Fast globally optimal segmentation of 3d prostate mri with axial symmetry prior

Wu Qiu, Jing Yuan, Eranga Ukwatta, Yue Sun, Martin Rajchl, Aaron Fenster

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

11 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2013
Subtitle of host publication16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part II
EditorsKensaku Mori
Place of PublicationBerlin
Number of pages8
ISBN (Electronic)978-3-642-40763-5
ISBN (Print)978-3-642-40762-8
Publication statusPublished - 2013
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (LNCS)


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