Fully automated prostate segmentation in 3D MR based on normalized gradient fields cross-correlation initialization and LOGISMOS refinement

Yin Yin; Sergei V. Fotin; Senthil Periaswamy; Justin Kunz; Hrishikesh Haldankar; Naira Muradyan; François Cornud; Baris Turkbey; Peter Choyke

doi:10.1117/12.911758

14 February 2012 Fully automated prostate segmentation in 3D MR based on normalized gradient fields cross-correlation initialization and LOGISMOS refinement

Yin Yin, Sergei V. Fotin, Senthil Periaswamy, Justin Kunz, Hrishikesh Haldankar, Naira Muradyan, François Cornud, Baris Turkbey, Peter Choyke

Author Affiliations +

Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 831406 (2012) https://doi.org/10.1117/12.911758
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Manual delineation of the prostate is a challenging task for a clinician due to its complex and irregular shape. Furthermore, the need for precisely targeting the prostate boundary continues to grow. Planning for radiation therapy, MR-ultrasound fusion for image-guided biopsy, multi-parametric MRI tissue characterization, and context-based organ retrieval are examples where accurate prostate delineation can play a critical role in a successful patient outcome. Therefore, a robust automated full prostate segmentation system is desired. In this paper, we present an automated prostate segmentation system for 3D MR images. In this system, the prostate is segmented in two steps: the prostate displacement and size are first detected, and then the boundary is refined by a shape model. The detection approach is based on normalized gradient fields cross-correlation. This approach is fast, robust to intensity variation and provides good accuracy to initialize a prostate mean shape model. The refinement model is based on a graph-search based framework, which contains both shape and topology information during deformation. We generated the graph cost using trained classifiers and used coarse-to-fine search and region-specific classifier training. The proposed algorithm was developed using 261 training images and tested on another 290 cases. The segmentation performance using mean DSC ranging from 0.89 to 0.91 depending on the evaluation subset demonstrates state of the art performance. Running time for the system is about 20 to 40 seconds depending on image size and resolution.

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Yin Yin, Sergei V. Fotin, Senthil Periaswamy, Justin Kunz, Hrishikesh Haldankar, Naira Muradyan, François Cornud, Baris Turkbey, and Peter Choyke "Fully automated prostate segmentation in 3D MR based on normalized gradient fields cross-correlation initialization and LOGISMOS refinement", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831406 (14 February 2012); https://doi.org/10.1117/12.911758

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