Author:

Keywords:

Science & Technology, Technology, Life Sciences & Biomedicine, Engineering, Biomedical, Radiology, Nuclear Medicine & Medical Imaging, Engineering, Active contours, B-spline explicit active surfaces, User interaction, Interactive segmentation, LIVE-WIRE, LEVEL-SET, IMAGE SEGMENTATION, EVOLUTION, SHAPE, Algorithms, Computer Systems, Echocardiography, Three-Dimensional, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Myocardial Ischemia, Numerical Analysis, Computer-Assisted, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, 0903 Biomedical Engineering, 1103 Clinical Sciences, Nuclear Medicine & Medical Imaging, 3202 Clinical sciences, 4003 Biomedical engineering, 4603 Computer vision and multimedia computation

Abstract:

Image segmentation is an ubiquitous task in medical image analysis, which is required to estimate morphological or functional properties of given anatomical targets. While automatic processing is highly desirable, image segmentation remains to date a supervised process in daily clinical practice. Indeed, challenging data often requires user interaction to capture the required level of anatomical detail. To optimize the analysis of 3D images, the user should be able to efficiently interact with the result of any segmentation algorithm to correct any possible disagreement. Building on a previously developed real-time 3D segmentation algorithm, we propose in the present work an extension towards an interactive application where user information can be used online to steer the segmentation result. This enables a synergistic collaboration between the operator and the underlying segmentation algorithm, thus contributing to higher segmentation accuracy, while keeping total analysis time competitive. To this end, we formalize the user interaction paradigm using a geometrical approach, where the user input is mapped to a non-cartesian space while this information is used to drive the boundary towards the position provided by the user. Additionally, we propose a shape regularization term which improves the interaction with the segmented surface, thereby making the interactive segmentation process less cumbersome. The resulting algorithm offers competitive performance both in terms of segmentation accuracy, as well as in terms of total analysis time. This contributes to a more efficient use of the existing segmentation tools in daily clinical practice. Furthermore, it compares favorably to state-of-the-art interactive segmentation software based on a 3D livewire-based algorithm.