Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data...

Area of Science:

• Cardiovascular Imaging
• Artificial Intelligence in Medicine
• Medical Image Analysis

Background:

• Fully automatic analysis of myocardial perfusion MRI is crucial for objective reporting of stress/rest studies in suspected ischemic heart disease.
• Developing robust deep learning (DL) models for multi-center MRI datasets presents challenges due to variations in software and hardware.
• Existing DL techniques struggle with data heterogeneity from different acquisition protocols and scanner vendors.

Purpose of the Study:

• To develop and evaluate a novel DL approach for robust segmentation of myocardial perfusion MRI datasets.
• To enhance the accuracy and reliability of automated analysis across multi-center, multi-vendor, and multi-protocol MRI data.
• To address the limitations of current DL methods in handling variations in pulse sequences and scanner hardware.

Main Methods:

• A Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis approach was developed using a pool of deep neural networks (DNNs) with a U-Net architecture.
• A space-time sliding-patch analysis generated pixel-wise uncertainty maps, which were used to select the best segmentation outcome from the DNN pool.
• The DAUGS approach was trained and validated on an internal dataset and tested on two external datasets with variations in pulse sequence and scanner vendor.

Main Results:

• DAUGS analysis demonstrated comparable performance to established methods on the internal dataset (Dice score: 0.896 ± 0.050).
• DAUGS significantly outperformed the established approach on external datasets (exD-1 Dice: 0.885 ± 0.040; exD-2 Dice: 0.811 ± 0.070).
• The proposed method substantially reduced segmentation failures compared to the established approach (4.3% vs. 17.1%).

Conclusions:

• The DAUGS analysis approach shows significant potential for improving the robustness of DL-based segmentation for multi-center myocardial perfusion MRI.
• This method effectively handles variations in pulse sequences, site locations, and scanner vendors, leading to more reliable automated analysis.
• DAUGS offers a promising solution for consistent and accurate myocardial segmentation in clinical settings with diverse imaging data.