  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.
