Abstract:
Systems, methods, and media for detecting an anatomical object in a medical device image are provided. In some embodiments, system for detecting an anatomical object in a medical device image are provided, the systems comprising: at least one hardware processor that: applies the medical device image to a classifier having a plurality of stages, wherein a first stage of the plurality of stages and a second stage of the plurality of stages each includes a strong learner formed fro ma plurality of weak learners, and the weak learners in the second stage include a plurality of the weak learners included in the first stage; and identifies the medical device image as being positive or negative of showing the anatomical object based on the application the medical device image to be classifier.

Description:
CROSS REFERENCE TO RELATED APPLICATION 
       [0001]    This application claims the benefit on U.S. Provisional Patent Application No. 61/442,112, filed Feb. 11, 2011, which is hereby incorporated by reference herein in its entirety. 
     
    
     TECHNICAL FIELD 
       [0002]    The disclosed subject matter relates to systems, methods, and media for detecting an anatomical object in a medical device image. 
       BACKGROUND 
       [0003]    Pulmonary embolism (PE) is a relatively common cardiovascular emergency with about 600,000 cases occurring annually and causing approximately 200,000 deaths in the United States per year. A pulmonary embolus usually starts from the lower extremity, travels in the bloodstream through the heart and into the lungs, gets lodged in the pulmonary arteries, and subsequently blocks blood flow into, and oxygen exchange in, the lungs, leading to sudden death. Based on its relative location in the pulmonary arteries, an embolus may be classified into four groups (central, lobar, segmental and sub-segmental). 
         [0004]    Computed tomography pulmonary angiography (CTPA) has become the test of choice for PE diagnosis. The interpretation of CTPA image datasets is made complex and time consuming by the intricate branching structure of the pulmonary vessels, a myriad of artifacts that may obscure or mimic PEs, and suboptimal bolus of contrast and inhomogeneity with the pulmonary arterial blood pool. 
         [0005]    Several approaches for computer-aided diagnosis of PE in CTPA have been proposed. However, these approaches are not adequately capable of detecting central PEs, distinguishing the pulmonary artery from the vein to effectively remove any false positives from the veins, and dynamically adapting to suboptimal contrast conditions associated the CTPA scans. 
         [0006]    Accordingly, new mechanisms for detecting an anatomical object in a medical device image are needed. 
       SUMMARY 
       [0007]    Systems, methods, and media for detecting an anatomical object in a medical device image are provided. In some embodiments, system for detecting an anatomical object in. a medical device image are provided, the systems comprising: at least one hardware processor that: applies the medical device image to a classifier having a plurality of stages, wherein a first stage of the plurality of stages and a second stage of the plurality of stages each includes a strong learner formed from a plurality of weak learners, and the weak learners in the second stage include a plurality of the weak learners included in the first stage; and identifies the medical device image as being positive or negative of showing the anatomical object based on the application the medical device image to the classifier. 
         [0008]    In some embodiments, methods for detecting art anatomical object in a medical device image are provided, the methods comprising: applying the medical device image to a classifier having a plurality of stages, wherein a first stage of the plurality of stages and a second stage of the plurality of stages each includes a strong learner formed from a plurality of weak learners, and the weak learners in the second stage include a plurality of the weak learners included in the first stage; and identifying the medical device image as being positive or negative of showing the anatomical object based on the application the medical device image to the classifier. 
         [0009]    In some embodiments, non-transitory computer-readable media containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for detecting an anatomical object in a medical device image are provided, the method comprising: applying the medical device image to a classifier having a plurality of stages, wherein a first stage of the plurality of stages and a second stage of the plurality of stages each includes a strong learner formed from a plurality of weak learners, and the weak learners in the second stage include a plurality of the weak learners included in the first stage; and identifying the medical device image as being positive or negative of showing the anatomical object based on the application the medical device image to the classifier. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0010]      FIG. 1  is a block diagram of hardware that can be used in accordance with some embodiments. 
           [0011]      FIG. 2  shows examples of Haar features that can be used in accordance with some embodiments. 
           [0012]      FIG. 3  is a block diagram of a multi-stage classifier in accordance with some embodiments. 
           [0013]      FIG. 4  is a flow diagram of a process for training a multi-stage classifier in accordance with some embodiments. 
