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Medical imaging, including MRI, CT, and Ultrasound, plays a vital role inclinical decisions. Accurate segmentation is essential to measure the structureof interest from the image. However, manual segmentation is highlyoperator-dependent, which leads to high inter and intra-variability ofquantitative measurements. In this paper, we explore the feasibility thatBayesian predictive distribution parameterized by deep neural networks cancapture the clinicians' inter-intra variability. By exploring and analyzingrecently emerged approximate inference schemes, we evaluate whether approximateBayesian deep learning with the posterior over segmentations can learninter-intra rater variability both in segmentation and clinical measurements.The experiments are performed with two different imaging modalities: MRI andultrasound. We empirically demonstrated that Bayesian predictive distributionparameterized by deep neural networks could approximate the clinicians'inter-intra variability. We show a new perspective in analyzing medical imagesquantitatively by providing clinical measurement uncertainty.

Bayesian approaches for Quantifying Clinicians' Variability in Medical Image Quantification