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

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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.

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Versió postprint de la comunicació publicada a: https://doi.org/10.1117/12.3006783

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Comunicació a: Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 1292616 (2 April 2024)

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Comunicacions a congressos  (Matemàtiques i Informàtica)

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