Patent Publication Number: US-2021192717-A1

Title: Systems and methods for identifying atheromatous plaques in medical images

Description:
FIELD 
     The present description relates generally to medical imaging, such as optical coherence tomographic imaging, and more particularly to systems and methods for identifying atheromatous plaques in optical coherence tomography images. 
     BACKGROUND 
     Atheromatous plaques may build up in arteries, which, when left untreated, may result in thrombosis. Thrombosis may further lead to acute coronary syndromes detrimental to human health (e.g., sudden coronary death). Certain types of atheromatous plaques, such as thin-cap fibroatheromas (TCFAs), are of particular interest, as these plaques are vulnerable to potentially lethal plaque ruptures. It is therefore desirable to know where a TCFA is within the artery so that appropriate medical intervention (e.g., a surgical procedure) may be taken to prevent such ruptures. 
     To allow medical professionals to locate TCFAs, medical imaging techniques, such as optical coherence tomography (OCT), have been developed to image arteries. For example, OCT imaging techniques may scan at least a portion of the artery to generate a three-dimensional volumetric dataset from which two-dimensional cross-sections, or image slices, of the artery may be generated. The OCT image slices are often displayed in polar coordinates, in effect “unrolling” the wall of the artery (initially imaged in Cartesian coordinates as a circular shape) to facilitate TCFA identification. The medical professional may then mark a start coordinate and an end coordinate on a given image slice, indicating a location of the TCFA. 
     However, there are limitations to human identification. For example, differing medical professionals with varying levels of experience may identify different start and end coordinates for the TCFA (e.g., wider or tighter bounds), leading to inconsistent treatment. In some cases, the medical professional may accidentally miss a TCFA entirely, which may delay diagnosis. Advanced automated imaging techniques have been employed, but to limited success. Though such techniques may be able to detect inconsistencies between images, prior implementations have not been successful at determining locations for the TCFA in a replicable fashion. Thus, a challenge persists in the art to provide accurate, consistent, and automated identification of a location of a TCFA in OCT image slices. 
     The inventors have identified the above problems and herein provide systems and methods to at least partially address them. 
     SUMMARY 
     The current disclosure provides systems and methods for training and using a neural network to identify atheromatous plaques in optical coherence tomography (OCT) images. In one example, a method for a trained neural network may include acquiring an OCT image slice of an artery, identifying one or more image features of the OCT image slice with the trained neural network, and responsive to the one or more image features indicating a thin-cap fibroatheroma (TCFA), segmenting the OCT image slice into a plurality of regions with the trained neural network, the plurality of regions including a first region depicting the TCFA, and determining start and end coordinates for the TCFA based on the first region. 
     In another example, a method may include training a neural network to identify a TCFA in OCT image slices, where identifying the TCFA may include identifying TCFA features in the OCT image slices, and generating bounding boxes in the OCT image slices for the TCFA based on the TCFA features, receiving a particular OCT image slice depicting a particular TCFA, and identifying the particular TCFA in the particular OCT image slice using the trained neural network. 
     In yet another example, a medical imaging system may include a scanner operable to collect OCT imaging data of a plaque, a memory storing a trained neural network configured to separate visual characteristics from content of an image, and a processor communicably coupled to the scanner and the memory, wherein the processor is configured to receive the OCT imaging data from the scanner, generate a sequentially ordered set of OCT images from the OCT imaging data, where a subset of the OCT images may depict the plaque, identify, via the trained neural network, the subset of OCT images depicting the plaque, generate, via the trained neural network, a bounding box circumscribing the plaque in each OCT image in the subset of OCT images, and determine, for each OCT image in the subset of OCT images, start and end coordinates for the plaque based on the bounding box. 
     The above examples may provide several advantages over the current state of the art. Typically, OCT images received for a given scan of a given patient may include ˜270 frames. A medical professional may necessarily resort to inspection of each individual frame to determine TCFA boundaries. Such a process may be both time-consuming and may invite human error and oversight. In contrast, the methods and systems provided herein may, in some examples, provide medical professionals with an assessment for the ˜270 frames in less than 10 seconds. As such, a given medical professional may save time during particularly time-sensitive medical procedures, such as intraoperative surgeries, where the medical professional may need to localize critical regions quickly. Further, since detection and localization of TCFAs in OCT images may be challenging and thus subject to differences in even experienced opinions, agreement between medical professionals may be low, varying from one medical professional to another. 
     According to at least some of the embodiments provided herein, a machine learning framework may mitigate such inaccuracies and disagreements by learning agreed TCFA boundaries from multiple medical professionals in a systematic manner. As such, medical professionals may be provided with a means for increasing precision of both medical procedures and of their personal skill level in TCFA detection, and thus improved agreements between medical professionals in diagnoses of TCFAs may be achieved. 
     It should be understood that the summary above is provided to introduce in simplified form a selection of concepts that are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is defined uniquely by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any part of this disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows an example optical coherence tomography (OCT) imaging system. 
         FIG. 2  shows a high-level flow diagram of a processor operable to receive OCT imaging data as an input and output a thin-cap fibroatheroma (TCFA) region. 
         FIG. 3  shows a high-level flow diagram of a preprocessing module operable to preprocess the OCT imaging data. 
         FIG. 4  shows a schematic diagram illustrating an example neural network used for identifying a TCFA in an OCT image. 
         FIG. 5  shows a schematic diagram of an example process for generating bounding boxes on the OCT image using the example neural network. 
         FIG. 6A  shows a schematic diagram of an overlap between two pairs of vertical lines superimposed on the OCT image. 
         FIG. 6B  shows a schematic diagram of an overlap between two boxes superimposed on the OCT image. 
         FIG. 7  shows an example plot of an agreement between doctors in identification of OCT images depicting TCFAs. 
         FIG. 8  shows an example plot of an agreement between doctors in identification of TCFA regions in OCT images. 
         FIG. 9  shows a flow chart of a method for identifying the TCFA in the OCT image and displaying the OCT image. 
         FIG. 10  shows a flow chart of a first exemplary method for training the neural network to identify TCFAs in OCT images. 
         FIG. 11  shows a flow chart of a second exemplary method for training the neural network to identify TCFAs in OCT images. 
     
    
    
     DETAILED DESCRIPTION 
     The current disclosure provides systems and methods for training and using a neural network to identify atheromatous plaques, such as thin-cap fibroatheromas (TCFAs), in medical images, such as optical coherence tomography (OCT) images. One example OCT imaging system for generating and displaying OCT images, and identifying TCFAs therein, is depicted at  FIG. 1 .  FIG. 2  depicts a high-level flow diagram for a processor operable to read an OCT imaging data input and output a processed OCT image for display via the OCT imaging system of  FIG. 1 , for example.  FIG. 3  depicts a high-level flow diagram for a preprocessing module stored on a processor, such as the processor of  FIG. 2 , where the preprocessing module may be operable to read the OCT imaging data input and output preprocess OCT imaging data. An example convolutional neural network (CNN) configured to separate visual characteristics from content of an image is depicted at  FIG. 4 . The example CNN may be implemented on the OCT imaging system of  FIG. 1  to identify TCFAs in OCT images. An example process for generating bounding boxes with an example neural network, such as the example CNN of  FIG. 4 , is depicted at  FIG. 5 .  FIGS. 6A and 6B  depict two example overlaps between TCFA regions. Example plots showing agreement between doctors in TCFA identification are depicted at  FIGS. 7 and 8 . A method for identifying the TCFA in the OCT image and display the image is depicted at  FIG. 9 . Exemplary methods for training an example neural network, such as the example CNN of  FIG. 4 , are depicted at  FIGS. 10 and 11 . 
     Referring now to  FIG. 1 , a block diagram of an example system  100  is depicted according to an embodiment. In the illustrated embodiment, the system  100  is an imaging system and, more specifically, an OCT imaging system. However, it is understood that embodiments set forth herein may be implemented using other types of medical imaging modalities (e.g., magnetic resonance, ultrasound, etc.). Furthermore, it is understood that other embodiments do not actively acquire medical images. Instead, embodiments may retrieve image or OCT data that was previously acquired by an imaging system and analyze the image data as set forth herein. As shown, the system  100  includes multiple components. The components may be coupled to one another to form a single structure, may be separate but located within a common room, or may be remotely located with respect to one another. For example, one or more of the modules described herein may operate in a data server that has a distinct and remote location with respect to other components of the system  100 , such as a probe and user interface. Optionally, in the case of OCT systems, the system  100  may be a unitary system that is capable of being moved (e.g., portably) from room to room. For example, the system  100  may include wheels or be transported on a cart. 
