Deep learning for semantic segmentation of organs and tissues in laparoscopic surgery

Scheikl PM, Laschewski S, Kisilenko A, Davitashvili T, Müller B, Capek M, Müller-Stich BP, Wagner M, Mathis-Ullrich F (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Current Directions in Biomedical Engineering De Gruyter

Book Volume: 6

Article Number: 20200016

Journal Issue: 1

DOI: 10.1515/cdbme-2020-0016

Abstract

Semantic segmentation of organs and tissue types is an important sub-problem in image based scene understanding for laparoscopic surgery and is a prerequisite for context-aware assistance and cognitive robotics. Deep Learning (DL) approaches are prominently applied to segmentation and tracking of laparoscopic instruments. This work compares different combinations of neural networks, loss functions, and training strategies in their application to semantic segmentation of different organs and tissue types in human laparoscopic images in order to investigate their applicability as components in cognitive systems. TernausNet-11 trained on Soft-Jaccard loss with a pretrained, trainable encoder performs best in regard to segmentation quality (78.31% mean Intersection over Union [IoU]) and inference time (28.07 ms) on a single GTX 1070 GPU.

Authors with CRIS profile

Paul Scheikl
Franziska Mathis-Ullrich

Involved external institutions

Karlsruhe Institute of Technology (KIT) DE Germany (DE) Universitätsklinikum Heidelberg DE Germany (DE)

How to cite

APA:

Scheikl, P.M., Laschewski, S., Kisilenko, A., Davitashvili, T., Müller, B., Capek, M.,... Mathis-Ullrich, F. (2020). Deep learning for semantic segmentation of organs and tissues in laparoscopic surgery. Current Directions in Biomedical Engineering, 6(1). https://doi.org/10.1515/cdbme-2020-0016

MLA:

Scheikl, Paul Maria, et al. "Deep learning for semantic segmentation of organs and tissues in laparoscopic surgery." Current Directions in Biomedical Engineering 6.1 (2020).

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