as data having a common format from a single data source or similar data sources. Thus, in order to use the learning algorithm or machine with a different type of data other than that used in originally training, testing, and validating the algorithm, the learning algorithm and machine would typically have to be re-trained, re-tested, and re-validated based on a new set of training data. In some embodiments of the presently disclosed technique, however, data features may be pre-processed to describe such features in a source-invariant manner, such that the learning algorithm may classify objects based on source-invariant features obtained from data having different characteristics and received from different data sources. Consequently, the identification of acquisition source-invariant features in the data allows a learning classification algorithm to be broadly applied to a variety of data types from different sources, and may avoid a need to re-train, re-test, and re-validate the algorithm upon changes in data acquisition sources or technologies. Additionally, in some embodiments, the classification of the objects is based not only on image data or source-invariant features of such image data, but also on non-image data received by the data processing system 38. For instance, in one embodiment, the classification may be based on both image data and on non-image data, such as meta-data from an electronic medical record. Also, the results of this classification process may be organized in step 172 prior to any output indicative of the results in step 174, as discussed in greater detail below with respect to
Various components for carrying out the functionality described above are illustrated in the block diagram 178 of
As may be appreciated, multiple people may be interested in the results of the classification process, but may desire different levels of detail with respect to such results. Consequently, in one embodiment generally represented in block diagram 192 of
An exemplary machine training and validation method 210 is generally illustrated in
Finally, based on the foregoing, it may be appreciated that the present technique allows for significant independence in the learning steps used to train the learning machine, including data independence, feature independence, and algorithmic independence. Notably, the data independence provides the flexibilities to change the types of data being integrated without impacting the generation of source-invariant features used for the learning process. Further, the feature independence provides flexibility in the generation of source-invariant processes without impacting the selection of particular learning algorithms, thus allowing the present technique to employ multiple algorithms during the learning process. Still further, the algorithmic independence provides the flexibilities of selecting and working with varieties of learning algorithms without impacting the results and, ultimately, the knowledge generated from these learning algorithms. Consequently, the independence afforded by the present technique may result in a learning process that is more flexible, more adaptable, more efficient, and more powerful than previous learning processes. Further, the identification and use of acquisition system-invariant features may reduce or eliminate the need to re-train a learning classification algorithm due to differing data sources or technological changes. Still further, in one embodiment, the present technique facilitates classification based on both acquisition-system invariant features as well as active EMR meta-data such that the classification of objects is based on holistic considerations.
While only certain features of the invention have been illustrated and described herein, many modifications and changes will occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.