     In the illustrated embodiment, the system  100  may include a scanner  106  which may deliver continuous or pulsed low-coherence light into a body or volume (not shown) of a subject. The low-coherence light may be back-scattered from structures (e.g., an artery) in the body to produce echoes subsequently collected as OCT image signals. Scanners such as scanner  106  are well-known to those skilled in the art and will therefore be referenced only generally herein as relates to the described embodiments. The scanner  106  may be included in an intracoronary OCT probe attached to, or implemented in, a catheter, which may be utilized in a medical intervention procedure to image at least a portion of an artery of a subject. As such, the scanner  106  may be operable to collect OCT imaging data, such as three-dimensional (3D) volumetric OCT imaging data, depicting the artery. The artery may have a plaque, such as a TCFA, and an operator of the system  100  may utilize the scanner  106  to image the plaque. The 3D volumetric OCT imaging data may be processed as a set, or series, of sequential two-dimensional (2D) OCT image slices along a length of the artery, each of which may depict a cross section of a wall of the artery, for example. In some examples, an imaging resolution of less than 20 μm may be obtained by the scanner  106 . The scanner  106  may be communicably coupled to a system controller  102  that may be part of a single processing unit, or processor, or distributed across multiple processing units. The system controller  102  is configured to control operation of the system  100 . 
     For example, the system controller  102  may include an image-processing module (as described in greater detail below with reference to  FIG. 2 ) that receives image data (e.g., OCT image signals) and processes the image data. For example, the image-processing module may process 3D volumetric OCT imaging data to generate 2D image slices of OCT information (e.g., OCT images) for displaying to an operator (not shown) of the system  100 . Similarly, the image-processing module may process the OCT image signals to generate 3D renderings of OCT information (e.g., OCT images) for displaying to the operator. When the system  100  is an OCT system, the image-processing module may be configured to perform one or more processing operations according to a plurality of selectable OCT modalities on the acquired OCT information. For example, the image-processing module may implement a TCFA detection library for the identification of TCFAs in the OCT images. 
     Acquired OCT information may be processed in real-time during an imaging session (or scanning session) as the echoed signals are received. Additionally or alternatively, the OCT information may be stored temporarily in a memory  104  during an imaging session and processing less than real-time in a live or off-line operation. For longer-term storage, a storage device  108  is included for storing processed slices of acquired OCT information that are not scheduled to be displayed immediately. Further, the storage device  108  may store one or more datasets, such as training sets, for use with the image-processing module. The storage device  108  may include any known data storage medium, for example, a permanent storage medium, removable storage medium, and the like. Additionally, either or both of the memory  104  and the storage device  108  may be a non-transitory storage medium. 
     In operation, an OCT system may acquire data, for example, volumetric datasets, by various techniques (for example, 3D scanning, real-time 3D imaging, volume scanning, and the like). OCT images may be generated from the acquired data (at the controller  102 ) and displayed to the operator or user on a display device  112 . Further, the system controller  102  may be communicably coupled to the display device  112  via a user interface  110  that enables an operator to control at least some operations of the system  100 . The user interface  110  may include hardware, firmware, software, or a combination thereof that enables an individual (e.g., an operator) to directly or indirectly control operation of the system  100  and the various components thereof. As shown, the user interface  110  may include a display device  112  having a display area  114 . In some embodiments, the user interface  110  may be operably connected to one or more user interface input devices  116 , such as a physical keyboard, mouse, and/or touchpad. In one example, a touchpad may be configured to the system controller  102  and display area  114 , such that when a user moves a finger/glove/stylus across a face of the touchpad, a cursor atop a displayed OCT image on the display device  112  may move in a corresponding manner. 
     In an exemplary embodiment, the display device  112  is a touch-sensitive display (e.g., touchscreen) which may detect a presence of a touch from the operator on the display area  114  and may also identify a location of the touch in the display area  114 . The touch may be applied by, for example, at least one of an individual&#39;s hand, glove, stylus, or the like. As such, the touch-sensitive display may also be characterized as an input device that is configured to receive inputs from the operator (such as a request to adjust or update an orientation of a displayed image). The display device  112  may also communicate information from the controller  102  to the operator by displaying the information to the operator. The display device  112  and/or the user interface  110  may also communicate audibly. The display device  112  is configured to present information to the operator during or after the imaging or data acquiring session. The information presented may include OCT images (e.g., one or more 2D slices), graphical elements, measurement graphics of the displayed images, user-selectable elements, user settings, and other information (e.g., administrative information, personal information of the patient, and the like). 
     In addition to the image-processing module, the system controller  102  may also include one or more of a graphics module, an initialization module, a tracking module, and an analysis module. The image-processing module, the graphics module, the initialization module, the tracking module, and/or the analysis module may coordinate with one another to present information to the operator during and/or after the imaging session. For example, the image-processing module may be configured to display an acquired image on the display device  112 , and the graphics module may be configured to display designated graphics along with the displayed image, such as selectable icons (e.g., image rotation icons) and measurement parameters (e.g., data) relating to the image. One or more of the controller  102 , the memory  104 , and the storage device  108  may include algorithms and one or more neural networks (e.g., a system of neural networks) stored within a memory of the controller for automatically recognizing one or more anatomical features depicted by a generated OCT image, such as a 2D slice, as described further below with reference to  FIGS. 4, 9, and 10 . In some examples, the controller may include a deep learning module which includes the one or more deep neural networks and instructions for performing the deep learning and feature recognition discussed herein. In some embodiments, the one or more deep neural networks may include a convolutional neural network (CNN) implementing a two-stage object detection algorithm. In some embodiments, the one or more deep neural networks may include a trained neural network configured to separate visual characteristics from content of an image. 
     A screen of the display area  114  of the display device  112  may be made up of a series of pixels which display the data acquired with the scanner  106 . The acquired data includes one or more imaging parameters calculated for each pixel, or group of pixels (for example, a group of pixels assigned the same parameter value), of the display, where the one or more calculated image parameters includes one or more of an intensity, velocity (e.g., blood flow velocity), color flow velocity, texture, graininess, contractility, deformation, and rate of deformation value. The series of pixels may then make up the displayed image generated from the acquired OCT data. 
     The system  100  may be a medical OCT system used to acquire imaging data of a scanned object (e.g., an artery of a subject). The acquired image data may be used to generate one or more OCT images which may then be displayed via the display device  112  of the user interface  110 . The one or more generated OCT images may include one or more 2D image slices, for example. 
     In some embodiments, the system controller  102  may be communicably coupled to a network  120  via a network interface  118 . For example, the system controller  102  may communicate with and/or across the network  120  in a wired and/or wireless manner via the network interface  118 . The network  120  may be a wireless telephone network, a wireless local area network, a wired local area network, a wireless wide area network, a wired wide area network, etc. In some embodiments, the network interface  118  may allow message to be sent and/or received to and/or from other devices, such as a remote device  122 , via the network  120  (e.g., the public Internet). In some embodiments, the network  120  may be regarded as a private network connection to the remote device  122  and may include, for example, a virtual private network or an encryption or other security mechanism employed over the public Internet. The remote device  122  may be a computing device, such as a personal computing device (e.g., a laptop, tablet, smartphone, etc.), or another system similar to system  100  (e.g., another medical imaging system). 
     Referring now to  FIG. 2 , a high-level flow diagram  200  of an image-processing module  202  including a TCFA detection library  204  is depicted. The image-processing module  202  may be implemented on system controller  102  of system  100  of  FIG. 1 , for example. As such, the image-processing module  202  may receive an OCT imaging data input  206  to be processed by the TCFA detection library  204 , which may return processed OCT image data including a TCFA region  218 . The TCFA detection library  204  may include one or more modules for processing of the OCT imaging data input  206 . In the exemplary embodiment depicted at  FIG. 2 , the TCFA detection library  204  may include a preprocessing module  208 , a deep learning module  210 , a bounding boxes module  212 , a postprocessing module  214 , and a TCFA coordinates module  216 . 
