Method of determining contrast phase of a computerized tomography image

A computer-implemented method for classifying and presenting a contrast phase (CP) of a contrast enhanced computerized tomography (CECT) scan is provided. The method includes training an artificial intelligence (AI) algorithm utilizing a set of CPs labeled CECT data to associate a set of characteristics of the data with a probability associated with the CP. The method includes receiving a new set of unlabeled CECT data, and applying the AI algorithm to the new unlabeled CECT data to associate a first probability of a first CP and a second probability of a second CP. The method also includes providing a graphical representation including the first probability of the first CP and the second probability of the second CP.

BACKGROUND

The present disclosure relates to computer-implemented methods, systems and computer program products to determine a contrast phase of a contrast-enhanced (CE) computed (or computerized) tomography (CT) image. Artificial intelligence (AI) and deep learning systems may be used to train anomaly detection models related to a CT volume, and then the model is used with new CT volume data to attempt to identify visual anomalies (e.g., a tumor) in the contrast-enhanced computed tomography (CECT) images. The contrast medium used in the CECT scans has different contrast phases (CPs) that occur during different time periods after administration. Certain visual anomalies may appear differently in certain of the different contrast phases. Knowledge or identification of the contrast phase may be helpful to generate more accurate anomaly detection results.

SUMMARY

Embodiments of the present disclosure relate to a computer-implemented method for classifying and presenting a contrast phase (CP) of a contrast enhanced computerized tomography (CECT) scan is provided. The method includes training an artificial intelligence (AI) algorithm utilizing a set of CPs labeled CECT data to associate a set of characteristics of the data with a probability associated with the CP. The method includes receiving a new set of unlabeled CECT data, and applying the AI algorithm to the new unlabeled CECT data to associate a first probability of a first CP and a second probability of a second CP. The method also includes providing a graphical representation including the first probability of the first CP and the second probability of the second CP.

Other embodiments of the present disclosure are directed to a computer system and computer program product for performing the method.

It should be appreciated that elements in the figures are illustrated for simplicity and clarity. Well-understood elements that may be useful or necessary in a commercially feasible embodiment may not be shown for the sake of simplicity and to aid in the understanding of the illustrated embodiments.

DETAILED DESCRIPTION

The present disclosure relates to methods, systems and computer program products to identify a contrast phase of a contrast-enhanced (CE) computed (or computerized) tomography (CT) image. In certain embodiments, artificial intelligence (AI) and deep learning systems are utilized to identify probabilities that a CECT image is in a given contrast phase. In certain embodiments, the determined probabilities are presented to a user in a graphical representation.

In general, a CT scan is a medical imaging procedure that uses computer-processed combinations of many X-ray measurements taken from different angles to produce cross-sectional images of specific areas of a scanned object. In certain applications, contrast agents are used during the CT examinations to highlight specific tissues and parts of the body. Bones can generally be clearly seen on x-ray images without utilizing a contrast agent because of the density difference between the bone and the surrounding tissue. Having sufficient image contrast can aid in perceiving a difference in the density between different areas of a CT image. However, the visualization of certain other organs and soft tissues can be more difficult. Insufficient image contrast can make the identification of a disease or condition more challenging due to the difficulty in differentiating pathological tissues (for example tumors, metastases and abscesses) from normal organ structures and surrounding tissues.

Contrast agents may be used in in contrast-enhanced computed tomography (CECT) studies of various body parts to achieve opacification of a tissue of interest (e.g., kidney CT or liver CT) in relation to the background tissue. Abdominal CECT is the current standard in the assessment of various biological abnormalities (e.g., lesions in a liver). In liver applications, there are different types of lesions which, in general, can be classified as malignant (e.g., hepatocellular carcinoma, cholangiocarcinoma, angiosarcoma, metastasis, and other malignant lesion) or benign (e.g., hemangioma, focal nodular hyperplasia, adenoma, cyst or lipoma, and granuloma). A main purpose of CECT liver scans is to be able to differentiate between different type of lesions, as this analysis may be helpful to guide subsequent interventions. The CECT liver scan may also be used to analyze healthy liver parenchyma at different phases/stages of the lifecycle of the contrast, and then compare these results to actual liver lesions at these different contrast phases. Based on these differences, classification of the lesion may be performed.