           [0014]      FIG. 5  is a block diagram of another multi-stage classifier in accordance with some embodiments. 
           [0015]      FIG. 6  is a flow diagram of another process for training a multi-stage classifier in accordance with some embodiments. 
           [0016]      FIG. 7  is a flow diagram of a process for training a single-stage classifier in accordance with some embodiments. 
           [0017]      FIG. 8  is a flow diagram of a process for detecting objects in images using a classifier in accordance with some embodiments. 
       
    
    
     DETAILED DESCRIPTION 
       [0018]    Systems, methods, and media for detecting an anatomical object in a medical device image are provided. More particularly, in some embodiments, systems, methods, and media for detecting an anatomical object, such as a pulmonary trunk, in a medical device image, such as a computed tomography pulmonary angiography (CTPA) image, are provided. 
         [0019]    The pulmonary trunk is the main pulmonary artery that rises from the right ventricle of the heart, extends upward, and divides into the right and left pulmonary arteries carrying blood to the lungs. Because PEs are only found in the pulmonary artery, identifying the pulmonary trunk in medical device images, such as CTPA images, can be used in PE diagnosis. 
         [0020]    Turning to  FIG. 1 , an example of hardware  100  that can be used in accordance with some embodiments is illustrated. As shown, this hardware can include an imaging device  102  and an image processing device  104 . Imaging device  102  can be any suitable device for generating imaging data that can be provided to image processing device  104 . For example, in some embodiments, imaging device  102  can be a computed tomography (CT) scanner. Image processing device  104  can be any suitable device for receiving and processing imaging data. For example, in some embodiments, image processing device  104  can be a computer. Imaging device  102  can communicate with image processing device  104  in any suitable manner such as via a direct connection between the devices, via a communication network, etc. 
         [0021]    In some embodiments, image processing device  104  can be any of a general purpose device such as a computer or a special purpose device such as a client, a server, etc. Any of these general or special purpose devices can include any suitable components such as a hardware processor (which can be a microprocessor, digital signal processor, a controller, etc), memory, communication interfaces, display controllers, input devices, etc. 
         [0022]    In some embodiments, imaging device  102  and image processing device  104  can be integrated into a single device. 
         [0023]    In some embodiments, a machine-learning-based approach can be used by image processing device  104  for automatically detecting an anatomical object, such as a pulmonary trunk, in a medical device image. 
         [0024]    More particularly, for example, in some embodiments, a cascaded AdaBoost classifier can be trained with a large number of Haar features (example of which are shown in  FIG. 2 ) extracted from computed tomography pulmonary angiography (CTPA) image samples, so that an anatomical object, such as a pulmonary trunk, can subsequently be automatically identified by sequentially scanning CTPA images and classifying each encountered sub-image with the trained classifier. In some embodiments, CTPA images can be automatically scanned at multiple scales to handle size variations of the anatomical objects (e.g., pulmonary trunks). 
         [0025]    An AdaBoost classifier is a type of machine learning algorithm drat combines weak learners to create a single strong learner. A weak learner is a classifier that may perform only slightly better than random guessing. A commonly used weak classifier called the decision stump can be used to make a prediction based on the value of a single input feature. 
         [0026]    For example, h 1 , h 2 , . . . , h N  make up a set of weak learners, a combination of these weak learners can be written as: 
         [0000]        F ( x )=Σ j−1   N   f   j ( x )=Σ j=1   N ω j   h   j ( x ),
 
         [0000]    where ω j  is the corresponding coefficient for weak learner h j . Boosting is a process to select weak learners h j  and determine their coefficients ω j , so as to combine the selected weak learners to form a strong learner F(x). 
         [0027]    In some embodiments, AdaBoost can he used to select the most relevant, features from any suitable number (e.g., thousands) of Haar features, each corresponding to a weak learner. In some embodiments, a Haar feature can be defined in terms of two adjacent rectangle regions, which can be illustrated in white and black as shown in  FIG. 2 , for example. The value of a Haar feature can be the sum of any suitable pixels values (such as intensity) in one or more first rectangle(s) (e.g., the white rectangles) of the feature minus the sum of the suitable pixel values in one or more second rectangle(s) (e.g., the black rectangle(s)) of the feature. 
