Leveraging epistemic uncertainty to improve tumour segmentation in breast MRI: an exploratory analysis

Smriti Joshi; Richard Osuala; Lidia Garrucho; Apostolia Tsirikoglou; Javier del Riego; Katarzyna Gwoździewicz; Kaisar Kushibar; Oliver Diaz; Karim Lekadir

doi:10.1117/12.3006783

2 April 2024 Leveraging epistemic uncertainty to improve tumour segmentation in breast MRI: an exploratory analysis

Smriti Joshi, Richard Osuala, Lidia Garrucho, Apostolia Tsirikoglou, Javier del Riego, Katarzyna Gwoździewicz, Kaisar Kushibar, Oliver Diaz, Karim Lekadir

Author Affiliations +

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 1292616 (2024) https://doi.org/10.1117/12.3006783
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Medical image segmentation has improved with deep-learning methods, especially for tumor segmentation. However, variability in tumor shapes, sizes, and enhancement remains a challenge. Breast MRI adds further uncertainty due to anatomical differences. Informing clinicians about result reliability and using model uncertainty to improve predictions are essential. We study Monte-Carlo Dropout for generating multiple predictions and finding consensus segmentation. Our approach reduces false positives using per-pixel uncertainty and improves segmentation metrics. In addition, we study the correlation of model performance to the perceived ease of manual segmentation. Finally, we compare the per-pixel uncertainty with the inter-rater variability as segmented by six different radiologists. Our code is available at https://github.com/smriti-joshi/uncertainty-segmentation-mcdropout.git.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Smriti Joshi, Richard Osuala, Lidia Garrucho, Apostolia Tsirikoglou, Javier del Riego, Katarzyna Gwoździewicz, Kaisar Kushibar, Oliver Diaz, and Karim Lekadir "Leveraging epistemic uncertainty to improve tumour segmentation in breast MRI: an exploratory analysis", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 1292616 (2 April 2024); https://doi.org/10.1117/12.3006783

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