     The image-processing module  202  may be configured to receive OCT imaging data (e.g., 3D volumetric imaging data) from a scanner (e.g.,  106 ). From the OCT imaging data, the image-processing module  202  may generate a sequentially ordered set of OCT images (e.g., 2D image slices) from the OCT imaging data. The sequentially ordered set of OCT images may form the OCT imaging data input  206 , which may be input into the TCFA detection library  204  for processing. Upon receipt, the TCFA detection library  204  may first pass the OCT imaging data input  206  to the preprocessing module  208 , an embodiment of which is described below with reference to  FIG. 3 . 
     Referring now to  FIG. 3 , a high-level flow diagram  300  of the preprocessing module  208  is depicted. As discussed above with reference to  FIG. 2 , the preprocessing module  208  may be included in a TCFA detection library (e.g.,  204 ) implemented in an image-processing module (e.g.,  202 ). The processor may further be included in a system (e.g.,  100 ) operable for medical imaging. The preprocessing module  208  may receive the OCT imaging data input  206  for preprocessing for input to another module (e.g., the deep learning module  210  as described below with reference to  FIG. 2 ). 
     The OCT imaging data input  206  may first be passed to a read image function  302 , which may parse imaging data in the OCT imaging data input  206  for preprocessing. For example, the parsed data may include a matrix of pixel intensity values representing one or more images included in the OCT imaging data input  206 . The parsed data may then be passed to a subtract mean function  304 . The subtract mean function  304  may be operable to receive parsed data corresponding to one or more images (e.g., OCT image slices) and determine and subtract a mean value for each pixel as a function of that pixel&#39;s location in each of the one or more images. In other embodiments, the subtract mean function  304  may be operable to determine and subtract a mean value for one or more pixel channels in the parsed data. An output of the subtract mean function  304  may thus be less sensitive to detection of background “noise” (e.g., objects of non-interest which exhibit relatively little change across the one or more images). For example, in a plurality of OCT images, the background noise may include low light areas, or healthy portions of an artery. However, objects of interest which appear in relatively few images (e.g., a TCFA) may become more prominent and therefore may be more easily detectable by object detection algorithms. The output of the subtract mean function  304  may then be passed to a rescale function  306 , where a standard deviation of the mean-subtracted pixel intensity values may be determined, and then used to rescale the mean-subtracted pixel intensity values (e.g., each of the mean-subtracted pixel intensity values may be divided by the respective standard deviation). The rescale function  306  may then output the preprocessed OCT imaging data output, which may be passed to another module (e.g., the deep learning module  210  as described below with reference to  FIG. 2 ). 
     Referring again to  FIG. 2 , preprocessed OCT imaging data from the preprocessing module  208  may be passed to the deep learning module  210  and from there to the bounding boxes module  212 . The deep learning module  210  and the bounding boxes module  212  may interface with a neural network, such as a convolutional neural network, an exemplary embodiment of which is described below with reference to  FIG. 4 . The neural network may include a two-stage object detection algorithm, which may identify one or more image features (e.g., at the deep learning module  210 ) and then generate one or more bounding boxes based on the one or more identified image features (e.g., at the bounding boxes module  212 ). For example, the one or more image features may correspond to a TCFA depicted by a given OCT image, and a bounding box may be generated to circumscribe the TCFA. In some examples, the deep learning module  210  may further include functionalities to enforce rotational invariance of the OCT imaging data and to provide resilience to noise in the image without a need for data augmentation, and to convert preprocessed OCT imaging data from an image space to a coordinate space for postprocessing. Each of the three aforementioned functionalities are known generally in the art, and specific algorithmic details are therefore omitted here for brevity. Further, as used herein, “bounding box” may refer to any two-dimensional shape which circumscribes a given region of an OCT image, and is not limited to boxes, rectangles, squares, etc. except where otherwise indicated. 
     After the one or more bounding boxes have been generated based on the identified image features, output from the bounding boxes module  212  may be passed to the postprocessing module  214 , whereby continuity may be enforced in the set of OCT image slices. Because the set of OCT image slices is sequentially ordered, a given TCFA will appear in a sequential subset of images. As such, the postprocessing module  214  may correct for spurious or medically less important results. For example, the postprocessing module  214  may base continuity enforcement on a medically relevant TCFA persisting in four to six sequential OCT image slices. In some examples, the four to six sequential OCT image slices may spatially correspond to ˜1 mm of an imaged artery. 
     As an example, the postprocessing module  214  may identify a first series of OCT image slices, the first series of OCT image slices including at least five sequential OCT image slices depicting the TCFA. If a TCFA is instead identified in less than five sequential OCT image slices, the postprocessing module  214  may indicate that no TCFA is present in these OCT image slices, as the TCFA may be spuriously identified, or may be small enough that the TCFA may heal on its own (e.g., without medical intervention). 
     As another example, the postprocessing module  214  may identify a second series of OCT image slices, the second series of OCT image slices including at least five sequential OCT images, wherein at least a first OCT image slice and a last OCT image slice in the second series is identified as including the TCFA, and only one remaining OCT image in between the first OCT image slice and the last OCT image slice is identified as not including the TCFA. The postprocessing module  214  may enforce continuity by indicating the TCFA in each of the OCT image slices in the second series, including the one remaining OCT image slice. 
     As yet another example, the postprocessing module  214  may identify a third series of OCT image slices, the third series of OCT image slices including a sequential ordering of a first OCT image slice, a second OCT image slice, and a third OCT image slice, where the second OCT image slice is identified as including the TCFA, and the first OCT image slice and the third OCT image slice are identified as not including the TCFA. The postprocessing module  214  may enforce continuity by indicating no TCFA in the second OCT image slice, as the initially identified TCFA in the second OCT image slice may be interpreted by the postprocessing module  214  as spuriously detected by the neural network. 
     After continuity has been enforced by the postprocessing module  214 , the OCT image slices output to the TCFA coordinates module  216 . The TCFA coordinates module  216  may be operable to generate start and end coordinates for the TCFA based on the bounding box generated to circumscribe the TCFA. In some examples, the start and end coordinates may be polar spatial coordinates. Visual indicators may be respectively generated on the start and end coordinates of each OCT image slice determined to depict the TCFA. After being output from the TCFA detection library as the processed OCT imaging data including the TCFA region  218 , a subset of the OCT image slices depicting the TCFA and including the visual indicators may be displayed at a display area (e.g.,  114 ) of a display device (e.g.,  112 ) to a medical professional (e.g., an operator of the system  100 ). 
     Referring now to  FIG. 4 , a schematic diagram  400  of an example neural network  402  used for object detection and identification in image inputs (e.g., detection and identification of TCFAs in OCT image slices) and generating bounding boxes therefor is depicted. The neural network  402  may be included in a controller of an imaging system (e.g., controller  102  of system  100  of  FIG. 1 ) and/or in a system in electronic communication with the controller of the imaging system (or receiving data from the controller of the imaging system). The neural network  402  may be a convolutional neural network  402 . Convolutional neural networks are a class of biologically inspired deep neural networks that are powerful in image processing tasks. In particular, convolutional neural networks are modeled after the visual system of the brain. Unlike a “traditional” neural network, convolutional neural networks consist of layers organized in three dimensions and neurons in one layer are connected to only a subset of neurons in the next layer (instead of connecting to all neurons, such as in densely connected layers). 
