Differentiable Boundary Point Extraction for Weakly Supervised Star-shaped Object Segmentation
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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.
Original language | English |
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Journal | Proceedings of Machine Learning Research |
Volume | 172 |
Pages (from-to) | 188-198 |
ISSN | 2640-3498 |
Publication status | Published - 2022 |
Event | 5th International Conference on Medical Imaging with Deep Learning, MIDL 2022 - Zurich, Switzerland Duration: 6 Jul 2022 → 8 Jul 2022 |
Conference
Conference | 5th International Conference on Medical Imaging with Deep Learning, MIDL 2022 |
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Country | Switzerland |
City | Zurich |
Period | 06/07/2022 → 08/07/2022 |
Bibliographical note
Funding Information:
This work was funded by Netherlands Organisation for Scientific Research (NWO) VICI project VI.C.182.042.
Publisher Copyright:
© 2022 H. Kervadec, D. Bos & M. de Bruijne.
- Carotid artery stenosis, Image segmentation, weak annotations
Research areas
Links
- https://proceedings.mlr.press/v172/camarasa22a.html
Final published version
ID: 375726251