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Intervertebral Disc Labeling with Learning Shape Information, a Look once Approach

Azad R, Heidari M, Cohen-Adad J, Adeli E, Merhof D (2022)

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Publication Type: Conference contribution

Publication year: 2022

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 13564 LNCS

Pages Range: 49-59

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Virtual, Online

ISBN: 9783031169182

DOI: 10.1007/978-3-031-16919-9_5

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 (Figure presented.).

Involved external institutions

How to cite

APA:

Azad, R., Heidari, M., Cohen-Adad, J., Adeli, E., & Merhof, D. (2022). Intervertebral Disc Labeling with Learning Shape Information, a Look once Approach. In Islem Rekik, Ehsan Adeli, Sang Hyun Park, Celia Cintas (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 49-59). Virtual, Online: Springer Science and Business Media Deutschland GmbH.

MLA:

Azad, Reza, et al. "Intervertebral Disc Labeling with Learning Shape Information, a Look once Approach." Proceedings of the 5th International Workshop on Predictive Intelligence in Medicine, PRIME 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Virtual, Online Ed. Islem Rekik, Ehsan Adeli, Sang Hyun Park, Celia Cintas, Springer Science and Business Media Deutschland GmbH, 2022. 49-59.

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