How precise are performance estimates for typical medical image segmentation tasks?

• URL: http://arxiv.org/abs/2210.14677v3
• Date: Wed, 24 May 2023 12:32:40 GMT
• Title: How precise are performance estimates for typical medical image segmentation tasks?
• Authors: Rosana El Jurdi and Olivier Colliot
• Abstract summary: In this paper, we aim to estimate what is the typical confidence that can be expected in medical image segmentation studies. We extensively study precision estimation using both Gaussian assumption and bootstrapping. Overall, our work shows that small test sets lead to wide confidence intervals.
• Score: 3.606795745041439
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An important issue in medical image processing is to be able to estimate not only the performances of algorithms but also the precision of the estimation of these performances. Reporting precision typically amounts to reporting standard-error of the mean (SEM) or equivalently confidence intervals. However, this is rarely done in medical image segmentation studies. In this paper, we aim to estimate what is the typical confidence that can be expected in such studies. To that end, we first perform experiments for Dice metric estimation using a standard deep learning model (U-net) and a classical task from the Medical Segmentation Decathlon. We extensively study precision estimation using both Gaussian assumption and bootstrapping (which does not require any assumption on the distribution). We then perform simulations for other test set sizes and performance spreads. Overall, our work shows that small test sets lead to wide confidence intervals (e.g. $\sim$8 points of Dice for 20 samples with $\sigma \simeq 10$).

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