Efficient 3D multi-region prostate MRI segmentation using dual optimization

Authors:
Wu Qiu;Jing Yuan;Eranga Ukwatta;Yue Sun;Martin Rajchl;Aaron Fenster
Affiliations:
Robarts Research Institue, Western University, London, Ontario, Canada;Robarts Research Institue, Western University, London, Ontario, Canada;Robarts Research Institue, Western University, London, Ontario, Canada,Department of Biomedical Engineering, Western University, London, Ontario, Canada;Robarts Research Institue, Western University, London, Ontario, Canada,Department of Biomedical Engineering, Western University, London, Ontario, Canada;Robarts Research Institue, Western University, London, Ontario, Canada,Department of Biomedical Engineering, Western University, London, Ontario, Canada;Robarts Research Institue, Western University, London, Ontario, Canada,Department of Biomedical Engineering, Western University, London, Ontario, Canada
Venue:
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Year:
2013

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Abstract

Efficient and accurate extraction of the prostate, in particular its clinically meaningful sub-regions from 3D MR images, is of great interest in image-guided prostate interventions and diagnosis of prostate cancer. In this work, we propose a novel multi-region segmentation approach to simultaneously locating the boundaries of the prostate and its two major sub-regions: the central gland and the peripheral zone. The proposed method utilizes the prior knowledge of the spatial region consistency and employs a customized prostate appearance model to simultaneously segment multiple clinically meaningful regions. We solve the resulted challenging combinatorial optimization problem by means of convex relaxation, for which we introduce a novel spatially continuous flow-maximization model and demonstrate its duality to the investigated convex relaxed optimization problem with the region consistency constraint. Moreover, the proposed continuous max-flow model naturally leads to a new and efficient continuous max-flow based algorithm, which enjoys great advantages in numerics and can be readily implemented on GPUs. Experiments using 15 T2-weighted 3D prostate MR images, by inter- and intra-operator variability, demonstrate the promising performance of the proposed approach.