Medical physics · 2025
U-shaped network combining dual-stream fusion mamba and redesigned multilayer perceptron for myocardial pathology segmentation.
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Zhang Wenjie, Yang Tiejun, Fan Jiacheng, Wang Heng, Ji Mingzhu, Zhang Huiyao, Miao Jianyu
Аннотация
BACKGROUND: Cardiac magnetic resonance imaging (CMR) provides critical pathological information, such as scars and edema, which are vital for diagnosing myocardial infarction (MI). However, due to the limited pathological information in single-sequence CMR images and the small size of pathological regions, automatic segmentation of myocardial pathology remains a significant challenge.
PURPOSE: In the paper, we propose a novel two-stage anatomical-pathological segmentation framework combining Kolmogorov-Arnold Networks (KAN) and Mamba, aiming to effectively segment myocardial pathology in multi-sequence CMR images.
METHODS: First, in the coarse segmentation stage, we employed a multiline parallel MambaUnet as the anatomical structure segmentation network to obtain shape prior information. This approach effectively addresses the class imbalance issue and aids in subsequent pathological segmentation. In the fine segmentation stage, we introduced a novel U-shaped segmentation network, KANMambaNet, which features a Dual-Stream Fusion Mamba module. This module enhances the network's ability to capture long-range dependencies while improving its capability to distinguish different pathological features in small regions. Additionally, we developed a Kolmogorov-Arnold Network-based multilayer perceptron (KAN MLP) module that utilizes learnable activation functions instead of fixed nonlinear functions. This design enhances the network's flexibility in handling various pathological features, enabling more accurate differentiation of the pathological characteristics at the boundary between edema and scar regions. Our method achieves competitive segmentation performance compared to state-of-the-art models, particularly in terms of the Dice coefficient.
RESULTS: We validated our model's performance on the MyoPS2020 dataset, achieving a Dice score of 0.8041 ± $\pm$ 0.0751 for myocardial edema and 0.9051 ± $\pm$ 0.0240 for myocardial scar. Compared to the baseline model MambaUnet, our edema segmentation performance improved by 0.1420, and scar segmentation performance improved by 0.1081.
CONCLUSIONS: We developed an innovative two-stage anatomical-pathological segmentation framework that integrates KAN and Mamba, effectively segmenting myocardial pathology in multi-sequence CMR images. The experimental results demonstrate that our proposed method achieves superior segmentation performance compared to other state-of-the-art methods.
PURPOSE: In the paper, we propose a novel two-stage anatomical-pathological segmentation framework combining Kolmogorov-Arnold Networks (KAN) and Mamba, aiming to effectively segment myocardial pathology in multi-sequence CMR images.
METHODS: First, in the coarse segmentation stage, we employed a multiline parallel MambaUnet as the anatomical structure segmentation network to obtain shape prior information. This approach effectively addresses the class imbalance issue and aids in subsequent pathological segmentation. In the fine segmentation stage, we introduced a novel U-shaped segmentation network, KANMambaNet, which features a Dual-Stream Fusion Mamba module. This module enhances the network's ability to capture long-range dependencies while improving its capability to distinguish different pathological features in small regions. Additionally, we developed a Kolmogorov-Arnold Network-based multilayer perceptron (KAN MLP) module that utilizes learnable activation functions instead of fixed nonlinear functions. This design enhances the network's flexibility in handling various pathological features, enabling more accurate differentiation of the pathological characteristics at the boundary between edema and scar regions. Our method achieves competitive segmentation performance compared to state-of-the-art models, particularly in terms of the Dice coefficient.
RESULTS: We validated our model's performance on the MyoPS2020 dataset, achieving a Dice score of 0.8041 ± $\pm$ 0.0751 for myocardial edema and 0.9051 ± $\pm$ 0.0240 for myocardial scar. Compared to the baseline model MambaUnet, our edema segmentation performance improved by 0.1420, and scar segmentation performance improved by 0.1081.
CONCLUSIONS: We developed an innovative two-stage anatomical-pathological segmentation framework that integrates KAN and Mamba, effectively segmenting myocardial pathology in multi-sequence CMR images. The experimental results demonstrate that our proposed method achieves superior segmentation performance compared to other state-of-the-art methods.