In order to accurately classify the type of lesion seen in a CECT liver scan, a multi-phase study may be conducted. In general, certain types of tissues (e.g., a tumor) appear different based on the amount of time that has elapsed after the contrast has been administered to the patient. Other factors (e.g., a metabolic rate or heart rate of a patient) can affect how long each phase of the contrast may last. In certain types of liver CECT studies, an example protocol includes four phases (time periods) of the contrast. A first phase of the contrast may be referred to as the pre-contrast phase (PRE) where contrast has not yet been administered. Therefore, in this first PRE phase, the images correspond to a normal volumetric CT scan (i.e., without the use of a CE agent). A second phase of the contrast may be referred to as the arterial phase (ART). In this second phase, images are acquired soon after administration of the bolus while the contrast is distributed within the liver through the hepatic arteries of the liver. In this second phase, early enhancement of the lesion may be seen if the lesion has arterial circulation. A third phase of the contrast may be referred to as the portal venous phase (VEN), and this phase corresponds to a state when the contrast reaches hepatic portal circulation and is being drained from the liver through the hepatic veins. The portal circulation is a major source of blood supply during this third phase of the contrast. A fourth phase of the contrast may be referred to as the delayed scan phase (DEL), and during this phase a retention of contrast material in, for example, lesions and parenchyma may be seen. It should be appreciated that the present embodiments apply to CECT scans having any suitable number of CPs. For example, other CPs may include early-arterial and nephrogenic CPs.

Although the example of the application of CECT scans to a liver has been discussed, it should be appreciated that other medical imaging applications are applicable to the present embodiments. For example, CECT may be used to detect a pulmonary embolism. In this case, data is acquired while first-pass contrast (FP) has not cleared the lung. This is done to visualize the blockage of blood supply due to a blood clot or another cause. Other applications of CECT scans may be used to analyze other organs or biological abnormalities. In general, the present embodiments may be applied to any situation where contrast is injected into a patient and then distributed throughout the body using the blood supply. As discussed above, during the distribution of the contrast, certain phases can be identified (e.g., PRE, ART, VEN and DEL for the example of the liver CECT).

In the present embodiments, neural networks, artificial intelligence (AI), and other deep learning systems may be utilized to aid in automated determination of a contrast phase during a CECT scan. In general, AI can be applied to medical imaging applications, and includes the application of certain algorithms to certain medical images (e.g., CT volumes) to identify certain visual features. However, as discussed above, when utilizing CECT, different types of tissues can appear with different levels of image contrast depending on the phase of the contrast. Thus, an AI model developed for a PRE phase of the contrast may not work well when analyzing images in the ART, VEN or DEL phases of the contrast. That is, multiple AI models may be needed to accurately and consistently identify abnormalities for different phases of the contrast. For example, the contrast may need to be in the lung vasculature to detect pulmonary edema.

The information regarding what contrast phase is present for a given medical image may or may not be in the Digital Imaging and Communications in Medicine (DICOM) metadata that is associated with the CECT volume (or images of the volume). Even if this information is available, it may not be reliable. In related techniques that do not include an AI-based CP classifier model and that do not include DICOM metadata regarding the CP of the CT volume images, it may be necessary for a user (e.g., a physician) to manually enter their subjective determination of the contrast phases.

When developing AI solutions, it may be helpful to know what phase of the contrast the image is in to be able to apply appropriate AI processing (e.g., PRE scan will not work well for classification of liver lesions or detection abnormalities as dissection, etc.). In another example, the VEN phase may not be optimal for detecting PE. The present embodiments provide systems and methods to enable automatic determination of the likelihood that a particular CECT image is in particular contrast phase, and then display these results to a user in an intuitive and easy to comprehend manner.

In the present embodiments, after a CP classification model has been previously trained, a CT volume having an unknown contrast phase status is analyzed by, for example, convolutional neural networks and probability scores are determined for each possible phase based on the CP classification model. The probability indicates a likelihood of which contrast phase a given CECT volume (or given image of a volume) is in.

