EEMSNet: Eagle-Eye Multi-Scale Supervised Network for cardiac segmentation

Abstract

Cardiac segmentation plays a crucial role in computer-aided diagnosis of cardiac diseases. Nevertheless, the task of cardiac segmentation is inherently arduous due to the complex cardiac anatomy, and the intensity heterogeneity introduced during the acquisition of magnetic resonance images. To address these challenges, this paper presents an Eagle-Eye Multi-Scale Supervision Network. The network improves on the Full Convolutional Transformer network and introduces an innovative Eagle-Eye Multi-Scale Supervised module, which is used exclusively for edge supervision during the training process. The edge-supervised branch supervises the structure of cardiac edges at different scales within the input image pyramid structure. During training, the Eagle-Eye Multi-Scale Supervised module is based on multi-scale inputs. It uses edge information from Ground Truth to guide the model in learning the structural and detailed features of cardiac edges, ensuring the model captures edges accurately. Furthermore, considering that most cardiac segmentation networks typically overlook boundary profile information, our designed loss function places particular emphasis on the preservation of boundary profiles after the fusion of multi-scale edge features. Experimental results demonstrate that our proposed the Eagle-Eye Multi-Scale Supervision Network exhibits outstanding performance on the Automatic Cardiac Diagnosis Challenge dataset, achieving a notable average Dice Similarity Coefficient of 93.21%. Particularly, the Dice Similarity Coefficient value for right ventricle segmentation stands out at an impressive 93.52%. These findings signify the exceptional accuracy and effectiveness of our approach in addressing cardiac image segmentation tasks.