Leveraging Probabilistic Segmentation Models for Improved Glaucoma Diagnosis: A Clinical Pipeline Approach

Anna M. Wundram, Paul Fischer, Stephan Wunderlich, Hanna Faber, Lisa M. Koch, Philipp Berens, Christian F. Baumgartner
Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning, PMLR 250:1725-1740, 2024.

Abstract

The accurate segmentation of the optic cup and disc in fundus images is essential for diagnostic processes such as glaucoma detection. The inherent ambiguity in locating these structures often poses a significant challenge, leading to potential misdiagnosis. To model such ambiguities, numerous probabilistic segmentation models have been proposed. In this paper, we investigate the integration of these probabilistic segmentation models into a multistage pipeline closely resembling clinical practice. Our findings indicate that leveraging the uncertainties provided by these models substantially enhances the quality of glaucoma diagnosis compared to relying on a single segmentation only.

Cite this Paper

BibTeX
@InProceedings{pmlr-v250-wundram24a, title = {Leveraging Probabilistic Segmentation Models for Improved Glaucoma Diagnosis: A Clinical Pipeline Approach}, author = {Wundram, Anna M. and Fischer, Paul and Wunderlich, Stephan and Faber, Hanna and Koch, Lisa M. and Berens, Philipp and Baumgartner, Christian F.}, booktitle = {Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning}, pages = {1725--1740}, year = {2024}, editor = {Burgos, Ninon and Petitjean, Caroline and Vakalopoulou, Maria and Christodoulidis, Stergios and Coupe, Pierrick and Delingette, Hervé and Lartizien, Carole and Mateus, Diana}, volume = {250}, series = {Proceedings of Machine Learning Research}, month = {03--05 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v250/main/assets/wundram24a/wundram24a.pdf}, url = {https://proceedings.mlr.press/v250/wundram24a.html}, abstract = {The accurate segmentation of the optic cup and disc in fundus images is essential for diagnostic processes such as glaucoma detection. The inherent ambiguity in locating these structures often poses a significant challenge, leading to potential misdiagnosis. To model such ambiguities, numerous probabilistic segmentation models have been proposed. In this paper, we investigate the integration of these probabilistic segmentation models into a multistage pipeline closely resembling clinical practice. Our findings indicate that leveraging the uncertainties provided by these models substantially enhances the quality of glaucoma diagnosis compared to relying on a single segmentation only.} }
Endnote
%0 Conference Paper %T Leveraging Probabilistic Segmentation Models for Improved Glaucoma Diagnosis: A Clinical Pipeline Approach %A Anna M. Wundram %A Paul Fischer %A Stephan Wunderlich %A Hanna Faber %A Lisa M. Koch %A Philipp Berens %A Christian F. Baumgartner %B Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2024 %E Ninon Burgos %E Caroline Petitjean %E Maria Vakalopoulou %E Stergios Christodoulidis %E Pierrick Coupe %E Hervé Delingette %E Carole Lartizien %E Diana Mateus %F pmlr-v250-wundram24a %I PMLR %P 1725--1740 %U https://proceedings.mlr.press/v250/wundram24a.html %V 250 %X The accurate segmentation of the optic cup and disc in fundus images is essential for diagnostic processes such as glaucoma detection. The inherent ambiguity in locating these structures often poses a significant challenge, leading to potential misdiagnosis. To model such ambiguities, numerous probabilistic segmentation models have been proposed. In this paper, we investigate the integration of these probabilistic segmentation models into a multistage pipeline closely resembling clinical practice. Our findings indicate that leveraging the uncertainties provided by these models substantially enhances the quality of glaucoma diagnosis compared to relying on a single segmentation only.
APA
Wundram, A.M., Fischer, P., Wunderlich, S., Faber, H., Koch, L.M., Berens, P. & Baumgartner, C.F.. (2024). Leveraging Probabilistic Segmentation Models for Improved Glaucoma Diagnosis: A Clinical Pipeline Approach. Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 250:1725-1740 Available from https://proceedings.mlr.press/v250/wundram24a.html.

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