Cardiac CT segmentation based on distance regularized level set

TL;DR

This paper introduces a distance regularized level set method for efficient and accurate segmentation of the left ventricle's inner and outer membranes in cardiac CT images, aiding in faster diagnosis.

Contribution

It proposes a novel segmentation approach using DRLSE tailored for cardiac CT images, improving segmentation accuracy and efficiency over manual methods.

Findings

High dice scores indicating accurate segmentation

Low Hausdorff distances showing precise boundary detection

Effective separation of inner and outer membranes

Abstract

Before analy z ing the CT image, it is very important to segment the heart image, and the left ve ntricular (LV) inner and outer membrane segmentation is one of the most important contents. However, manual segmentation is tedious and time consuming. In order to facilitate doctors to focus on high tech tasks such as disease analysis and diagnosis, it is crucial to develop a fast and accurate segmentation method [1]. In view of this phenomenon, this paper uses distance regularized level set (DRL SE) to explore the segmentation effect of epicardium and endocardium 2 ]], which includes a distance regula riz ed t erm and an external energy term. Finally, five CT images are used to verify the proposed method, and image quality evaluation indexes such as dice score and Hausdorff distance are used to evaluate the segmentation effect. The results showed that the me tho d could separate the inner and outer membrane very well (endocardium dice = 0.9253, Hausdorff = 7.8740; epicardium Hausdorff = 0.9687, Hausdorff = 6 .