In general, an Artificial Neural Network (ANN) (also referred to more generally as a neural network) is a computing system made up of a number of simple, highly interconnected processing elements (nodes), which process information by their dynamic state response to external inputs. ANNs are processing devices (algorithms and/or hardware) that are loosely modeled after the neuronal structure of the mammalian cerebral cortex, but on much smaller scales. Such systems progressively and autonomously learn tasks by means of examples, and they have successfully been applied to, for example, speech recognition, text processing and computer vision. A large ANN might have hundreds or thousands of processor units, whereas a mammalian brain has billions of neurons with a corresponding increase in magnitude of their overall interaction and emergent behavior.

Many types of neural networks are known, starting with feedforward neural networks, such as multilayer perceptrons, deep learning neural networks (DNNs) and convolutional neural networks. A feedforward neural network is an artificial neural network (ANN) where connections between the units do not form a cycle. A deep learning neural network is an artificial neural network with multiple hidden layers of units between the input and output layers. Similar to shallow ANNs, DNNs can model complex non-linear relationships. DNN architectures, e.g., for object detection and parsing, generate compositional models where the object is expressed as a layered composition of image primitives. The extra layers enable composition of features from lower layers, giving the potential of modeling complex data with fewer units than a similarly performing shallow network. DNNs are typically designed as feedforward networks.

In certain embodiments described herein, systems, methods and computer program products are provided that use Artificial Intelligence (AI) to facilitate contrast phase image detection with regard to different CECT image sources. Machine learning, which is a subset of AI, utilizes algorithms to learn from data (e.g., Big Data) and create foresights based on this data. AI refers to the intelligence when machines, based on information, are able to make decisions, which maximize the chance of success in a given topic. More specifically, AI is able to learn from a data set to solve problems and provide relevant recommendations. AI is a subset of cognitive computing, which refers to systems that learn at scale, reason with purpose, and naturally interact with humans. Cognitive computing is a mixture of computer science and cognitive science. Cognitive computing utilizes self-teaching algorithms that use data, visual recognition, and natural language processing to solve problems and optimize processes.

Referring now to the drawings in which like numerals represent the same or similar elements and initially toFIG. 1, this figure illustrates an example of a method100of utilizing AI and/or a recurrent neural network to train a contrast phase (CP) classification model, according to embodiments. Once trained, the CP classification model can be used on new volumes (or images) to make determinations as to probabilities that the given volume (or image) corresponds to one or more contrast phases. For example, a first image of an object taken during a first contrast phase (e.g., a PRE phase) may have visual differences (e.g., contrast, color, etc.) from a second image of the same object taken during a second contrast phase (e.g., an ART phase). The trained CP classification model uses AI to analyze new images and determine which contrast phase the image is likely in. Thus, in certain embodiments, for new CT volumes, user input may not be required to determine which CP the image is in. It should be noted that, in certain embodiments, the application of the CP classification is for determination of which contrast phase the image is in, and it is not used to identify the visual anomalies that may be present in the image. Once the image is classified as being in a particular CP, another AI model can then be selected and used to actually identify the anomalies. In this regard, multiple AI anomaly detection models may be trained, with each of the different AI anomaly detection models corresponding to a different one of the contrast phases. One AI anomaly detection model may not be suitable for a particular CP where the contrast for a particular visual anomaly are low, but it may work very well with a different CP where the contrast characteristics for the particular visual anomaly are high.

The visualization of the probabilities obtained by the model may present certain challenges. First, the CP boundaries between phases may be difficult to define. Therefore, there may be scans that occur at a point in time between two or more different CP states. One way to unify the description of CP may be to specify an amount of time that has elapsed after the injection of contrast. However, this method may suffer from a problem that depending on the metabolism of the subject (e.g., the patient), the actual CP may vary substantially between subjects considered at the same amount of time that has elapsed after injection of the contrast. Second, CP status may be cyclical. Therefore, for very late scans, CT volumes may either resemble a late scan or a pre-contrast injection scan. Therefore, relying solely on the amount of elapsed time after injection may create a serious discontinuity in the scores (i.e., one of the images could resemble either time zero or time 600 seconds (i.e., a late scan)). According to certain of the present embodiments, the outcome of the model is represented as probabilities that the volume is in a certain CP, and these probabilities are visualized using a cyclical visualization technique.

