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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AbstractAccurate 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.
All Author(s) ListChen H, Shen CY, Qin J, Ni D, Shi L, Cheng JCY, Heng PA
Name of Conference18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
Start Date of Conference05/10/2015
End Date of Conference09/10/2015
Place of ConferenceMunich
Country/Region of ConferenceGermany
Journal nameLecture Notes in Artificial Intelligence
Detailed descriptionMICCAI
Year2015
Month1
Day1
Volume Number9349
PublisherSPRINGER INT PUBLISHING AG
Pages515 - 522
ISBN978-3-319-24552-2
eISBN978-3-319-24553-9
ISSN0302-9743
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Radiology, Nuclear Medicine & Medical Imaging

Last updated on 2021-21-10 at 00:54