         [0028]    In some embodiments, any suitable criteria, such as desired true positive rate, false positive rate, and number of weak learners, can be used to determine the number of strong boosted classifiers, the number of weak learners in each boosted classifier, and the relative operating characteristic (ROC) operating points (which can can be selected from a ROC curve produced during training) for classifying images. For example, in some embodiments, a True Positive Rate (TPR) a, a False Positive Rate (FPR) β i , and a maximum number of weak learners η i  can be used as criteria for training a cascaded classifier stage. 
         [0029]    As shown in FIG,  3 , an AdaBoost classifier  300  can include any suitable number of strong classifier stages  302 ,  304 , and  306 . D i   + , D i   −  can be used to refer to positive sub-images and negative sub-images that can be used for training an AdaBoost classifier stage i. In each stage  302 ,  304 , or  306 , during training, weak learners can be added to tire stage until a given target performance (α i , β i ) or a given number of weak learners η i  in the stage is reached. The output of the training at stage i is a boosted classifier containing weak learners from f τ     i−1     +1  to f 96     i   . Upon completing training a given stage, new negative samples can be classified by the stage to identify false positives (i.e., negative samples which are classified as positive) and then these negative samples (which are falsely classified as positives) can be combined with the negative samples used for training the current stage and the combination used for training the subsequent stage. 
         [0030]    Turning to  FIG. 4 , an example process  400  for training this classifier in 
         [0031]    accordance with some embodiments is shown. As illustrated, after process  400  begins at  402 , the process selects a first stage of the classifier to train. This stage can be selected in any suitable manner. Next, at  406 , the process can select an initial set of weak learners for the stage. Any suitable number of weak learners, including one, can be selected, and the weak learners can be selected in any suitable manner, such as randomly. Then, at  408 , process  400  can apply positive and negative sub-image samples to the set of weak learners. Any suitable number of positive and negative sub-image samples (e.g., 100 each) can be applied, and these samples can be selected for application in any suitable manner, such as randomly. The process can then determine at  410  whether the performance of the stage is sufficient or whether the maximum number of weak learners for the stage has been reached. Any suitable criteria or criterion can be used for determining whether the performance of the stage is sufficient in some embodiments. For example, in some embodiments, the performance of the stage can be deemed to be sufficient when the TPR α i  is over 0.99 and FPR β i  is below 0.05. Any suitable threshold η i  for a maximum number of weak learners can be used in some embodiments. For example, η i  can be 30 in some embodiments. If it is determined at  410  that the performance is not sufficient and the maximum number of weak learners has not been reached, then process  400  can add one or more weak learners to the set at  412  and loop back to  408 . The weak learners to be added can be selected in any suitable manner (e.g., randomly) and any suitable number of weak learners (including one) can be added, in some embodiments. Otherwise, at  414  process  400  can then assign the set of weak, learners to the boosted strong classifier for the current stage. Next, at  416 , process  400  can use the set of weak, learners to detect new negative samples that appear positive (i.e., false positives) and add these new negative samples to the set of negative samples and use this new set for the next stage. Any suitable number of new negative samples, such as  100 . can be used in some embodiments. At  418 , process  400  can then determine whether the current stage is the last stage, and, if not, select the next stage at  420 . Otherwise, process can end at  422 . 
         [0032]    Another example classifier  500  that can be used in some embodiments is illustrated in  FIG. 5 . As shown, classifier  500  can include any suitable number of strong classifier stages  502 ,  504 , and  506 . D i   + , D i   −  can be used to refer to positive sub-images and negative sub-images that can be used for the teaming a classifier stage i. In each stage  502 ,  504 , or  506 , during training, weak learners can be added to the stage until a given target performance (α i , β i ) or a given number of weak learners η i  in the stage is reached. The output of the training at stage i is a boosted classifier  504  containing weak learners from f 1  to f 96     i   . That is, a stage can include all of the weak learners of all previous stages in some embodiments. Upon completing training a given stage, new negative samples can be classified by the stage to identify false positives (i.e., negative samples which are classified as positive) and then these negative samples (which are falsely classified as positives) can be added to the negative samples from the current stage and used for training the subsequent stage. 
