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13 March 2019 Spinal vertebrae segmentation and localization by transfer learning
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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.
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Jiashi Zhao, Zhengang Jiang, Kensaku Mori, Liyuan Zhang, Wei He, Weili Shi, Yu Miao, Fei Yan, and Fei He "Spinal vertebrae segmentation and localization by transfer learning", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095023 (13 March 2019); https://doi.org/10.1117/12.2512675
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