Differentiable Boundary Point Extraction for Weakly Supervised Star-shaped Object Segmentation

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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 bound-
ary 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
Original languageEnglish
Pages1
Number of pages11
Publication statusPublished - 2022
EventMedical Imaging with Deep Learning - Zürich, Switzerland
Duration: 6 Jul 20228 Jul 2022
https://2022.midl.io/

Conference

ConferenceMedical Imaging with Deep Learning
Abbreviated titleMIDL
Country/TerritorySwitzerland
CityZürich
Period6/07/228/07/22
Internet address

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