Fast Segmentation of Left Ventricle in CT Images by Explicit Shape Regression using Random Pixel Difference Features
This work addresses the need for faster segmentation in medical imaging for applications like real-time analysis or handling large datasets, though it is incremental as it builds on existing learning-based methods.
The paper tackles the slow speed of left ventricle segmentation in CT images by proposing a joint localization and boundary delineation algorithm using explicit shape regression with random pixel difference features, achieving an average running time of 1.2 milliseconds per case and segmentation errors of 1.21 ± 0.11 mm for endocardium and 1.23 ± 0.11 mm for epicardium.
Recently, machine learning has been successfully applied to model-based left ventricle (LV) segmentation. The general framework involves two stages, which starts with LV localization and is followed by boundary delineation. Both are driven by supervised learning techniques. When compared to previous non-learning-based methods, several advantages have been shown, including full automation and improved accuracy. However, the speed is still slow, in the order of several seconds, for applications involving a large number of cases or case loads requiring real-time performance. In this paper, we propose a fast LV segmentation algorithm by joint localization and boundary delineation via training explicit shape regressor with random pixel difference features. Tested on 3D cardiac computed tomography (CT) image volumes, the average running time of the proposed algorithm is 1.2 milliseconds per case. On a dataset consisting of 139 CT volumes, a 5-fold cross validation shows the segmentation error is $1.21 \pm 0.11$ for LV endocardium and $1.23 \pm 0.11$ millimeters for epicardium. Compared with previous work, the proposed method is more stable (lower standard deviation) without significant compromise to the accuracy.