"ShapeNet": A Shape Regression Convolutional Neural Network Ensemble Applied to the Segmentation of the Left Ventricle in Echocardiography.

Abstract

Left ventricle (LV) segmentation is crucial for cardiac diagnosis but remains challenging in echocardiography. We present ShapeNet, a fully automatic method combining a convolutional neural network (CNN) ensemble with an improved active shape model (ASM). ShapeNet predicts optimal pose (rotation, translation, and scale) and shape parameters, which are refined using the improved ASM. The ASM optimizes an objective function constructed from gray-level profiles concatenated into a single contour appearance vector. The model was trained on 4800 augmented CAMUS images and tested on both CAMUS and EchoNet databases. It achieved a Dice coefficient of 0.87 and a Hausdorff Distance (HD) of 4.08 pixels on CAMUS, and a Dice coefficient of 0.81 with an HD of 10.21 pixels on EchoNet, demonstrating robust performance across datasets. These results highlight the improved accuracy in HD compared to previous semantic and shape-based segmentation methods by generating statistically valid LV contours from ultrasound images.