Abstract
Accurate and automatic segmentation of intervertebral discs from medical images is a critical task for the assessment of spine-related diseases such as osteoporosis, vertebral fractures, and intervertebral disc herniation. To date, various approaches have been developed in the literature which routinely rely on detecting the discs as the primary step for detecting abnormality in intervertebral Discs. A disadvantage of many cohort studies is that the localization algorithm also yields to false positive detections. In this study, we aim to alleviate this problem by proposing a novel U-Net-based structure to predict a set of candidates for intervertebral disc locations. In our design, we integrate the image shape information (image gradients) to encourage the model to learn rich and generic geometrical information. This additional signal guides the model to selectively emphasize the contextual representation and to supress the less discriminative features. On the post-processing side, to further decrease the false positive rate, we propose a permutation invariant “look once” model, which accelerates the candidate recovery procedure. In comparison with previous studies, our proposed approach does not need to perform the selection in an iterative fashion. The proposed method was evaluated on the spine generic public multi-center dataset and demonstrated superior performance compared to previous work. The codes is publicly available at
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Azad, R., Heidari, M., Cohen-Adad, J., Adeli, E., Merhof, D. (2022). Intervertebral Disc Labeling with Learning Shape Information, a Look once Approach. In: Rekik, I., Adeli, E., Park, S.H., Cintas, C. (eds) Predictive Intelligence in Medicine. PRIME 2022. Lecture Notes in Computer Science, vol 13564. Springer, Cham. https://doi.org/10.1007/978-3-031-16919-9_5
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