Segmentation-Consistent Probabilistic Lesion Counting
- URL: http://arxiv.org/abs/2204.05276v1
- Date: Mon, 11 Apr 2022 17:26:49 GMT
- Title: Segmentation-Consistent Probabilistic Lesion Counting
- Authors: Julien Schroeter, Chelsea Myers-Colet, Douglas L Arnold, Tal Arbel
- Abstract summary: Lesion counts are important indicators of disease severity, patient prognosis, and treatment efficacy, yet counting as a task in medical imaging is often overlooked in favor of segmentation.
This work introduces a novel continuously differentiable function that maps lesion segmentation predictions to lesion count probability distributions in a consistent manner.
- Score: 3.6513059119482145
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Lesion counts are important indicators of disease severity, patient
prognosis, and treatment efficacy, yet counting as a task in medical imaging is
often overlooked in favor of segmentation. This work introduces a novel
continuously differentiable function that maps lesion segmentation predictions
to lesion count probability distributions in a consistent manner. The proposed
end-to-end approach--which consists of voxel clustering, lesion-level voxel
probability aggregation, and Poisson-binomial counting--is non-parametric and
thus offers a robust and consistent way to augment lesion segmentation models
with post hoc counting capabilities. Experiments on Gadolinium-enhancing lesion
counting demonstrate that our method outputs accurate and well-calibrated count
distributions that capture meaningful uncertainty information. They also reveal
that our model is suitable for multi-task learning of lesion segmentation, is
efficient in low data regimes, and is robust to adversarial attacks.
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