Automatic segmentation and labelling of wrist bones in four-dimensional computed tomography datasets via deep learning-Reference-Cited by-同舟云学术

Automatic segmentation and labelling of wrist bones in four-dimensional computed tomography datasets via deep learning

Published:2023-10-26 Issue: Volume: Page:
ISSN:1753-1934
Container-title:Journal of Hand Surgery (European Volume)
language:en
Short-container-title:J Hand Surg Eur Vol

Author:

Teule E. H. S.¹²^ORCID,Lessmann N.³,van der Heijden E. P. A.²⁴,Hummelink S.²

Affiliation:

1. Technical Medicine, University of Twente, Enschede, The Netherlands

2. Department of Plastic, Reconstructive, and Hand Surgery, Radboud University Medical Center, Nijmegen, The Netherlands

3. Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands

4. Department of Plastic, Reconstructive, and Hand Surgery, Jeroen Bosch Hospital, ‘s-Hertogenbosch, The Netherlands

Abstract

This study developed a deep learning model for fully automatic segmentation and labelling of wrist bones from four-dimensional computed tomography (4DCT) scans. This is a crucial step towards implementing 4DCT for diagnosing wrist ligament lesions, reducing time-consuming analysis of extensive data.

Publisher

SAGE Publications

Subject

Surgery

Link

http://journals.sagepub.com/doi/pdf/10.1177/17531934231209876

Reference4 articles.

1. Automatic Segmentation of Wrist Bones in CT Using a Statistical Wrist Shape $+$ Pose Model

2. CSR-Net: Cross-Scale Residual Network for multi-objective scaphoid fracture segmentation

3. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

4. The use of 4D-CT in assessing wrist kinematics and pathology

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