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Automated Stroke Lesion Segmentation in Rat Brain MR Images Using an Encoder-Decoder Framework.

Herng-Hua Chang, Shin-Joe Yeh, Ming-Chang Chiang

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 12, 2023

Summary

This summary is machine-generated.

A new deep learning framework accurately segments stroke lesions in diffusion-weighted imaging (DWI), improving preclinical stroke research and potentially aiding clinical scoring systems.

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Area of Science:

Neuroscience
Medical Imaging
Artificial Intelligence

Background:

Stroke is a primary cause of mortality and long-term disability globally.
Accurate segmentation of stroke lesions in diffusion-weighted imaging (DWI) is crucial for research and clinical applications.
Nonuniform intensity and blurred boundaries in DWI images present significant segmentation challenges.

Purpose of the Study:

To develop and evaluate a deep learning-based framework for automated stroke lesion segmentation in DWI images.
To address the difficulties associated with segmenting infarct regions with nonuniform intensity and blurred boundaries.
To provide a reliable tool for preclinical stroke investigation.

Main Methods:

An encoder-decoder deep learning network was designed, incorporating a hybrid block for multiscale feature extraction.
An in-house dataset of DWI images was curated for training and evaluating the segmentation framework.
The proposed method was compared against existing techniques through extensive experiments.

Main Results:

The developed framework achieved accurate segmentation of stroke lesions in DWI images.
The system demonstrated superior performance compared to several competitive methods, both qualitatively and quantitatively.
The deep learning approach effectively handled the challenges of nonuniform intensity and blurred boundaries.

Conclusions:

The proposed deep learning framework offers a robust solution for automated stroke lesion segmentation in DWI.
This automated system can significantly facilitate preclinical stroke research by providing accurate lesion identification.
The tool has the potential to aid neuroscientists in developing new clinical scoring systems, reducing examination time and improving inter-rater reliability.