Automatic Localization and Identification of Vertebrae in Spine CT via a Joint Learning Model with Deep Neural Networks
Refereed conference paper presented and published in conference proceedings


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摘要Accurate localization and identification of vertebrae in 3D spinal images is essential for many clinical tasks. However, automatic localization and identification of vertebrae remains challenging due to similar appearance of vertebrae, abnormal pathological curvatures and image artifacts induced by surgical implants. Traditional methods relying on hand-crafted low level features and/or a priori knowledge usually fail to overcome these challenges on arbitrary CT scans. We present a robust and efficient approach to automatically locating and identifying vertebrae in 3D CT volumes by exploiting high level feature representations with deep convolutional neural network (CNN). A novel joint learning model with CNN (J-CNN) is proposed by considering both the appearance of vertebrae and the pairwise conditional dependency of neighboring vertebrae. The J-CNN can effectively identify the type of vertebra and eliminate false detections based on a set of coarse vertebral centroids generated from a random forest classifier. Furthermore, the predicted centroids are refined by a shape regression model. Our approach was quantitatively evaluated on the dataset of MICCAI 2014 Computational Challenge on Vertebrae Localization and Identification. Compared with the state-of-the-art method [1], our approach achieved a large margin with 10.12% improvement of the identification rate and smaller localization errors.
著者Chen H, Shen CY, Qin J, Ni D, Shi L, Cheng JCY, Heng PA
會議名稱18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
會議開始日05.10.2015
會議完結日09.10.2015
會議地點Munich
會議國家/地區德國
期刊名稱Lecture Notes in Artificial Intelligence
詳細描述MICCAI
出版年份2015
月份1
日期1
卷號9349
出版社SPRINGER INT PUBLISHING AG
頁次515 - 522
國際標準書號978-3-319-24552-2
電子國際標準書號978-3-319-24553-9
國際標準期刊號0302-9743
語言英式英語
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Radiology, Nuclear Medicine & Medical Imaging

上次更新時間 2020-27-11 於 01:27