Hyperspectral image segmentation of the common bile duct

Daniel Samarov; Eleanor Wehner; Roderich Schwarz; Karel Zuzak; Edward Livingston

doi:10.1117/12.2005612

8 March 2013 Hyperspectral image segmentation of the common bile duct

Daniel Samarov, Eleanor Wehner, Roderich Schwarz, Karel Zuzak, Edward Livingston

Proceedings Volume 8618, Emerging Digital Micromirror Device Based Systems and Applications V; 861807 (2013) https://doi.org/10.1117/12.2005612
Event: SPIE MOEMS-MEMS, 2013, San Francisco, California, United States

Abstract

Over the course of the last several years hyperspectral imaging (HSI) has seen increased usage in biomedicine. Within the medical field in particular HSI has been recognized as having the potential to make an immediate impact by reducing the risks and complications associated with laparotomies (surgical procedures involving large incisions into the abdominal wall) and related procedures. There are several ongoing studies focused on such applications. Hyperspectral images were acquired during pancreatoduodenectomies (commonly referred to as Whipple procedures), a surgical procedure done to remove cancerous tumors involving the pancreas and gallbladder. As a result of the complexity of the local anatomy, identifying where the common bile duct (CBD) is can be difficult, resulting in comparatively high incidents of injury to the CBD and associated complications. It is here that HSI has the potential to help reduce the risk of such events from happening. Because the bile contained within the CBD exhibits a unique spectral signature, we are able to utilize HSI segmentation algorithms to help in identifying where the CBD is. In the work presented here we discuss approaches to this segmentation problem and present the results.

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Daniel Samarov, Eleanor Wehner, Roderich Schwarz, Karel Zuzak, and Edward Livingston "Hyperspectral image segmentation of the common bile duct", Proc. SPIE 8618, Emerging Digital Micromirror Device Based Systems and Applications V, 861807 (8 March 2013); https://doi.org/10.1117/12.2005612

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KEYWORDS

Image segmentation

Hyperspectral imaging

Liver

Detection and tracking algorithms

Image processing algorithms and systems

Surgery

Digital photography

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