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Home
Two-stage Dynamic Synergistic Segmentation Method For Myocardial Pathology.

Home
Two-stage Dynamic Synergistic Segmentation Method For Myocardial Pathology.

Related Experiment Video

3D Whole-heart Myocardial Tissue Analysis

3D Whole-heart Myocardial Tissue Analysis

Published on: April 12, 2017

Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.

Dongsheng Ruan¹, Xiaolin Zhang¹, Zihan Yuan¹

¹Department of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China.

Journal of Imaging

|June 25, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

A new dynamic synergistic segmentation network (DSS-Net) improves myocardial scar and edema segmentation in cardiac MRI. This method enhances accuracy for assessing myocardial infarction by combining anatomical guidance with multi-modal learning.

Keywords:

UNet class imbalance multi-modal cardiac MRI myocardial pathology segmentation small object segmentation

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Ultrasonic Assessment of Myocardial Microstructure

Ultrasonic Assessment of Myocardial Microstructure

Published on: January 14, 2014

Related Experiment Videos

3D Whole-heart Myocardial Tissue Analysis

3D Whole-heart Myocardial Tissue Analysis

Published on: April 12, 2017

Ultrasonic Assessment of Myocardial Microstructure

Ultrasonic Assessment of Myocardial Microstructure

Published on: January 14, 2014

Area of Science:

Cardiovascular Imaging
Medical Image Analysis
Artificial Intelligence in Medicine

Background:

Accurate segmentation of myocardial scar and edema from multi-sequence cardiac magnetic resonance (MS-CMR) is crucial for myocardial infarction assessment.
Challenges include heterogeneous imaging characteristics, class imbalance, and ambiguous pathological regions.

Purpose of the Study:

To propose a dynamic synergistic segmentation network (DSS-Net) for robust myocardial pathology segmentation in MS-CMR.
To address limitations in current segmentation methods for scar and edema detection.

Main Methods:

A coarse-to-fine strategy with initial myocardium segmentation for anatomical priors.
A Modality Dynamic Fusion Module (MDFM) for adaptive emphasis on relevant imaging data.

A Stage Feature Aggregation Module (SFAM) to improve cross-stage feature interaction and lesion representation.

Main Results:

DSS-Net achieved competitive performance on the MyoPS 2020 and MyoPS 2024 datasets.
Dice scores of 0.706 for scar and 0.753 for edema were obtained on the MyoPS 2020 dataset.
The method demonstrated a balanced trade-off between sensitivity and specificity compared to state-of-the-art approaches.

Conclusions:

Combining anatomical guidance with pathology-aware multi-modal learning is effective for myocardial pathology segmentation.
DSS-Net offers a promising strategy for accurate and robust assessment of myocardial infarction from MS-CMR images.