Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach
Reza Azad, Moein Heidari, Julien Cohen-Adad, Ehsan Adeli, Dorit Merhof

TL;DR
This paper introduces a novel U-Net-based method incorporating shape information and a 'look once' approach for automatic intervertebral disc segmentation, reducing false positives and improving efficiency.
Contribution
It proposes a new shape-aware U-Net architecture combined with a permutation invariant 'look once' model for faster, more accurate disc detection without iterative selection.
Findings
Superior performance on multi-center dataset
Reduced false positive rate
Faster candidate recovery process
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 relies on detecting the discs as the primary step. A disadvantage of many cohort studies is that the localization algorithm also yields 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 suppress the less…
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