As shown inFIG. 1, at operation102, a three-dimensional CT training volume is provided that will be used to train the CP classification model. In certain embodiments, the CT training volume includes a plurality of different images of an object (e.g., a liver). In operation104, each of these images are assigned or designated a contrast phase. Thus, prior to performing the AI classification training of the CP classification model in operation108, it is assumed that the contrast phase of each of the images is known or has been otherwise determined. In certain embodiments, a doctor (or user) may examine each of the images and manually make determinations as to which of a plurality of different contrast phases the subject image is in. In alternate embodiments, rather than having a user make a manual designation of the CP of each image in the CT volume, AI systems may be used to classify the contrast phase of the particular image. That is, although the embodiments described above herein with respect toFIG. 1are directed to training a CP classification model, AI systems may be used (i.e., as an alternative to, or in conjunction with user input) to help determine the CPs for the training images. In an example, AI systems may determine Hounsfield unit (HU) values in various parts of the vasculature (e.g., ascending/descending aorta, pulmonary artery, pulmonary vein, portal vein, etc.), and those HU values may be used to determine the contrast phase. In certain embodiments, average Hounsfield units (AHUs) values are used to infer certain contrast injection parameters such as the concentration of contrast, the injection rate of the contrast, the injection duration, etc. Therefore, this AI analysis (or pre-classification) of CP for the images of the CT training volume may aid in facilitating the overall training process of the CP classification model. In other embodiments, the pre-classification of the CP by the AI systems may be provided to a user, and then the user may confirm or override the pre-classification according to their judgment.

As shown inFIG. 1, in operation106, each CT volume is divided axially in N overlapped sections. Each section is one instance used for training. In operation108, the CP classified images of the 3D CT training volume are processed by the AI systems to perform training. At operation110, the CP classification model is created and is ready to use to classify new data (i.e., new CT volumes).

Referring now toFIG. 2, this figure illustrates an example method200of the application of the trained CP classification model to a new CT volume to determine the CP for each of the new images. In operation202, a new 3D CT volume is provided. The new CT volume includes a plurality of new images that are different from the images used in training the CP classification model. As shown inFIG. 2, in operation203, each CT volume is divided axially in N overlapped sections. In operation204, the trained CP classification model is applied to each of the images in the new CT volume. In operation206, the AI system determines the probabilities that a given image falls into a certain contrast phase (CP). For example, if there are four contrast phases, the AI system may determine that a first image has a 64% probability of being in a first CP, a 30% probability that the image is in a second CP, a 4% probability that the image is in a third CP, and a 2% probability that the image is in a fourth CP. In certain embodiments, at operation208, the AI system generates and displays a visual representation of these probabilities on a display device.

At operation210inFIG. 2, the AI system determines whether the probability for a given CP for a given image exceeds a certain threshold. For example, a requisite threshold (or probability, or score) may be 60%. Thus, in the example above, the first image of the new CT volume having a 64% probability of being in the first CP exceeds the threshold level of 60%, and therefore is it determined to be in this CP. If it is determined that the image does not meet the requisite threshold (operation210:NO), then the image is not further processed by the AI system to determine the presence of abnormalities/anomalies. If it is determined that the image does meet the requisite threshold (operation210:YES), then the AI system applies an AI inference model to determine the presence of abnormalities/anomalies at operation214. In certain embodiments, a different AI inference model is developed for each of the different contrast phases (e.g., four different AI inference models corresponding to the PRE, ART, VEN and DEL contrast phases). In other embodiments, a single AI inference model is used that considers the contrast phase determined by the AI system at operation206.

In certain of the present embodiments, the scores obtained from contrast phase classification model are used for further processing by AI systems to determine a suitability of the scan for further processing (e.g., abnormality detection processing). In one example, for analysis of liver lesions, VEN scans may be defined as those scans with a probability of occurring in the VEN contrast phase, where such a probability is larger than a particular threshold level. In other words, the CP classification model may triage new CECT volumes to determine whether or not they are in the CP of interest for a particular type of anomaly. For example, if the CP classification model determines that there is a low likelihood that a particular new CECT is in the VEN phase, it will not be further analyzed to determine the presence of anomalies.