     As shown in  FIG. 4 , the convolutional neural network  402  may consist of layers of computational units that process visual information hierarchically in a feed-forward manner. The output of each layer may include a plurality of feature maps  404  which may be understood as differently filtered versions of an input image  410 . For example, the convolutional neural network  402  may include a plurality of convolutional layers  406  and pooling layers  408 . Though the convolutional layers  406  and pooling layers  408  are shown in an alternating pattern in  FIG. 4 , in some embodiments, there may be more or less convolutional layers and/or more or less pooling layers and the number of convolutional layers and pooling layers may not be equal and may not be in the alternating pattern. The input image  410  (e.g., a preprocessed OCT image slice) may be input into the convolutional neural network  402 . The input image  410 , and each image of the feature maps  404 , may be represented as a matrix of pixel intensity values. The matrices of pixel intensity values may be understood as the data which may be used by the convolutional neural network  402 . Though a single input image  410  is shown in  FIG. 4 , as described herein, a plurality of sequential input images may be input into the convolutional neural network  402 . 
     Convolution may occur at each of the convolutional layers  406 . Convolution may be performed in order to extract features from the input image  410  (or the feature maps  404  in higher layers further along in the processing hierarchy). Convolution preserves the spatial relationship between pixels by mapping image features from a portion of a first layer to a portion of a second layer, using learning filters including a plurality of weights. Each convolutional layer  406  may include a collection of image filters, each of which extracts a certain feature from the given input image (e.g.,  404 ,  410 ). The output of each convolutional layer  406  may include a plurality of feature maps  404 , each being a differently filtered version of the input image. In some examples, there may be one resulting feature map  404  per applied filter. 
     Pooling (e.g., spatial pooling, which may be max pooling in one example) may occur at each of the pooling layers  408 . Pooling may be performed in order to reduce a dimensionality (e.g., size) of each feature map  404  while retaining or increasing certainty of feature identification. By pooling, a number of parameters and computations in the neural network  402  may be reduced, thereby controlling for overfitting, and a certainty of feature identification may be increased. 
     As shown in  FIG. 4 , following the first convolution, three feature maps  404  may be produced (however, it should be noted that this number may be representative and there may be greater than three feature maps in the first convolutional layer  406 ). Following the first pooling operation, the size of each feature map  404  may be reduced, though the number of feature maps  404  may be preserved. Then, during the second convolution, a larger number of filters may be applied and the output may be a correspondingly greater number of feature maps  404  in the second convolutional layer  406 . Later layers along the processing hierarchy, shown by directional arrow  412 , may be referred to as “higher” layers. The first few layers of the processing hierarchy may detect larger features while the later (higher) layers may pick up finer details and organize such details into more complex features. In some embodiments, a final output layer  414  may be fully connected (e.g., all neurons in the final output layer  414  may be connected to all neurons in the previous layer). However, in other embodiments, final output layer  414  may not be fully connected. 
     By training the convolutional neural network  402  on object recognition, the convolutional neural network  402  may develop a representation of the input image  410  which makes object information increasingly explicit along the processing hierarchy (as shown by arrow  412 ). Thus, along the processing hierarchy of the convolutional neural network  402 , the input image  410  may be transformed into representations which increasingly emphasize the actual content of the input image  410  compared to its detailed pixel intensity values. Images reconstructed from the feature maps  404  of the higher layers in the convolutional neural network  402  may capture the high-level content in terms of objects and their arrangement in the input image  410  but may not constrain exact pixel intensity values of the content reconstructions. In contrast, image reconstructions from the lower layers may reproduce the exact pixel intensity values of the original input image  410 . Thus, feature responses in the higher (e.g., deeper) layers of the convolutional neural network  402  may be referred to as the content representation. 
     In an exemplary embodiment, the convolutional neural network  402  may be employed to identify one or more image features corresponding to a TCFA in an imaged artery. The input image  410  may therefore include one or more preprocessed OCT image slices depicting the artery having the TCFA. The one or more preprocessed OCT image slices may be scaled by a preprocessing module (e.g.,  208 ) such that the convolutional neural network  402  may be more easily able to distinguish subtle variations between regions of the images and thereby identify the one or more image features. Upon identification of the one or more image features (e.g., the high-level content) in a given OCT image slice, outputted objects may be used as inputs for segmentation of the OCT image slice into one or more regions. As will be further discussed below with reference to  FIG. 5 , the one or more regions may be respectively bound by one or more bounding boxes generated based on the one or more image features, wherein at least one of the one or more bounding boxes circumscribes the depicted TCFA. As such, the convolutional neural network  402  may be considered a two-stage object detection algorithm which identifies one or more TCFAs depicted by an inputted OCT image slice and then generates one or more bounding boxes based on the one or more identified TCFAs. 
     Referring now to  FIG. 5 , a schematic diagram  500  of an example process  504  for generating bounding boxes and TCFA coordinates on an OCT image using a neural network is depicted. The neural network may be the convolutional neural network  402  of  FIG. 4 , for example. A first image  502  may form an input for the example process  504 , which may be processed to obtain a second image  506 . Each of the first image  502  and the processed second image  506  may be an OCT image slice of an artery having a TCFA. Further, each of the first image  502  and the second image  506  may depict the artery in polar spatial coordinates (as indicated by the z- and ( ) axes), such that a wall of the artery is depicted as “unrolled” in each image (as opposed to circular, such as when the artery is depicted in Cartesian spatial coordinates). Depicting OCT image slices in polar coordinates may help medical professionals viewing the OCT image slices to make diagnoses. 
     The second image  506  may include one or more rectangular bounding boxes generated via the example process  504  corresponding to one or more regions of the second image  506 . In general, the neural network employed by the example process  504  may divide a given image into three parts: one or more healthy portions of the artery, one or more unhealthy portions of the artery (e.g., TCFAs), and a low-light background. As shown in  FIG. 5 , a first bounding box  508  may circumscribe a first region depicting the TCFA, such that the first region may bounded by the first bounding box  508 . In some examples not depicted by  FIG. 5 , a plurality of first bounding boxes  508  may be generated, each of which may circumscribe an additional first region depicting an additional TCFA. As further shown by  FIG. 5 , one or more second bounding boxes  510  may circumscribe one or more second regions respectively depicting one or more healthy portions of the artery, such that each second region may be bounded by one of the one or more second bounding boxes  510 . Further, one or more third bounding boxes  512  may circumscribe one or more third regions respectively corresponding to the low-light, or black, background not containing visually discernable structures, such that each third region may be bounded by one of the one or more third bounding boxes  512 . The one or more bounding boxes may not include the entire second image  506 , as at least some of the second image  506  may not be cleanly categorized as a healthy portion of the artery, an unhealthy portion of the artery, or low-light background. As such, the one or more bounding boxes may not extend an entire length of the second image  506  along the z-axis. 
     The example process  504  may further determine a start coordinate and an end coordinate for the second image  506 . Specifically, the example process  504  may determine coordinates along two opposite sides of the first bounding box  508  parallel to the z-axis. Thus, the two opposite sides of the first bounding box  508  may be approximately perpendicular to a length of the depicted TCFA in polar coordinates. As used with reference to  FIG. 5 , “approximately” may refer to within 10° of orthogonality between a line approximating the length of the depicted TCFA in polar coordinates and the two sides of the first bounding box  508  parallel to the z-axis. A first dashed line  514  and a second dashed line  516 , each parallel to the z-axis, are superimposed on the second image  506  in  FIG. 5  to indicate the start and end coordinates, respectively. In this way, the neural network of the present disclosure may segment an OCT image slice into a plurality of regions, the plurality of regions including a first region depicting a TCFA, and may further determine start and end coordinates for the TCFA based on the first region. 
     Referring now to  FIGS. 6A-8 , a training dataset for the neural network (e.g., the convolutional neural network  402  of  FIG. 4 ) may be developed by first determining an overlap of a plurality of provisional TCFA regions in OCT images of an initial dataset. Two example processes for determining this overlap are respectively depicted by  FIGS. 6A and 6B . The plurality of provisional TCFA regions may be respectively selected by a plurality of medical professionals, for example. Two plots depict features of an example initial dataset of OCT images including TCFA regions selected by medical professionals are respectively depicted by  FIGS. 7 and 8 . As used herein, “provisional TCFA regions” may refer to TCFA regions present in the initial dataset prior to generation of the overlap regions for the training dataset; thus, the plurality of provisional TCFA regions may not directly be used in training of the neural network. 
