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.

翻译：医学成像,包括MRI、CT和Ultrasound,在临床决策中发挥着关键作用。准确的分解对于测量图象的兴趣结构至关重要。然而,人工的分解高度依赖操作者,这导致数量测量的内在和内部差异性较高。在本文中,我们探讨了深神经网络所测量的Bayesian预测分布参数能够捕捉临床医生的跨内变异性的可行性。通过探索和分析最近出现的近似推导计划,我们评估了巴伊西亚人与后部相近的深层学习能否在分解和临床测量中学习跨间速率变异性。实验是以两种不同的成像模式进行的:MRI和超声波。我们从经验上表明,深神经网络所测量的Bayesian预测分布参数可以接近临床医生的跨内变异性。我们展示了通过提供临床测量不确定性来定量分析医学图象的新视角。