Keywords: Image segmentation, Weak annotations, Carotid artery stenosis
TL;DR: In this paper we differentiably extract boundary points to supervise star-shaped object segmentation using diameter annotations.
Abstract: Although Deep Learning is the new gold standard in medical image segmentation, the annotation burden limits its expansion to clinical practice. We also observe a mismatch between annotations required by deep learning methods designed with pixel-wise optimization in mind and clinically relevant annotations designed for biomarkers extraction (diameters, counts, etc.). Our study proposes a first step toward bridging this gap, optimizing vessel segmentation based on its diameter annotations. To do so we propose to extract boundary points from a star-shaped segmentation in a differentiable manner. This differentiable extraction allows reducing annotation burden as instead of the pixel-wise segmentation only the two annotated points required for diameter measurement are used for training the model. Our experiments show that training based on diameter is efficient; produces state-of-the-art weakly supervised segmentation; and performs reasonably compared to full supervision. Our code is publicly available: https://gitlab.com/radiology/aim/carotid-artery-image-analysis/diameter-learning
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Paper Type: methodological development
Primary Subject Area: Segmentation
Secondary Subject Area: Learning with Noisy Labels and Limited Data
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Code And Data: code: https://gitlab.com/radiology/aim/carotid-artery-image-analysis/diameter-learning data: https://vessel-wall-segmentation.grand-challenge.org/