Automated segmentation of hip and thigh muscles in metal artifact contaminated CT using CNN

Mitsuki Sakamoto; Yuta Hiasa; Yoshito Otake; Masaki Takao; Yuki Suzuki; Nobuhiko Sugano; Yoshinobu Sato

doi:10.1117/12.2521440

27 March 2019 Automated segmentation of hip and thigh muscles in metal artifact contaminated CT using CNN

Mitsuki Sakamoto, Yuta Hiasa, Yoshito Otake, Masaki Takao, Yuki Suzuki, Nobuhiko Sugano, Yoshinobu Sato

Author Affiliations +

Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 110500S (2019) https://doi.org/10.1117/12.2521440
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

Abstract

In total hip arthroplasty, analysis of postoperative images is important to evaluate surgical outcome. Since CT is most prevalent modality in orthopedic surgery, we aimed at the analysis of CT image. The challenge in this work is the metal artifact in postoperative CT caused by the metallic implant, which reduces the accuracy of segmentation especially in the vicinity of the implant. Our goal was to develop an automated segmentation method of the muscles in the postoperative CT images. In this paper, we propose a method that combines Normalized Metal Artifact Reduction (NMAR), which is one of the state-of-the-art metal artifact reduction methods, and a CNN- based segmentation using the U-Net architecture. We conducted experiments using simulated images and real images of the lower extremity to evaluate the segmentation accuracy of 19 muscles that are contaminated with metallic artifact. The training dataset we used is 20 CTs that were manually traced by an expert surgeon. In simulation study, the proposed method improved the average symmetric surface distance (ASD) from 1.85 ± 1.63 mm to 1.24 ± 0.67 mm (mean ± std). The real image study using two CTs with the ground truth of gluteus maximus, medius and minimus muscles showed the reduction of ASD from 1.67 ± 0.40 mm to 1.52 ± 0.47 mm. Our future work includes the end-to-end convolutional neural network for metal artifact reduction and musculoskeltal segmentation and to establish a ground truth dataset by performing non-rigid registration between the postoperative and preoperative CT of the same patient.

Citation Download Citation

Mitsuki Sakamoto, Yuta Hiasa, Yoshito Otake, Masaki Takao, Yuki Suzuki, Nobuhiko Sugano, and Yoshinobu Sato "Automated segmentation of hip and thigh muscles in metal artifact contaminated CT using CNN", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500S (27 March 2019); https://doi.org/10.1117/12.2521440

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