Utility of a novel integrated deep convolutional neural network for the segmentation of hip joint from computed tomography images in the preoperative planning of total hip arthroplasty.

Journal: Journal of orthopaedic surgery and research

Published Date: Mar 15, 2022

Abstract

PURPOSE: Preoperative three-dimensional planning is important for total hip arthroplasty. To simulate the placement of joint implants on computed tomography (CT), pelvis and femur must be segmented. Accurate and rapid segmentation of the hip joint is challenging. This study aimed to develop a novel deep learning network, named Changmugu Net (CMG Net), which could achieve accurate segmentation of the femur and pelvis.

Authors

Dong Wu
Xin Zhi

Senior Department of Orthopedics, The Fourth Medical Center of PLA General Hospital, Beijing, China.
Xingyu Liu

First People's Hospital of Zunyi City, Zunyi, China.
Yiling Zhang

Department of Otolaryngology Head and Neck Surgery，the Second Xiangya Hospital，Central South University，Changsha，410011，China.
Wei Chai

Department of Orthopaedics, Chinese People's Liberation Army General Hospital (301 Hospital), 28 Fuxing Rd, 100853, Beijing, China.

Keywords

Aged Aged, 80 and over Arthroplasty, Replacement, Hip Female Hip Joint Humans Male Middle Aged Neural Networks, Computer Pelvis Preoperative Period Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (35292056)

Utility of a novel integrated deep convolutional neural network for the segmentation of hip joint from computed tomography images in the preoperative planning of total hip arthroplasty.

Abstract

Authors

Keywords

External Resources

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