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Proceedings Paper

Liver segmentation in color images
Author(s): Burton Ma; T. Peter Kingham; Michael I. Miga; William R. Jarnagin; Amber L. Simpson
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Paper Abstract

We describe the use of a deep learning method for semantic segmentation of the liver from color images. Our intent is to eventually embed a semantic segmentation method into a stereo-vision based navigation system for open liver surgery. Semantic segmentation of the stereo images will allow us to reconstruct a point cloud containing the liver surfaces and excluding all other non-liver structures. We trained a deep learning algorithm using 136 images and 272 augmented images computed by rotating the original images. We tested the trained algorithm on 27 images that were not used for training purposes. The method achieves an 88% median pixel labeling accuracy over the test images.

Paper Details

Date Published: 22 August 2017
PDF: 6 pages
Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101351O (22 August 2017); doi: 10.1117/12.2255393
Show Author Affiliations
Burton Ma, York Univ. (Canada)
T. Peter Kingham, Memorial Sloan-Kettering Cancer Ctr. (United States)
Michael I. Miga, Vanderbilt Univ. (United States)
William R. Jarnagin, Memorial Sloan-Kettering Cancer Ctr. (United States)
Amber L. Simpson, Memorial Sloan-Kettering Cancer Ctr. (United States)


Published in SPIE Proceedings Vol. 10135:
Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling
Robert J. Webster; Baowei Fei, Editor(s)

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