     In some examples, the neural network may have difficulty visually recognizing a TCFA region without feedback from a plurality of medical professionals. Specifically, TCFA regions may be particularly difficult to recognize visually without experienced guidance to indicate which portions of a given image correspond to TCFA regions. However, the neural network may not be adequately trained based on feedback from one medical professional alone, as any given medical professional may bias results of the training. Thus, the neural network may be trained using a training dataset of images including composite overlapped regions corresponding to agreement by a plurality of medical professionals. These composite overlapped regions may serve as the “ground truth” TCFA regions during training of the neural network. Further, the neural network may be trained to assign higher weights to overlapped regions which correspond to agreement by a greater number of medical professionals. In this way, the neural network may aggregate the combined experience of the plurality of medical professionals and learn which image features are typically agreed upon as corresponding to TCFA regions. 
     Referring now to  FIG. 6A , a schematic diagram  600  of an overlap between two pairs of vertical lines superimposed on an OCT image  602  is depicted. The OCT image  602  may depict an artery having a TCFA in polar spatial coordinates (as indicated by the z- and θ-axes). A first long-dashed line  604  and a second long-dashed line  606  may indicate where a first medical professional has identified a first provisional TCFA region. A first short-dashed line  608  and a second short-dashed line  610  may indicate where a second medical professional has identified a second provisional TCFA region. Further, each of the lines  604 ,  606 ,  608 ,  610  may be parallel to the z-axis. 
     The pair of long-dashed lines  604 ,  606  and the pair of short-dashed lines  608 ,  610  may respectively define a first length  612  and a second length  614 . The first length  612  may correspond to the first provisional TCFA region and the second length  614  may correspond to the second provisional TCFA region. The overlap between the first and second provisional TCFA regions may be determined as a ratio of the intersection  616  of the first length  612  and the second length  614  to the union  618  of the first length  612  and the second length  614 . As such, the overlap exemplified by the schematic diagram  600  may be regarded as a one-dimensional intersection over union (IoU) metric. As a value of the overlap grows higher, a training algorithm may assign a greater confidence (e.g., weight) to the overlap as corresponding to a ground truth TCFA region. 
     Referring now to  FIG. 6B , a schematic diagram  650  of an overlap between two boxes superimposed on an OCT image  652  is depicted. The OCT image  652  may depict an artery having a TCFA in polar spatial coordinates (as indicated by the z- and θ-axes). A first box  654  may indicate where a first medical professional has identified a first provisional TCFA region. A second box  656  may indicate where a second medical professional has identified a second provisional TCFA region. Each of the first box  654  and the second box  656  may be respectively oriented such that two sides are parallel to the z-axis and two sides are parallel to the θ-axis. 
     The first box  654  and the second box  656  may respectively define a first area  658  and a second area  660 . The first area  658  may correspond to the first provisional TCFA region and the second area  660  may correspond to the second provisional TCFA region. The overlap between the first and second provisional TCFA regions may be determined as a ratio of the intersection  662  (encompassed by dashed lines in  FIG. 6B ) of the first area  658  and the second area  660  to the union  664  (encompassed by solid lines in  FIG. 6B ) of the first area  658  and the second area  660 . As a value of the overlap grows higher, a training algorithm may assign a greater confidence (e.g., weight) to the overlap as corresponding to a ground truth TCFA region. 
     Referring now to  FIG. 7 , an example plot  700  is depicted, the example plot  700  showing an agreement between medical professionals (e.g., doctors) in identification of OCT images depicting TCFAs. Plotted along an ordinate is a total number of positive images, where a “positive image” may refer to an OCT image indicated by a doctor as depicting a TCFA (as opposed to a “negative image,” which may refer to an OCT image indicated by a doctor as not depicting a TCFA). Plotted along an abscissa is a number of doctors that agree that a particular image is a positive image. 
     The dataset depicted by the example plot  700  includes 5684 OCT images corresponding to OCT imaging data for 21 patients, where 1000 OCT images are identified as positive images by at least one doctor in a group of six doctors. As shown by the example plot  700 , of the 1000 positive images, at least two doctors identify a TCFA in 745 images, at least three doctors identify a TCFA in 706 images, at least four doctors identify a TCFA in 633 images, at least five doctors identify a TCFA in 525 images, and all six doctors identify a TCFA in 197 images. In one example, the ground truth for the training dataset may be selected as the 197 images of which all six doctors agree depict a TCFA. 
     Referring now to  FIG. 8 , an example plot  800  is depicted, the example plot  800  showing an agreement between medical professionals (e.g., doctors) in identification of provisional TCFA regions in OCT images. Plotted along an abscissa is a percentage of overlap between provisional TCFA regions in a given set of OCT images, where the percentage of overlap may be determined via one of the processes described with reference to  FIGS. 6A and 6B . Plotted along an ordinate is a percentage of images in a given set of OCT images which have at least the percentage of overlap plotted by the abscissa. The darker bars plot results from the set of 745 images in which at least two doctors identified a TCFA, as described above with reference to  FIG. 7 . The lighter bars plot results from the set of 633 images in which at least four doctors identified a TCFA, as described above with reference to  FIG. 7 . 
     The dataset depicted by the example plot  800  may be the same dataset as that depicted by the example plot  700 . As shown by the example plot  800 , at least 10% overlap between provisional TCFA regions is present in 92% of the 745 images indicated as positive by at least two doctors, at least 30% overlap is present in 58% of these images, and at least 50% overlap is present in at least 30% of these images. Further, at least 30% overlap between provisional TCFA regions is present in 55.6% of the images indicated as positive by at last four doctors and at least 50% overlap is present in 27.6% of these images. In one example, the ground truth for the training dataset may be selected as the images agreed on by at least four doctors which exhibit at least 50% overlap between provisional TCFA regions. 
     Referring now to  FIG. 9 , a flow chart of a method  900  for identifying a TCFA in an OCT image and displaying the OCT image is depicted. Method  900  will be described with reference to the embodiments provided hereinabove, though it may be understood that similar methods may be applied to other systems without departing from the scope of this disclosure. For example, method  900  may be executed by the system  100  of  FIG. 1 . Specifically, method  900  may be carried out via the controller  102 , and may be stored as executable instructions at a non-transitory storage medium, such as the memory  104  or the storage device  108 . 
     At  902 , method  900  may include initiating an OCT scan. The OCT scan may be performed with a scanner (e.g.,  106 ) which emits, and collects echoes from, low-coherence light to image one or more anatomical structures of a subject. In some examples, the low-coherence light may be echoed from an artery having a plaque, such as a TCFA. 
     At  904 , method  900  may include acquiring OCT imaging data. The low-coherence light collected by the scanner (e.g.,  106 ) may correspond to OCT imaging data (e.g., 3D volumetric imaging data) depicting the artery having the TCFA. The OCT imaging data may be received by a controller (e.g.,  102 ) may be communicably coupled to the scanner. 
     At  906 , method  900  may include generating one or more OCT images (e.g., 2D image slices) from the OCT imaging data. The one or more OCT images may include a sequentially ordered series, or set, of OCT images. In some examples, the set of OCT images may include ˜270 image slices of a single OCT scan for one patient. In some examples, a single OCT image may be generated and subsequently processed at any one time. In other examples, each OCT image in the set of OCT images may be generated and subsequently processed one at a time. In other examples, each OCT image in the set of OCT images may be subsequently processed in parallel. Within the set of OCT images, a subset of OCT images may depict the plaque being imaged. 
     At  908 , method  900  may include preprocessing the one or more OCT images for processing by a neural network. The preprocessing may occur via a preprocessing module, such as preprocessing module  208 , as described above with reference to  FIGS. 2 and 3 . The preprocessing may include preparing data encoding the one or more OCT images for the neural network. For example, the one or more OCT images may be averaged and normalized so that the neural network may distinguish between subtle regions of each OCT image and extract one or more TCFA regions therein. 
     At  910 , method  900  may include processing the one or more preprocessed OCT images via the neural network. The neural network may be configured to separate visual characteristics from content of an image. As such, the neural network may be a convolutional neural network, such as the convolutional neural network  402  of  FIG. 4 . The neural network may include a trained two-stage object detection algorithm, which may identify one or more image features and then generate one or more bounding boxes based on the one or more image features. 
