Extracting and Visualizing Uncertainties in Segmentations from 3D Medical Data

Abstract

Assessing surfaces of segmentations extracted from 3D image data for medical purposes requires dedicated extraction and visualization methods. In particular, when assessing follow-up cases, the exact volume and confidence level of the segmentation surface is crucial for medical decision-making. This paper introduces a new processing chain comprising a series of carefully selected and well-matched steps to determine and visualize a segmentation boundary. In a first step, the surface, segmentation confidence and statistical partial volume are extracted. Then, a mesh-based method is applied to determine a refined boundary of the segmented object based on these properties, whilst smoothness, confidence of the surface and partial volume are considered locally. In contrast to existing methods, the proposed approach is able to guarantee the estimated volume for the whole segmentation, which is an important prerequisite for clinical application. Furthermore, a novel visualization method is presented which was specifically designed to simultaneously provide information about 3D morphology, confidence and possible errors. As opposed to classical visualization approaches that take advantage of color and transparency but need some geometric mapping and interpretation from the observer, the proposed scattered visualization utilizes density and scattering, which are much closer and more intuitively related to the original geometric meaning. The presented method is particularly suitable to assess pleural thickenings from follow-up CT images, which further illustrates the potential of the proposed method.