Research Progress and Prospects of Minimally Invasive Surgical Instrument Segmentation Methods Based on Artificial Intelligence.
10.12455/j.issn.1671-7104.240436
- Author:
Weimin CHENG
1
;
Xiaohua WU
2
;
Jing XIONG
1
Author Information
1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen,
2. Shenzhen Mindray Bio-Medical Electronics Co., Ltd., Shenzhen,
- Publication Type:Journal Article
- Keywords:
artificial intelligence;
instance segmentation;
instrument segmentation;
minimally invasive surgery;
semantic segmentation
- MeSH:
Artificial Intelligence;
Minimally Invasive Surgical Procedures/instrumentation*;
Algorithms;
Deep Learning;
Humans;
Image Processing, Computer-Assisted
- From:
Chinese Journal of Medical Instrumentation
2025;49(1):15-23
- CountryChina
- Language:Chinese
-
Abstract:
With the development of artificial intelligence technology and the growing demand for minimally invasive surgery, the intelligentization of minimally invasive surgery has become a current research hotspot. Surgical instrument segmentation is a highly promising technology that can enhance the performance of minimally invasive endoscopic imaging systems, surgical video analysis systems, and other related systems. This article summarizes the semantic and instance segmentation methods of minimally invasive surgical instruments based on deep learning, deeply analyzes the supervision methods of training algorithms, network structure improvements, and attention mechanisms, and then discusses the methods based on the Segment Anything Model. Given that deep learning methods have extremely high requirements for data, current data augmentation methods have also been explored. Finally, a summary and outlook on instrument segmentation technology are provided.