     Specifically, at  912 , the neural network may identify one or more image features in one or more OCT images (e.g., the one or more preprocessed OCT images). The identifying may occur via a deep learning module, such as deep learning module  210 , as described above with reference to  FIG. 2 . In some examples, the one or more image features may include one or more TCFA features (e.g., image features corresponding to one or more TCFAs). However, in some examples, some of the OCT images may not depict one or more TCFAs. For example, the neural network may identify one or more image features indicating a TCFA in a subset of OCT images, where all other remaining OCT images (e.g., apart from the subset) are identified as including no TCFA. In some examples, the neural network may identify one or more additional image features depicting one or more additional TCFAs in the subset of OCT images. In one example, the neural network may identify one or more additional image features depicting one or more additional TCFAs in a single OCT image. In this way, the neural network may identify one or more particular TCFAs in one or more particular OCT images after being trained to generally identify TCFAs in OCT images. In other examples, none of the OCT images may depict the TCFA, and the neural network may identify no TCFA in any of the OCT images. 
     Then, at  914 , one or more bounding boxes may be generated based on the one or more image features. The generating may occur via a bounding boxes module, such as bounding boxes module  212 , as described above with reference to  FIG. 2 . The neural network may segment each of the one or more OCT images into one or more regions based on the one or more image features. In examples wherein a TCFA is identified in the one or more OCT images (e.g., the subset of OCT images depicting the TCFA), the one or more regions may correspondingly include one or more first regions each depicting the TCFA. In examples wherein one or more additional TCFAs are identified in the one or more OCT images (e.g., the subset of OCT images depicting the TCFA), the one or more regions may include one or more second regions depicting the one or more additional TCFAs. In examples wherein one or more healthy portions of the artery are identified in the one or more OCT images, the one or more regions may correspondingly include one or more third regions depicting the one or more healthy portions of the artery. In examples wherein one or more low-light backgrounds are identified in the one or more OCT images, the one or more regions may correspondingly include one or more fourth regions depicting the one or more low-light backgrounds. Once the one or more OCT images is segmented into the one or more regions, the one or more bounding boxes may be generated based on the one or more regions. As such, the one or more regions may be respectively bound by the one or more bounding boxes. Thus, for example, a bounding box may be generated based on one of the one or more first regions such that the bounding box may circumscribe the TCFA indicated by the one or more TCFA features. In this way, the neural network may identify a TCFA and then localize the TCFA within an OCT image by generating a bounding box to circumscribe the TCFA. 
     At  916 , method  900  may include determining whether the neural network has identified one or more TCFAs in the one or more OCT images. If no TCFA is identified by the neural network (e.g., if no image feature indicates a TCFA), method  900  may proceed to  918  to display a notification at a display area (e.g.,  114 ) of a display device (e.g.,  112 ). The notification may indicate to an operator of the system (e.g.,  100 ) that no TCFA was identified during the OCT scan. In some examples, the display device may further be operable to display the one or more OCT images processed by the neural network at the display area. Method  900  may then end. 
     If one or more TCFAs are identified by the neural network (e.g., if one or more image features indicate a TCFA), method  900  may proceed to  920  to enforce continuity among the subset of processed OCT images (e.g., the OCT images identified by the neural network as including a TCFA) based on the one or more bounding boxes. The enforcing may occur via a postprocessing module, such as postprocessing module  214 , as described above with reference to  FIG. 2 . As such, enforcing continuity may include one or more of the example operations described above with reference to  FIG. 2 , which will not be repeated here for brevity. That is, enforcing continuity may be based upon a medically relevant TCFA persisting in four to six sequential OCT image slices. Thus, at  920 , method  900  may correct for spurious TCFAs (e.g., TCFAs erroneously identified by the neural network) or medically less important TCFAs (e.g., TCFAs which will heal on their own). 
     At  922 , method  900  may include determining TCFA start and end coordinates in the subset of processed OCT images (e.g., the OCT images identified by the neural network as including a TCFA) based on the one or more bounding boxes. The determining may occur via a TCFA coordinates module, such as TCFA coordinates module  216 , as described above with reference to  FIG. 2 . For a given TCFA in a given OCT image, the TCFA start and end coordinates may be determined based on a given bounding box circumscribing the TCFA (and thus, based on a given first region identified by the neural network as depicting the TCFA). Further, as the OCT images may be generated and processed in polar spatial coordinates, the TCFA start and end coordinates may correspondingly be polar spatial coordinates. For example, the TCFA start and end coordinates may be respectively determined as corresponding to two opposite sides of the given (rectangular) bounding box, where the two opposite sides of the given bounding box may be perpendicular, or approximately perpendicular, to a length of the given TCFA in polar coordinates. 
     At  924 , method  900  may include generating visual indicators at each of the determined TCFA start and end coordinates on each of the subset of processed OCT images. The generating may also occur via the TCFA coordinates module (e.g.,  216 ). The visual indicators may assist a medical professional (e.g., an operator of the system  100 ) in diagnosing a TCFA by indicating to the medical professional a location of the TCFA within the artery. 
     At  926 , method  900  may include displaying the subset of processed OCT images with the generated visual indicators at the display area (e.g.,  114 ) of the display device (e.g.,  112 ). Thus, an operator of the system (e.g.,  100 ) may be automatically presented with a clearly indicated depictions of any identified TCFAs in the artery of a subject. In some examples, the display device may further be operable to display one or more remaining processed OCT images (e.g., the OCT images indicated by the neural network as not depicting a TCFA) at the display area. Method  900  may then end. 
     Referring now to  FIG. 10 , a flow chart of a first exemplary method  1000  for training a neural network to identify TCFAs is depicted. Method  1000  will be described with reference to the embodiments provided hereinabove, though it may be understood that similar methods may be applied to other systems without departing from the scope of this disclosure. For example, method  1000  may be executed by the system  100  of  FIG. 1  to train a neural network, such as the convolutional neural network  402  of  FIG. 4 . Specifically, method  1000  may be carried out via the controller  102 , and may be stored as executable instructions at a non-transitory storage medium, such as the memory  104  or the storage device  108 . 
     At  1002 , method  1000  may include acquiring a first dataset of first OCT images (e.g., 2D image slices) for training the neural network, each of the first OCT images including one or more provisional TCFA regions. Each of the first OCT images may depict an artery of a subject, the artery having one or more TCFAs. Each TCFA depicted by each of the first OCT images may correspond to at least one of the one or more provisional TCFA regions. Each of the one or more provisional TCFA regions may be respectively received from one or more medical professionals. As such, the first dataset may be the dataset described with reference to  FIGS. 7 and 8 , for example. Thus, each provisional TCFA region may be judged by one of the one or more medical professionals to include a TCFA. 
     At  1004 , method  1000  may include selecting one of the one or more first OCT images. In examples wherein the one or more first OCT images are in a sequential order, the selecting may also be performed in the sequential order. 
     At  1006 , method  1000  may include determining an agreement of the one or more provisional TCFA regions in the selected first OCT image. Each of the one or more provisional TCFA regions may be partitioned into columns, each of which may be evaluated for a degree to which the one or more medical professionals who selected the one or more provisional TCFA regions agree with one another. In some examples, the agreement of the one or more medical professionals for each of the columns may be determined based on an amount of overlap. In such examples, the amount of overlap may be a percentage of overlap determined via one of the processes described above with reference to  FIGS. 6A and 6B , respectively. The amount of overlap may represent a degree to which the one or more medical professionals who selected the one or more provisional TCFA regions agree with one another. 
     At  1008 , method  1000  may include determining whether the agreement in the selected first OCT image is greater than an agreement threshold. The agreement threshold may be optimized to filter out TCFA regions which are less visually identifiable from training of the neural network. In some examples, the agreement threshold may be based on a majority agreement as to whether a respective TCFA is depicted by the columns of the one or more provisional TCFA regions (e.g., whether a majority of columns is determined to depict a portion of a TCFA). 
     If the agreement is greater than the agreement threshold, method  1000  may proceed to  1010  to generate a second OCT image indicating the agreement. The second OCT image may be generated from the same OCT imaging data as the selected first OCT image, and may only differ in one or more generated labels annotating a TCFA and/or a generated box indicating the amount of overlap; however, the one or more provisional TCFA regions may no longer be indicated in the second OCT image. 
