In:
Current Directions in Biomedical Engineering, Walter de Gruyter GmbH, Vol. 6, No. 1 ( 2020-09-17)
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.
Type of Medium:
Online Resource
ISSN:
2364-5504
DOI:
10.1515/cdbme-2020-0016
Language:
English
Publisher:
Walter de Gruyter GmbH
Publication Date:
2020
detail.hit.zdb_id:
2835398-5