Open AccessAutomatic Segmentation of Supraspinatus Muscle via Bone-Based Localization in Torso Computed Tomography Images Using U-Net
Author(s) -
Yuichi Wakamatsu,
Naoki Kamiya,
Xiangrong Zhou,
Hiroki Kato,
Takeshi Hara,
Hiroshi Fujita
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3127565
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The supraspinatus tendon is the most frequently torn tendon in the rotator cuff. Rotator cuff reconstruction is more likely to result in retear if the muscle has atrophy or fatty degeneration. Thus, atrophy and fatty degeneration of the supraspinatus muscle are predictors of the postoperative course, and volume analysis using three-dimensional segmentation of the supraspinatus muscle is necessary. The supraspinatus muscle is attached to the scapula, making it possible to estimate the region of the muscle based on the position of the scapula. In this paper, we propose a supraspinatus muscle segmentation method based on the scapula position in torso computed tomography (CT) images. Our proposed method consists of supraspinatus muscle localization using a scapula segmentation result and supraspinatus muscle segmentation based on the localization result. U-Net is used for scapula and supraspinatus muscle segmentation. In this experiment, we used torso CT images and pseudo-chest CT images which were generated from the scans of the same patient. The mean Dice values of the segmentation results obtained by applying the proposed method to the torso and pseudo-chest CT images were both 0.881. When localization was not used, the mean Dice values of the segmentation results in the torso and pseudo-chest CT images were 0.000 and 0.850, respectively. The experimental results demonstrate the effectiveness of bone-based localization in supraspinatus muscle segmentation using U-Net.