Advanced two-layer level set with a soft distance constraint for dual surfaces segmentation in medical images

Yuanbo Ji; Rob J. van der Geest; Saman Nazarian; Boudewijn P. F. Lelieveldt; Qian Tao

doi:10.1117/12.2293246

2 March 2018 Advanced two-layer level set with a soft distance constraint for dual surfaces segmentation in medical images

Yuanbo Ji, Rob J. van der Geest, Saman Nazarian, Boudewijn P. F. Lelieveldt, Qian Tao

Author Affiliations +

Proceedings Volume 10574, Medical Imaging 2018: Image Processing; 105743B (2018) https://doi.org/10.1117/12.2293246
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Anatomical objects in medical images very often have dual contours or surfaces that are highly correlated. Manually segmenting both of them by following local image details is tedious and subjective. In this study, we proposed a two-layer region-based level set method with a soft distance constraint, which not only regularizes the level set evolution at two levels, but also imposes prior information on wall thickness in an effective manner. By updating the level set function and distance constraint functions alternatingly, the method simultaneously optimizes both contours while regularizing their distance. The method was applied to segment the inner and outer wall of both left atrium (LA) and left ventricle (LV) from MR images, using a rough initialization from inside the blood pool. Compared to manual annotation from experience observers, the proposed method achieved an average perpendicular distance (APD) of less than 1mm for the LA segmentation, and less than 1.5mm for the LV segmentation, at both inner and outer contours. The method can be used as a practical tool for fast and accurate dual wall annotations given proper initialization.

Citation Download Citation

Yuanbo Ji, Rob J. van der Geest, Saman Nazarian, Boudewijn P. F. Lelieveldt, and Qian Tao "Advanced two-layer level set with a soft distance constraint for dual surfaces segmentation in medical images", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105743B (2 March 2018); https://doi.org/10.1117/12.2293246

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