     At  1012 , method  1000  may include adding the generated second OCT image to a second dataset. The second dataset may be utilized to effectively train the neural network, as the second dataset will be less biased by an individual judgment of any one of the one or more medical professionals. 
     Once the second OCT image has been added to the second dataset, or if the agreement is less than the agreement threshold, method  1000  may proceed to  1014  to determine whether further first OCT images are in the first dataset. If further first OCT images are in the first dataset (e.g., for which the amount of overlap has not yet been determined), method  1000  may return to  1004  to select another first OCT image. 
     If an amount of overlap has been determined for each first OCT image in the first dataset, method  1000  may proceed to  1016  to train the neural network based on the second dataset. As an example, the neural network may be trained as at  1114  of method  1100 , as described below with reference to  FIG. 11 . In this way, training of the neural network may be based upon the acquired first dataset of first OCT images which indicate medical opinions of one or more medical professionals as to a presence of a given TCFA. As such, the neural network may be trained to identify a TCFA in OCT images by identifying one or more TCFA features in the OCT images and then generating one or more bounding boxes in the OCT images for the TCFA based on the one or more TCFA features. Method  1000  may then end. 
     Referring now to  FIG. 11 , a flow chart of a second exemplary method  1100  for training a neural network to identify TCFAs is depicted. Method  1100  will be described with reference to the embodiments provided hereinabove, though it may be understood that similar methods may be applied to other systems without departing from the scope of this disclosure. For example, method  1100  may be executed by the system  100  of  FIG. 1  to train a neural network, such as the convolutional neural network  402  of  FIG. 4 . Specifically, method  1100  may be carried out via the controller  102 , and may be stored as executable instructions at a non-transitory storage medium, such as the memory  104  or the storage device  108 . 
     At  1102 , method  1100  may include acquiring a dataset of OCT images (e.g., 2D image slices) for training the neural network, each of the OCT images depicting an artery of a subject. The dataset of OCT images may include both positive and negative images, where the positive and negative images have been generated and labeled during a data preparation process. As such, in the positive images, the depicted artery may have one or more TCFAs, and in the negative images, the depicted artery may have no TCFA. Each TCFA depicted by each of the positive images may correspond to at least one of one or more provisional TCFA regions labeled on the positive image. Each of the one or more provisional TCFA regions may be respectively received from one or more medical professionals. As such, the positive images may correspond to the dataset described with reference to  FIGS. 7 and 8 , for example. Thus, each provisional TCFA region may be judged by one of the one or more medical professionals to include a TCFA. 
     At  1104 , method  1100  may include determining whether an image batch balancing routine is requested. In general, batch balancing may be implemented to more uniformly capture features of dominant and minority classes. As an example, the positive images may be considered the dominant class and the negative images may be considered the minority class. If the acquired dataset is imbalanced, features of the dominant class may be overrepresented, for example. In the case of the method of the present disclosure, such imbalance may result in spurious predictions of TCFA regions where none are present in a given OCT image. 
     Provided within method  1100  are two exemplary instances where a batch balancing routine may be requested: during preprocessing of the OCT images for neural network training (e.g., at  1104 ) and during neural network training itself (e.g., at  1118 ). If the image batch balancing routine (e.g., during preprocessing) is requested, method  1100  may proceed to  1106  to set a first balancing ratio. The first balancing ratio may be a desired ratio of dominant class members to minority class members, that is, of positive images to negative images, for data preprocessing. The first balancing ratio may be predetermined to minimize spurious predictions of TCFA regions (or to minimize instances of missing TCFA regions). In one example, the first balancing ratio may be 90:10. In another example, the first balancing ratio may be 80:20. In yet another example, the first balancing ratio may be 70:30. In yet another example, the first balancing ratio may be 60:40. In yet another example, the first balancing ratio may be 50:50. 
     At  1108 , method  1100  may include selecting a first batch, or image batch, of OCT images based on the first balancing ratio. For example, if the dataset includes at least 100 OCT images, and the first balancing ratio is set at 90:10, then method  1100  may include selecting 90 positive images and 10 negative images for neural network training. The selection may be performed arbitrarily, so as not to bias neural network training. 
     Returning to  1104 , if the image batch balancing routine is not requested, method  1100  may proceed to  1110  to select the first batch of OCT images. For example, if the dataset includes at least a first threshold number of OCT images, then method  1100  may include selecting the first threshold number of OCT images. The selection may be performed arbitrarily, so as not to bias neural network training. Alternatively, in some examples, the entire dataset may constitute the first batch of OCT images, and no selection process is employed. 
     Once the first batch of OCT images has been selected (e.g., at  1108  or at  1110 ), method  1100  may proceed to  1112  to perform data augmentation on the first batch of OCT images. In general, data augmentation routines are employed to add features to a dataset (e.g., in preparation for neural network training) without collecting new data. In object detection applications, simple image processing techniques, such as transformations, rotations, reflections, and color alterations may be applied to images in the dataset to improve identification of desired objects. As such, data augmentation may provide an increased number of OCT images in the dataset without further input from medical professionals, further OCT scans, etc. It will be appreciated that numerous data augmentation routines are well-known to those skilled in the art and will therefore be referenced only generally herein as relates to the described embodiments. As noted above, in alternative examples, rotational invariance and robustness to noise may be included in some embodiments of the present disclosure, such that no data augmentation is employed. 
     At  1114 , after the first batch of OCT images has been selected and preprocessed, method  1100  may include training the neural network with the first batch of OCT images, where each positive image therein may include indications of one or more provisional TCFA regions. In this way, training of the neural network may be based upon the selected first batch of OCT images which indicate medical opinions of one or more medical professionals as to a presence or an absence of TCFAs. 
     Specifically, at  1116 , method  1100  may include identifying, via the neural network, one or more image features in the first batch of OCT images. The identifying may occur via a deep learning module, such as deep learning module  210 , as described above with reference to  FIG. 2 . In some examples, the one or more image features may include one or more TCFA features (e.g., image features corresponding to one or more TCFAs). As an example, the neural network may identify one or more image features indicating a TCFA in a subset of OCT images, where all other remaining OCT images (e.g., apart from the subset) are identified as including no TCFA. In some examples, the neural network may identify one or more additional image features depicting one or more additional TCFAs in the subset of OCT images. In one example, the neural network may identify one or more additional image features depicting one or more additional TCFAs in a single OCT image. In this way, the neural network may be trained to generally identify TCFAs in OCT images. 
     At  1118 , method  1100  may include determining whether a region of interest batch balancing routine is requested. If the region of interest batch balancing routine is requested, method  1100  may proceed to  1120  to set a second balancing ratio. The second balancing ratio may be a desired ratio of dominant class members to minority class members, that is, of positive images to negative images, for neural network training. The second balancing ratio may be predetermined to minimize spurious characterizations of TCFA regions (or to minimize instances of missing TCFA regions) during bounding box generation. In one example, the second balancing ratio may be 90:10. In another example, the second balancing ratio may be 80:20. In yet another example, the second balancing ratio may be 70:30. In yet another example, the second balancing ratio may be 60:40. In yet another example, the second balancing ratio may be 50:50. 
     At  1122 , method  1100  may include selecting a second batch, or region of interest batch, of OCT images based on the second balancing ratio. For example, if the dataset includes at least 100 OCT images, and the second balancing ratio is set at 90:10, then method  1100  may include selecting 90 positive images and 10 negative images for bounding box generation. The selection may be performed arbitrarily, so as not to bias the bounding box generation routine. 
     Returning to  1118 , if the region of interest batch balancing routine is not requested, method  1100  may proceed to  1124  to select the second batch of OCT images. For example, if the dataset includes at least a second threshold number of OCT images, then method  1100  may include selecting the second threshold number of OCT images. The selection may be performed arbitrarily, so as not to bias bounding box generation. Alternatively, in some examples, the entire first batch may constitute the second batch of OCT images, and no selection process is employed. 
