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

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/deep-learning-for-semantic-segmentation-of-organs-and-tissues-in-laparoscopic/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Paul Maria Scheikl,Stefan Laschewski,Anna Kisilenko,Tornike Davitashvili,Benjamin Müller,Manuela Capek,Beat P. Müller‐Stich,Martin Wagner,Franziska Mathis-Ullrich |
| Citations | 41 |
| DOI | 10.1515/cdbme-2020-0016 |
| Fields | Computer Science,Engineering |
| Open Access | true |
| OA Status | gold |
| OA URL | https://www.degruyter.com/document/doi/10.1515/cdbme-2020-0016/pdf |
| OpenAlex ID | https://openalex.org/W3087393366 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Anna Kisilenko](https://scholariq.org/researchers/anna-kisilenko/)
- [Tornike Davitashvili](https://scholariq.org/researchers/tornike-davitashvili/)

## Paper journal

- [Current Directions in Biomedical Engineering](https://scholariq.org/journals/current-directions-in-biomedical-engineering/)

## Paper primary topic

- [Medical Image Segmentation Techniques](https://scholariq.org/topics/medical-image-segmentation-techniques/)

## Paper topics

- [Medical Image Segmentation Techniques](https://scholariq.org/topics/medical-image-segmentation-techniques/)
- [Advanced Neural Network Applications](https://scholariq.org/topics/advanced-neural-network-applications/)
- [Medical Imaging and Analysis](https://scholariq.org/topics/medical-imaging-and-analysis/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.