         [0033]    Turning to  FIG. 6 , an example process  600  for training this classifier in accordance with some embodiments is shown. As illustrated, process  600  includes steps  402 ,  404 ,  406 ,  408 , 410 ,  412 ,  414 ,  416 ,  418 ,  420 , and  422  which can be performed as described above in connection with process  400  of  FIG. 4 . Unlike in process  400 , however, after performing step  420 , process  600  can branch to step  412  rather than step  406 . 
         [0034]    In some embodiments, rather than using a multi-stage classifier as described above, a single stage classifier can be used. Such a classifier may include a single classifier stage  302  as shown in  FIG. 3 . 
         [0035]    Turning to  FIG. 7 , an example process  700  for training this classifier in accordance with some embodiments is shown. As illustrated, process  700  includes steps  402 ,  406 ,  408 ,  410 ,  412 ,  414 , and  422  which can be performed as described above in connection with process  400  of  FIG. 4 . However, unlike process  400 , in this approach, 100 positive samples (or any other suitable number) and 500 negative samples (or any other suitable number) can be used to train the single stage, and training can be completed when the TPR α i =100, the FPR β i =0, and when the number of weak classifiers η i =100. In some embodiments, negative samples can be false positive samples from other training techniques as described above. ( 00351  As described above, to perform detection using a classifier, an image can be 
         [0036]    provided to the one or more stages of the classifier and a positive indication or a negative indication can be provided. If at any stage in the classifier, an image is classified as negative, the image can be removed from subsequent testing by subsequent stages of the classifier and the classification of the image can be maintained as negative. 
         [0037]    Turning to  FIG. 8 , an example process  800  for detecting images in accordance with some embodiments is shown. As illustrated, after process  800  begins at  802 , the process can select a detection scheme at  806 . Any suitable detection scheme can be used, such as the multi-stage or single-stage schemes described above. Next, the first image can be selected at  806 . The first image can be selected in any suitable manner (e.g., such as randomly, in-order, etc), and the image can be any suitable portion of another image (e.g., such as a random portion of a first image). Then, at  808 , the first strong classifier in the selected scheme can be selected. At  810 , the image can then be applied to the selected strong classifier, which can assign a classification and a score to the image. At  812 , process  800  can then determine if the classification from the stage is negative. If so, the next image can be selected at  814  and process  800  can loop back to  808 . Otherwise, at  816 , it can be determined if the current stage is the last strong classifier. If not, then process  800  can select the next strong classifier at  818  and loop back, to  810 . Otherwise, process  800  can classify the image as positive at  820  and merge the image with any previous overlapping, positive-classified images at  822 . Any suitable images can be. identified as being overlapping in some embodiments. For example, images can be identified as being overlapping if the images share over 25% of their data (e.g., based, on location and size of the image) and/or if their z-axis distance is less than five pixels, in some embodiments, when merging images, their individual scores can be added together. Next, at  824 , process  800  can determine if the current image is the last image. If not, the process can select the next image at  814  and loop back to  808 . Otherwise, the process can select the highest-score merged image as the detected object at  826  and terminate at  828 . 
         [0038]    In some embodiments, any suitable computer readable media can be used for storing instructions for performing the processes described herein, such as performing training of classifiers and classifying of images. For example, in some embodiments, computer readable media can be transitory or non-transitory. For example, non-transitory computer readable media can include media such as magnetic media (such as hard disks, floppy disks, etc.), optical media (such as compact discs, digital video discs, Blu-ray discs, etc.), semiconductor media (such as flash memory, electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), etc.), any suitable media that is not fleeting or devoid of any semblance of permanence during transmission, and/or any suitable tangible media. As another example, transitory computer readable media can include signals on networks, in wires, conductors, optical fibers, circuits, any suitable media that is fleeting and devoid of any semblance of permanence during transmission, and/or any suitable intangible media. 
         [0039]    Although the invention has been described and illustrated in the foregoing illustrative embodiments, it is understood that the present disclosure has been made only by way of example, and that numerous changes in the details of implementation of the invention can be made without departing from the spirit and scope of the invention, which is limited only by the claims that follow. Features of the disclosed embodiments cm be combined and rearranged in various ways.