     Once the second batch of OCT images has been selected (e.g., at  1122  or at  1124 ), method  1100  may proceed to  1126  to generate one or more bounding boxes based on the one or more image features identified at  1116  for those OCT images included in the second batch of OCT images. The generating may occur via a bounding boxes module, such as bounding boxes module  212 , as described above with reference to  FIG. 2 . The neural network may segment each of the one or more OCT images into one or more regions based on the one or more image features. In examples wherein a TCFA is identified in the one or more OCT images (e.g., the subset of OCT images depicting the TCFA), the one or more regions may correspondingly include one or more first regions each depicting the TCFA. In examples wherein one or more additional TCFAs are identified in the one or more OCT images (e.g., the subset of OCT images depicting the TCFA), the one or more regions may include one or more second regions depicting the one or more additional TCFAs. In examples wherein one or more healthy portions of the artery are identified in the one or more OCT images, the one or more regions may correspondingly include one or more third regions depicting the one or more healthy portions of the artery. In examples wherein one or more low-light backgrounds are identified in the one or more OCT images, the one or more regions may correspondingly include one or more fourth regions depicting the one or more low-light backgrounds. Once the one or more OCT images is segmented into the one or more regions, the one or more bounding boxes may be generated based on the one or more regions. As such, the one or more regions may be respectively bound by the one or more bounding boxes. Thus, for example, a bounding box may be generated based on one of the one or more first regions such that the bounding box may circumscribe the TCFA indicated by the one or more TCFA features. In this way, the neural network may be trained to identify a TCFA and then localize the TCFA within an OCT image by generating a bounding box to circumscribe the TCFA. Method  1100  may then end. 
     In this way, an OCT imaging system including a neural network is provided, where the neural network may be trained for identifying TCFAs in OCT images. The neural network may include a trained two-stage object detection algorithm for identifying one or more OCT image features corresponding to a TCFA and generating a bounding box containing the TCFA. A technical effect of implementing the trained neural network in the OCT imaging system is that TCFAs may be automatically and accurately identified in OCT images. Such automated identification may result in improved medical diagnoses and treatments. 
     In one example, a method for a trained neural network, the method comprising acquiring an optical coherence tomography (OCT) image slice of an artery, identifying one or more image features of the OCT image slice with the trained neural network, and responsive to the one or more image features indicating a thin-cap fibroatheroma (TCFA), segmenting the OCT image slice into a plurality of regions with the trained neural network, the plurality of regions including a first region depicting the TCFA, and determining start and end coordinates for the TCFA based on the first region. A first example of the method further including wherein the plurality of regions further includes one or more second regions, the one or more second regions respectively depicting one or more healthy portions of the artery. A second example of the method, optionally including the first example of the method, further including wherein the plurality of regions further includes one or more third regions, the one or more third regions respectively depicting one or more additional TCFAs. A third example of the method, optionally including one or more of the first and second examples of the method, further including wherein the OCT image slice is one of a series of OCT image slices, where the series of OCT image slices is sequentially ordered. A fourth example of the method, optionally including one or more of the first through third examples of the method, further comprising acquiring remaining OCT image slices in the series of OCT image slices, identifying, for each of the remaining OCT image slices, one or more additional image features with the trained neural network, and responsive to the one or more additional image features indicating the TCFA in a subset of the remaining OCT image slices, segmenting the subset of the remaining OCT image slices into an additional plurality of regions with the trained neural network, the additional plurality of regions including one or more additional first regions, each of the one or more additional first regions depicting the TCFA, and determining, for each remaining OCT image slice in the subset of the remaining OCT image slices, additional start and end coordinates for the TCFA based on the one or more additional first regions. A fifth example of the method, optionally including one or more of the first through fourth examples of the method, further including wherein the additional plurality of regions further includes one or more third regions, the one or more third regions depicting one or more additional TCFAs. A sixth example of the method, optionally including one or more of the first through fifth examples of the method, further including wherein the first region is bounded by a rectangular bounding box. A seventh example of the method, optionally including one or more of the first through sixth examples of the method, further including wherein identifying the TCFA start and end coordinates includes determining coordinates along each of two opposite sides of the rectangular bounding box, the two opposite sides being perpendicular to a length of the TCFA in polar coordinates. 
     In another example, a method comprises training a neural network to identify a thin-cap fibroatheroma (TCFA) in optical coherence tomography (OCT) image slices, where identifying the TCFA includes identifying TCFA features in the OCT image slices, and generating bounding boxes in the OCT image slices for the TCFA based on the TCFA features, receiving a particular OCT image slice depicting a particular TCFA, and identifying the particular TCFA in the particular OCT image slice using the trained neural network. A first example of the method further including wherein the neural network is a convolutional neural network. A second example of the method, optionally including the first example of the method, further comprising receiving a dataset including training OCT image slices, each of the training OCT image slices including one or more provisional TCFA regions, wherein the neural network is trained based on the received dataset. A third example of the method, optionally including one or more of the first and second examples of the method, further including wherein the one or more provisional TCFA regions are respectively received from one or more medical professionals. 
     In yet another example, a medical imaging system comprises a scanner operable to collect optical coherence tomography (OCT) imaging data of a plaque, a memory storing a trained neural network configured to separate visual characteristics from content of an image, and a processor communicably coupled to the scanner and the memory, wherein the processor is configured to receive the OCT imaging data from the scanner, generate a sequentially ordered set of OCT images from the OCT imaging data, where a subset of the OCT images depicts the plaque, identify, via the trained neural network, the subset of OCT images depicting the plaque, generate, via the trained neural network, a bounding box circumscribing the plaque in each OCT image in the subset of OCT images, and determine, for each OCT image in the subset of OCT images, start and end coordinates for the plaque based on the bounding box. A first example of the medical imaging system further including wherein the OCT imaging data includes 3D volumetric imaging data, and the sequentially ordered set of OCT images includes 2D image slices of the 3D volumetric imaging data. A second example of the medical imaging system, optionally including the first example of the medical imaging system, further including wherein the plaque is a thin-cap fibroatheroma. A third example of the medical imaging system, optionally including one or more of the first and second examples of the medical imaging system, further comprises a display device communicably coupled to the processor, the display device including a display area, wherein the processor is further configured to include, for each OCT image in the subset of OCT images, visual indicators at the start and end coordinates, and display, via the display area of the display device, the subset of OCT images including the visual indicators. A fourth example of the medical imaging system, optionally including one or more of the first through third examples of the medical imaging system, further includes wherein identifying the subset of OCT images depicting the plaque includes identifying a series of OCT images, the series of OCT images including at least five sequential OCT images depicting the plaque, and adding the series of OCT images to the subset of OCT images. A fifth example of the medical imaging system, optionally including one or more of the first through fourth examples of the medical imaging system, further includes wherein the processor is further configured to identify a series of OCT images in the sequentially ordered set of OCT images, the series of OCT images including at least five sequential OCT images, wherein at least a first OCT image and a last OCT image are identified as including the plaque, and only one remaining OCT image is indicated as including no plaque, and indicate the plaque in each OCT image in the series of OCT images. A sixth example of the medical imaging system, optionally including one or more of the first through fifth examples of the medical imaging system, wherein the processor is further configured to identify a series of OCT images in the sequentially ordered set of OCT images, the series of OCT images including a sequential ordering of a first OCT image, a second OCT image, and a third OCT image, where the second OCT image is identified as including the plaque, and the first OCT image and the third OCT image are identified as including no plaque, and indicate no plaque in each OCT image in the series of OCT images. A seventh example of the medical imaging system, optionally including one or more of the first through sixth examples of the medical imaging system, further includes wherein the start and end coordinates are polar coordinates. 
     The following claims particularly point out certain combinations and sub-combinations regarded as novel and non-obvious. These claims may refer to “an” element or “a first” element or the equivalent thereof. Such claims should be understood to include incorporation of one or more such elements, neither requiring nor excluding two or more such elements. Other combinations and sub-combinations of the disclosed features, functions, elements, and/or properties may be claimed through amendment of the present claims or through presentation of new claims in this or a related application. Such claims, whether broader, narrower, equal, or different in scope to the original claims, also are regarded as included within the subject matter of the present disclosure.