# Deep-learning-based semantic segmentation of autonomic nerves from laparoscopic images of colorectal surgery: an experimental pilot study

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/deep-learning-based-semantic-segmentation-of-autonomic-nerves-from-laparoscopic/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Shigehiro Kojima,Daichi Kitaguchi,Takahiro Igaki,Kei Nakajima,Yuto Ishikawa,Yuriko Harai,Atsushi Yamada,Younae Lee,Kazuyuki Hayashi,Norihito Kosugi,Hiro Hasegawa,Masaaki Ito |
| Citations | 43 |
| DOI | 10.1097/js9.0000000000000317 |
| Fields | Medicine |
| Open Access | true |
| OA Status | hybrid |
| OA URL | https://doi.org/10.1097/js9.0000000000000317 |
| OpenAlex ID | https://openalex.org/W4362459080 |
| PMID | 36999784 |
| Type | article |
| Year | 2023 |

## Paper authors

- [Daichi Kitaguchi](https://scholariq.org/researchers/daichi-kitaguchi/)

## Paper journal

- [International Journal of Surgery](https://scholariq.org/journals/international-journal-of-surgery/)

## Paper primary topic

- [Surgical Simulation and Training](https://scholariq.org/topics/surgical-simulation-and-training/)

## Paper topics

- [Surgical Simulation and Training](https://scholariq.org/topics/surgical-simulation-and-training/)
- [Colorectal Cancer Surgical Treatments](https://scholariq.org/topics/colorectal-cancer-surgical-treatments/)
- [Enhanced Recovery After Surgery](https://scholariq.org/topics/enhanced-recovery-after-surgery/)

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