Bayesian approaches for Quantifying Clinicians' Variability in Medical Image Quantification

• URL: http://arxiv.org/abs/2207.01868v2
• Date: Wed, 6 Jul 2022 06:14:40 GMT
• Title: Bayesian approaches for Quantifying Clinicians' Variability in Medical Image Quantification
• Authors: Jaeik Jeon, Yeonggul Jang, Youngtaek Hong, Hackjoon Shim, Sekeun Kim
• Abstract summary: We show that Bayesian predictive distribution parameterized by deep neural networks could approximate the clinicians' inter-intra variability. We show a new perspective in analyzing medical images quantitatively by providing clinical measurement uncertainty.
• Score: 0.16314780449435543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical imaging, including MRI, CT, and Ultrasound, plays a vital role in clinical decisions. Accurate segmentation is essential to measure the structure of interest from the image. However, manual segmentation is highly operator-dependent, which leads to high inter and intra-variability of quantitative measurements. In this paper, we explore the feasibility that Bayesian predictive distribution parameterized by deep neural networks can capture the clinicians' inter-intra variability. By exploring and analyzing recently emerged approximate inference schemes, we evaluate whether approximate Bayesian deep learning with the posterior over segmentations can learn inter-intra rater variability both in segmentation and clinical measurements. The experiments are performed with two different imaging modalities: MRI and ultrasound. We empirically demonstrated that Bayesian predictive distribution parameterized by deep neural networks could approximate the clinicians' inter-intra variability. We show a new perspective in analyzing medical images quantitatively by providing clinical measurement uncertainty.

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