In certain embodiments, probability scores obtained from phase classification are used to generate an intuitive visualization (or graphic representation300) for a user, so that the user can readily understand the likelihood of a phase of the contrast for a particular CECT image. Referring now toFIG. 3A, an example of a graphic representation is shown. InFIG. 3A, an example of a four-sided polygon312(e.g., a diamond or a square) is displayed. In general, the CP probabilities are visualized using regular polygons where each of the corners represents a single CP. In certain embodiments, the actual visualization of the CP for a particular image in the CT volume is represented by a point310(or some other suitable shape, marker or indicator) located within the polygon312or on the edge of the polygon312. In the example shown inFIG. 3A, a four-sided polygon312is shown that corresponds to four different contrast phases: pre-contrast (PRE); arterial (ART); venous (VEN); and delayed (DEL). Each corner of the polygon312corresponds to a pure phase (i.e., a 100% probability that the image is in the particular contrast phase). In the example shown inFIG. 3A, the PRE phase302probability is 0%, the ART phase304probability is 0%, the VEN phase306probability is 44%, and the DEL phase308probability is 56%. Thus, the point310corresponding to the subject image (e.g., subject image350ofFIG. 3B) lies exactly on the line between the VEN phase306and the DEL phase308. If there were a greater than 0% probability for one (or both) of the PRE phase302and the ART phase304, then the point310would occur within the main area of the polygon312somewhat closer to one (or both) of the vertices of the polygon corresponding to the PRE phase302and the ART phase304. Thus, in the example shown inFIG. 3A, there is only a slightly higher percentage chance that the subject image is in the DEL phase308than the VEN phase306.

Referring now toFIG. 3B, the subject image350includes an anomaly354and the surrounding tissue352of, for example, a liver. As shown inFIG. 3B, there is an amount of contrast between the darker anomaly354and the relative lighter surrounding tissue352. In general, this anomaly354appears quite distinct to an observer, and would seem to be readily identifiable. However, assuming that this subject image350corresponds to the output of the CP classification model (i.e., the graphic representation300) discussed above with respect toFIG. 3A, then even though the anomaly appears in stark contrast to the surrounding tissue it may not correlate well with either the a DEL anomaly detection model or a VEN anomaly detection model. In this particular example, the subject image350may have been taken at a particular moment in time when the VEN phase306was transitioning to the DEL phase308(or vice versa). Assuming this image was taken during such a phase transition, there may be visual characteristics of the image that do not apply well to either of the AI-based anomaly detection models. Therefore, this particular image may not be selected for further AI inferencing because the none of the CP probabilities (or scores) generated by the CP classification model exceeded the threshold level, as discussed above with respect to operation210ofFIG. 2.

In certain embodiments, a decision as to whether a particular volume (or particular image within the volume) is to be processed by AI for anomaly detection may be made based on the determined contrast phase (CP). In certain embodiments, this is done automatically based on the thresholds discussed above. In other embodiments, this may be done manually by a radiologist (or user) using the probability visualizations (or graphical representations) discussed herein. In the embodiments where the user makes a manual determination of the contrast phase based on the visualization, the user looks at the graphical visualization and makes a subjective determination as to what phase they believe the image is most likely to be based on the position of the point310in the polygon312. For example, if the point310appears close to one of the vertices of the polygon312, the user may be confident that it is in a particular CP.

Referring now toFIG. 4A, this figure shows a diagram of an example of a visual representation of the probabilities of certain CPs for a given image in a CT volume as determined by the CP classification model, according to embodiments. In the example shown inFIG. 4A, a graphical representation400including a four-sided polygon404is shown that corresponds to four different contrast phases: an A-phase402-A; a B-phase402-B; a C-phase402-C and a D-phase402-D. Each corner (or vertex) of the polygon404corresponds to a pure phase (i.e., a 100% probability that the image is in the particular contrast phase). Similar to the example discussed above with respect toFIG. 3A, a point410is shown in the polygon404which reflects the relative probabilities that the image falls into a particular contrast phase. The closer the point410is to a particular vertex of the polygon, the higher percentage the probability is that the volume (or image) corresponds to the respective CP associated with that vertex. In certain embodiments, the graphical representation400also includes one or more additional visual indicators of these probabilities. In this example, the graphical representation400includes graphical representation of springs412. The location of the point410is determined based on an equilibrium point assuming it is attached to corners of the display using perfect springs412with a stiffness constant equal to the probability that the given volume (or image) is in the given phase determined by the model. The rest length of all springs412is assumed to be zero. In this example graphical representation, the probability of the D-phase402-D is the largest, the spring412connecting the point410with the D-phase402-D vertex is the stiffest, and the equilibrium point410is closest to the D-phase402-D vertex.

