Bayesian approaches for Quantifying Clinicians' Variability in Medical Image Quantification
This addresses the issue of inconsistent quantitative measurements in medical imaging for clinicians, but it is incremental as it applies existing Bayesian methods to a known bottleneck.
The paper tackled the problem of high variability in manual medical image segmentation by exploring Bayesian deep learning to capture clinicians' inter- and intra-variability, demonstrating empirically that this approach can approximate such variability in MRI and ultrasound images.
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.