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Efficient abdominal segmentation on clinically acquired CT with SIMPLE context learning.

, , , , , , and . Medical Imaging: Image Processing, volume 9413 of SPIE Proceedings, page 94130L. SPIE, (2015)

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Multi-atlas segmentation for abdominal organs with Gaussian mixture models., , , , , , and . Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, volume 9417 of SPIE Proceedings, page 941707. SPIE, (2015)Efficient abdominal segmentation on clinically acquired CT with SIMPLE context learning., , , , , , and . Medical Imaging: Image Processing, volume 9413 of SPIE Proceedings, page 94130L. SPIE, (2015)Whole abdominal wall segmentation using Augmented Active Shape Models (AASM) with multi-atlas label fusion and level set., , , , and . Medical Imaging: Image Processing, volume 9784 of SPIE Proceedings, page 97840U. SPIE, (2016)Evaluation of body-wise and organ-wise registrations for abdominal organs., , , , , , , and . Medical Imaging: Image Processing, volume 9784 of SPIE Proceedings, page 97841O. SPIE, (2016)Evaluation of five image registration tools for abdominal CT: pitfalls and opportunities with soft anatomy., , , , , , and . Medical Imaging: Image Processing, volume 9413 of SPIE Proceedings, page 94131N. SPIE, (2015)Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning., , , , , , and . Medical Image Anal., 24 (1): 18-27 (2015)