Referring now toFIG. 4B, this figure shows a diagram of another example of a visual representation of the probabilities of certain CPs for a given image in a CT volume as determined by the CP classification model, according to embodiments. In the example shown inFIG. 4B, a graphical representation400including a five-sided polygon404is shown that corresponds to five different contrast phases: an A-phase402-A; a B-phase402-B; a C-phase402-C; a D-phase402-D; and an E-phase402-E. Similar toFIG. 4Adiscussed above, the corner (or vertex) of the polygon404corresponds to a pure phase (i.e., a 100% probability that the image is in the particular contrast phase). Similar to the example discussed above with respect toFIG. 4A, a point410is shown in the polygon404which reflects the relative probabilities that the volume (or image) falls into a particular contrast phase. The closer the point410is to a particular vertex of the five-sided polygon, the higher percentage the probability is that the image corresponds to the respective CP associated with that vertex. In this example, the graphical representation400also includes springs412. In this example graphical representation, the probability of the A-phase402-A is the largest, the spring412connecting the point410with the A-phase402-A vertex is the stiffest, and the equilibrium point410is closest to the A-phase402-A vertex.

Referring now toFIG. 4C, this figure shows a diagram of another example of a visual representation of the probabilities of certain CPs for a given image in a CT volume as determined by the CP classification model, according to embodiments. In the example shown inFIG. 4C, a graphical representation400including a six-sided polygon404is shown that corresponds to six different contrast phases: an A-phase402-A; a B-phase402-B; a C-phase402-C; a D-phase402-D; an E-phase402-E; and an F-phase402-F. Similar toFIG. 4Adiscussed above, the corner (or vertex) of the polygon404corresponds to a pure phase (i.e., a 100% probability that the image is in the particular contrast phase). Similar to the example discussed above with respect toFIG. 4A, a point410is shown in the polygon404which reflects the relative probabilities that the volume (or image) falls into a particular contrast phase. The closer the point410is to a particular vertex of the six-sided polygon, the higher percentage the probability is that the volume (or image) corresponds to the respective CP associated with that vertex. In this example, the graphical representation400also includes springs412. In this example graphical representation, the probability of the B-phase402-A is the largest, the spring412connecting the point410with the B-phase402-B vertex is the stiffest, and the equilibrium point410is closest to the B-phase402-B vertex.

Referring now toFIG. 5, this figure is a diagram of an example of a visual representation of the probabilities of certain CPs for a plurality of images of a CT volume as determined by the CP classification model, according to embodiments. In the example shown inFIG. 5, a graphical representation500including a four-sided polygon503is shown that corresponds to four different contrast phases: a PRE phase514-A; an ART phase514-B; a VEN phase514-C and a DEL phase514-D. Each corner (or vertex514-A,514-B,514-C,514-D) of the polygon503corresponds to a pure phase (i.e., a 100% probability that the image is in the particular contrast phase). A plurality of points504,506,508,510are shown located at different positions in the interior of the polygon503which reflect the relative probabilities that the image falls into a particular contrast phase. The closer the point504,506,508,510is to a particular vertex of the polygon503, the higher the likelihood is that the volume (or image) corresponds to the respective CP associated with that vertex. In certain embodiments, as shown inFIG. 5, the polygon503is divided into sections502-A,502-B,502-C and502-D corresponding to the different contrast phases.

