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2 March 2018 Automatic and fast CT liver segmentation using sparse ensemble with machine learned contexts
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A fast and automatic method, using machine learning and min-cuts on a sparse graph, for segmenting Liver from CT Contrast enhanced (CTCE) datasets is proposed. The method first localizes the liver by estimating its centroid using a machine learnt model with features that capture global contextual information. Individual ‘N’ rapid segmentations are carried out by running a min-cut on a sparse 3D rectilinear graph placed at the estimated liver centroid with fractional offsets. Edges of the graph are assigned a cost that is a function of a conditional probability, predicted using a second machine learnt model, which encodes relative location along with a local context. The costs represent the likelihood of the edge crossing the liver boundary. Finally, 3D ensembles of ‘N’ such low resolution, high variance sparse segmentations gives a final high resolution, low variance semantic segmentation. The proposed method is tested on three publically available challenge databases (SLIVER07, 3Dircadb1 and Anatomy3) with M-fold cross validation. On the most popular database: SLIVER07 alone, consisting of 20 datasets we obtained a mean dice score of 0.961 with 4-fold cross validation and an average run-time of 6.22s on a commodity hardware (Intel 3.6GHz dual core, with no GPU). On a combined database of 60 datasets from all three, we obtained a mean dice score of 0.934 with 6-fold cross validation.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bhavya Ajani, Aditya Bharadwaj, and Karthik Krishnan "Automatic and fast CT liver segmentation using sparse ensemble with machine learned contexts", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105740L (2 March 2018);

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