Liver segmentation in color images

Burton Ma; T. Peter Kingham; Michael I. Miga; William R. Jarnagin; Amber L. Simpson

doi:10.1117/12.2255393

22 August 2017 Liver segmentation in color images

Burton Ma, T. Peter Kingham, Michael I. Miga, William R. Jarnagin, Amber L. Simpson

Proceedings Volume 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling; 101351O (2017) https://doi.org/10.1117/12.2255393
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

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.

Conference Presentation

Citation Download Citation

Burton Ma, T. Peter Kingham, Michael I. Miga, William R. Jarnagin, and Amber L. Simpson "Liver segmentation in color images", Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101351O (22 August 2017); https://doi.org/10.1117/12.2255393

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available