In operation, as the AI system cycles through each of the images in the CT volume, the CP classification model will generate a point for each of the images. Each image will have an actual contrast phase (the phase that the contrast is actually in), and the AI system attempts to determine what this phase is by applying the CP classification model. InFIG. 5, the points in the polygon503have four different types of cross-hatching to signify what the actual contrast phase is. In particular, all of the points504with vertical hatching correspond to points that are actually in the PRE phase, all of the points506with crisscross hatching correspond to points that are actually in the ART phase, all of the points508with light hatching correspond to points that are actually in the VEN phase, and all of the points510with dark hatching correspond to points that are actually in the DEL phase. The position of the various points in the polygon503reflects the output of the CP classification model for the particular images. For example, a majority of the points508are located in a region512-C close to the vertex514-C. This proximity indicates that the CP classification model determined a high probability (e.g., 90%) that the image occurs in the VEN CP. However, some of the points508appear outside the region512-C but within the section502-C that is associated with the VEN CP. For these points, the CP classification model has determined that the image is in the VEN CP, but with a lower probability. Moreover, some of the points508may occur in, for example, the section502-B associated with the ART CP. For these points, the CP classification model has essentially misclassified the images as being in the wrong CP. One possible reason for this is that the CP was transitioning between two different phases where it may be difficult for the CP classification model to determine the correct CP. As described above with respect to operation210ofFIG. 2, some of these classifications (or misclassifications) may have probability scores that are below a determined threshold. Thus, these images may not be selected for anomaly detection by the AI systems because it is not clear what CP the image is in, and it is difficult to determine which AI anomaly detection model to apply to the image. As mentioned above, one AI anomaly detection model that is developed for a particular CP phase (e.g., the ART phase) may not work well for an image that is in a different CP phase (e.g., the VEN phase). It should be appreciated that the diamond shaped four-sided graphical representation500shown inFIG. 5could be modified for any different number of CPs (e.g., the six-sided polygon shown inFIG. 4C). Therefore, the example graphical representation500shown inFIG. 5provides a reviewer with an easy to understand distribution of where the CP probabilities fall for a plurality of CECT images.

Referring now toFIG. 6, an exemplary processing system600to which the present embodiments may be applied is shown in accordance with one embodiment. The processing system600includes at least one processor (CPU)604operatively coupled to other components via a system bus602. A cache606, a Read Only Memory (ROM)608, a Random-Access Memory (RAM)610, an input/output (I/O) adapter620, a sound adapter630, a network adapter640, a user interface adapter650, and a display adapter660, are operatively coupled to the system bus602.

A first storage device622and a second storage device624are operatively coupled to system bus602by the I/O adapter620. The storage devices622and624may be any of a disk storage device (e.g., a magnetic or optical disk storage device), a solid-state magnetic device, and so forth. The storage devices622and624may be the same type of storage device or different types of storage devices.

A speaker632is operatively coupled to system bus602by the sound adapter630. A transceiver642is operatively coupled to system bus602by network adapter640. A display device662is operatively coupled to system bus602by display adapter660.

A first user input device652, a second user input device654, and a third user input device656are operatively coupled to system bus602by user interface adapter650. The user input devices652,654, and656may be any of a keyboard, a mouse, a keypad, an image capture device, a motion sensing device, a microphone, a device incorporating the functionality of at least two of the preceding devices, or any other suitable types of input devices. The user input devices652,654, and656may be the same type of user input device or different types of user input devices. The user input devices652,654, and656are used to input and output information to and from system600. In certain embodiments, a neural network component670with a CP classification mode is operatively coupled to system bus602.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Referring now toFIG. 7, illustrative cloud computing environment750is depicted. As shown, cloud computing environment750includes one or more cloud computing nodes710with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone754A, desktop computer754B, laptop computer754C, and/or automobile computer system754N may communicate. Nodes710may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment750to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices754A-N shown inFIG. 7are intended to be illustrative only and that computing nodes710and cloud computing environment750can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

Hardware and software layer860includes hardware and software components. Examples of hardware components include: mainframes861; RISC (Reduced Instruction Set Computer) architecture-based servers862; servers863; blade servers864; storage devices865; and networks and networking components866. In some embodiments, software components include network application server software867and database software868.

Virtualization layer870provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers871; virtual storage872; virtual networks873, including virtual private networks; virtual applications and operating systems874; and virtual clients875.

Workloads layer890provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation891; software development and lifecycle management892; virtual classroom education delivery893; data analytics processing894; transaction processing895; and neural network CECT CP classification processing896.