Automatic Segmentation of Supraspinatus Muscle via Bone-Based Localization in Torso Computed Tomography Images Using U-Net
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...
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| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/407c1e3014de4dcbbcda793d43d4dd11 |
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| Sumario: | 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. |
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