This paper presents a new supervised learning framework for the e\ufb03cient recognition and segmentation of anatomical structures in 3D computed tomography (CT), with as little training data as possible. Training supervised classi\ufb01ers to recognize organs within CT scans requires a large number of manually delineated exemplar 3D images, which are very expensive to obtain. In this study, we borrow ideas from the \ufb01eld of active learning to optimally select a minimum subset of such images that yields accurate anatomy segmentation. The main contribution of this work is in designing a combined generative-discriminative model which: i) drives optimal selection of training data; and ii) increases segmentation accuracy. The optimal training set is constructed by \ufb01nding unlabeled scans which maximize the disagreement between our two complementary probabilistic models, as measured by a modi\ufb01ed version of the Jensen-Shannon divergence. Our algorithm is assessed on a database of 196 labeled clinical CT scans with high variability in resolution, anatomy, pathologies, etc. Quantitative evaluation shows that, compared with randomly selecting the scans to annotate, our method decreases the number of training images by up to 45%. Moreover, our generative model of body shape substantially increases segmentation accuracy when compared to either using the discriminative model alone or a generic smoothness prior (e.g. via a Markov Random Field).<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper presents a new supervised learning framework for the e\ufb03cient recognition and segmentation of anatomical structures in 3D computed tomography (CT), with as little training data as possible. Training supervised classi\ufb01ers to recognize organs within CT scans requires a large number of manually delineated exemplar 3D images, which are very expensive to obtain. 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