Abstract
The cervical spine is a flexible anatomy and vulnerable to injury, which may go unnoticed during a radiological exam. Towards building an automatic injury detection system, we propose a localization framework for the cervical spine in X-ray images. The proposed framework employs a segmentation approach to solve the localization problem. As the cervical spine is a single connected component, we introduce a novel region-aware loss function for training a deep segmentation network that penalises disjoint predictions. Using data augmentation, the framework has been trained on a dataset of 124 images and tested on another 124 images, all collected from real life medical emergency rooms. The results show a significant improvement in performance over the previous state-of-the-art cervical vertebrae localization framework.
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Al Arif, S.M.M.R., Knapp, K., Slabaugh, G. (2017). Region-Aware Deep Localization Framework for Cervical Vertebrae in X-Ray Images. In: Cardoso, M., et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham. https://doi.org/10.1007/978-3-319-67558-9_9
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DOI: https://doi.org/10.1007/978-3-319-67558-9_9
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