An enhanced visual state space model for myocardial pathology segmentation in multi-sequence cardiac MRI.

Abstract

Background: Myocardial pathology (scar and edema) segmentation plays a crucial role in the diagnosis, treatment, and prognosis of myocardial infarction (MI). However, the current mainstream models for myocardial pathology segmentation have the following limitations when faced with cardiac magnetic resonance(CMR) images with multiple objects and large changes in object scale: the remote modeling ability of convolutional neural networks is insufficient, and the computational complexity of transformers is high, which makes myocardial pathology segmentation challenging.

Purpose: This study aims to develop a novel model to address the image characteristics and algorithmic challenges faced in the myocardial pathology segmentation task and improve the accuracy and efficiency of myocardial pathology segmentation.

Methods: We developed a novel visual state space (VSS)-based deep neural network, MPS-Mamba. In order to accurately and adequately extract CMR image features, the encoder employs a dual-branch structure to extract global and local features of the image. Among them, the VSS branch overcomes the limitations of the current mainstream models for myocardial pathology segmentation by modeling remote relationships through linear computability, while the convolutional-based branch provides complementary local information. Given the unique properties of the dual branches, we design a modular dual-branch fusion module for fusing dual branches to enhance the feature representation of the dual encoder. To improve the ability to model objects of different scales in cardiac magnetic resonance (CMR) images, a multi-scale feature fusion (MSF) module is designed to achieve effective integration and fine expression of multi-scale information. To further incorporate anatomical knowledge to optimize segmentation results, a decoder with three decoding branches is designed to output segmentation results of scar, edema, and myocardium, respectively. In addition, multiple sets of constraint functions are used to not only improve the segmentation accuracy of myocardial pathology but also effectively model the spatial position relationship between myocardium, scar, and edema.

Results: The proposed method was comprehensively evaluated on the MyoPS 2020 dataset, and the results showed that MPS-Mamba achieved an average Dice score of 0.717 $\pm$ 0.169 in myocardial scar segmentation, which is superior to the current mainstream methods. In addition, MPS-Mamba also performed well in the edema segmentation task, with an average Dice score of 0.735 $\pm$ 0.073. The experimental results further demonstrate the effectiveness of MPS-Mamba in segmenting myocardial pathologies in multi-sequence CMR images, verifying its advantages in myocardial pathology segmentation tasks.

Conclusions: Given the effectiveness and superiority of MPS-Mamba, this method is expected to become a potential myocardial pathology segmentation tool that can effectively assist clinical diagnosis.