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Spinal vertebrae segmentation and localization by transfer learning
Author(s): Jiashi Zhao; Zhengang Jiang; Kensaku Mori; Liyuan Zhang; Wei He; Weili Shi; Yu Miao; Fei Yan; Fei He
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Paper Abstract

Spine curvature disorders have been found relevant as the nervous system diseases and may produce serious disturbances of the whole body. The ability to automatically segment and locate the spinal vertebrae is, therefore, an important task for modern studies of the spinal curvature disorders detection. In this work, we devise a modern, simple and automated human spinal vertebrae segmentation and localization method using transfer learning, that works on CT and MRI acquisitions. We exploit pre-trained models to spinal vertebrae segmentation and localization problem. We first explore and evaluate different medical imaging architectures and choose the deep dilated convolutions as the initialization for our spinal vertebrae segmentation and localization task. Then we conduct the pre-trained model from spinal cord gray matter dataset to our spinal vertebrae segmentation task with supervised fine-tuning. The vertebral centroid coordinate can be computed from the segmented result, and the centroid localization error is used as the feedback for fine-tuning. We evaluate our method against traditional method on medical image segmentation and localization task and report the comparison of evaluation metrics. We show the qualitative and quantitative evaluation on spine CT images which are from spine CT volumes on the publicity platform SpineWeb. The evaluation results show that our approach was able to capture many properties of the spinal vertebrae, and provided good segmentation and localization performance. From our research we show that the deep dilated convolutions pre-trained on MRI spinal cord gray matter images can be transfer to process CT spinal vertebrae images.

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095023 (13 March 2019); doi: 10.1117/12.2512675
Show Author Affiliations
Jiashi Zhao, Changchun Univ. of Science and Technology (China)
Zhengang Jiang, Changchun Univ. of Science and Technology (China)
Kensaku Mori, Nagoya Univ. (Japan)
Liyuan Zhang, Changchun Univ. of Science and Technology (China)
Wei He, Changchun Univ. of Science and Technology (China)
Weili Shi, Changchun Univ. of Science and Technology (China)
Yu Miao, Changchun Univ. of Science and Technology (China)
Fei Yan, Changchun Univ. of Science and Technology (China)
Fei He, Changchun Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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