Graph search with appearance and shape information for 3-D prostate and bladder segmentation

Authors:
Qi Song;Yinxiao Liu;Yunlong Liu;Punam K. Saha;Milan Sonka;Xiaodong Wu
Affiliations:
Department of Electrical & Computer Engineering, University of Iowa, Iowa City, IA;Department of Electrical & Computer Engineering, University of Iowa, Iowa City, IA;Department of Electrical & Computer Engineering, University of Iowa, Iowa City, IA;Department of Electrical & Computer Engineering, University of Iowa, Iowa City, IA;Department of Electrical & Computer Engineering, University of Iowa, Iowa City, IA and Department of Radiation Oncology, University of Iowa, Iowa City, IA and Department of Ophthalmology & ...;Department of Electrical & Computer Engineering, University of Iowa, Iowa City, IA and Department of Radiation Oncology, University of Iowa, Iowa City, IA
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Year:
2010

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Abstract

The segmentation of soft tissues in medical images is a challenging problem due to the weak boundary, large deformation and serious mutual influence. We present a novel method incorporating both the shape and appearance information in a 3-D graph-theoretic framework to overcome those difficulties for simultaneous segmentation of prostate and bladder. An arc-weighted graph is constructed corresponding to the initial mesh. Both the boundary and region information is incorporated into the graph with learned intensity distribution, which drives the mesh to the best fit of the image. A shape prior penalty is introduced by adding weighted-arcs in the graph, which maintains the original topology of the model and constraints the flexibility of the mesh. The surface-distance constraints are enforced to avoid the leakage between prostate and bladder. The target surfaces are found by solving a maximum flow problem in low-order polynomial time. Both qualitative and quantitative results on prostate and bladder segmentation were promising, proving the power of our algorithm.