Liver segmentation in color images Meeting Abstract
| Authors: | Ma, B.; Kingham, T. P.; Miga, M. I.; Jarnagin, W. R.; Simpson, A. L. |
| Abstract Title: | Liver segmentation in color images |
| Meeting Title: | Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling |
| 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. © 2017 SPIE. |
| Keywords: | liver; medical imaging; robotics; segmentation; image processing; image segmentation; semantics; color; liver segmentation; learning algorithms; learning methods; navigation systems; deep learning; color image; color image processing; stereo vision; augmented images; color images; original images; semantic segmentation; training purpose; vision-based navigation system; stereo image processing |
| Journal Title: | Proceedings of SPIE |
| Volume: | 10135 |
| Meeting Dates: | 2017 Feb 11-16 |
| Meeting Location: | Orlando, FL |
| ISSN: | 0277-786X |
| Publisher: | SPIE |
| Date Published: | 2017-08-22 |
| Start Page: | 101351O |
| Language: | English |
| DOI: | 10.1117/12.2255393 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Video Presentation -- Conference Paper -- Export Date: 2 October 2017 -- Source: Scopus |
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