Cardiac CT segmentation based on distance regularized level set
This work addresses the need for fast and accurate segmentation in medical imaging to assist doctors, but it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of segmenting left ventricular inner and outer membranes in cardiac CT images to automate a tedious manual process, achieving dice scores of 0.9253 for endocardium and 0.9687 for epicardium with Hausdorff distances of 7.8740 and 6.0, respectively.
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 .