dc.contributor.author | Al Arif, SMMR | |
dc.contributor.author | Knapp, K | |
dc.contributor.author | Slabaugh, G | |
dc.date.accessioned | 2018-05-02T09:00:13Z | |
dc.date.issued | 2018-01-12 | |
dc.description.abstract | The cervical spine is a highly flexible anatomy and therefore vulnerable to injuries. Unfortunately, a large number of injuries in lateral cervical X-ray images remain undiagnosed due to human errors. Computer-aided injury detection has the potential to reduce the risk of misdiagnosis. Towards building an automatic injury detection system, in this paper, we propose a deep learning-based fully automatic framework for segmentation of cervical vertebrae in X-ray images. The framework first localizes the spinal region in the image using a deep fully convolutional neural network. Then vertebra centers are localized using a novel deep probabilistic spatial regression network. Finally, a novel shape-aware deep segmentation network is used to segment the vertebrae in the image. The framework can take an X-ray image and produce a vertebrae segmentation result without any manual intervention. Each block of the fully automatic framework has been trained on a set of 124 X-ray images and tested on another 172 images, all collected from real-life hospital emergency rooms. A Dice similarity coefficient of 0.84 and a shape error of 1.69 mm have been achieved. | en_GB |
dc.identifier.citation | Vol. 157, pp. 95 - 111 | en_GB |
dc.identifier.doi | 10.1016/j.cmpb.2018.01.006 | |
dc.identifier.uri | http://hdl.handle.net/10871/32675 | |
dc.language.iso | en | en_GB |
dc.publisher | Elsevier | en_GB |
dc.rights.embargoreason | Under embargo until 12 January 2019 in compliance with publisher policy. | en_GB |
dc.rights | © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0/ | en_GB |
dc.title | Fully automatic cervical vertebrae segmentation framework for X-ray images | en_GB |
dc.type | Article | en_GB |
dc.identifier.issn | 0169-2607 | |
dc.description | This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record. | en_GB |
dc.identifier.journal | Computer Methods and Programs in Biomedicine | en_GB |