FUN-SIS: A Fully UNsupervised approach for Surgical Instrument Segmentation-Reference-Cited by-同舟云学术

FUN-SIS: A Fully UNsupervised approach for Surgical Instrument Segmentation

Published:2023-04 Issue: Volume:85 Page:102751
ISSN:1361-8415
Container-title:Medical Image Analysis
language:en
Short-container-title:Medical Image Analysis

Author:

Sestini Luca^ORCID,Rosa Benoit,De Momi Elena,Ferrigno Giancarlo,Padoy Nicolas

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference73 articles.

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1. Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art;Computers in Biology and Medicine;2024-02

2. 3D surgical instrument collection for computer vision and extended reality;Scientific Data;2023-11-11

3. MFF‐Net: Multiscale feature fusion semantic segmentation network for intracranial surgical instruments;The International Journal of Medical Robotics and Computer Assisted Surgery;2023-11-06

4. InstrumentNet: An integrated model for real-time segmentation of intracranial surgical instruments;Computers in Biology and Medicine;2023-11

5. Visual-Kinematics Graph Learning for Procedure-Agnostic Instrument Tip Segmentation in Robotic Surgeries;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01