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

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.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Deep learning for semantic segmentation of organs and tissues in laparoscopic surgery ; volume:6 ; number:1 ; year:2020 ; extent:05 ; Current directions in biomedical engineering ; 6, Heft 1 (2020) (gesamt 05)

Contributors, Places and Time

• Creator: Scheikl, Paul Maria ; Laschewski, Stefan ; Kisilenko, Anna ; Davitashvili, Tornike ; Müller, Benjamin ; Capek, Manuela ; Müller, Beat P. ; Wagner, Martin ; Mathis-Ullrich, Franziska

Further information

• DOI: 10.1515/cdbme-2020-0016
• URN: urn:nbn:de:101:1-2410141659583.088943378895
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:30 AM CEST