| Autor: |
Scheikl Paul Maria, Laschewski Stefan, Kisilenko Anna, Davitashvili Tornike, Müller Benjamin, Capek Manuela, Müller-Stich Beat P., Wagner Martin, Mathis-Ullrich Franziska |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Current Directions in Biomedical Engineering, Vol 6, Iss 1, Pp 1-11 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2364-5504 |
| DOI: |
10.1515/cdbme-2020-0016 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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