https://scholars.lib.ntu.edu.tw/handle/123456789/638198
標題: | Automated Stroke Lesion Segmentation in Rat Brain MR Images Using an Encoder-Decoder Framework | 作者: | HERNG-HUA CHANG SHIN-JOE YEH Chiang, Ming Chang SUNG-TSANG HSIEH |
公開日期: | 七月-2023 | 卷: | 2023 | 來源出版物: | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | 摘要: | Stroke is a leading cause of serious long-term disability and the major cause of mortality worldwide. Experimental ischemic stroke models play an important role in realizing the mechanism of cerebral ischemia and evaluating the development of pathological extent. An accurate and reliable image segmentation tool to automatically identify the stroke lesion is important in the subsequent processes. However, the intensity distribution of the infarct region in the diffusion weighted imaging (DWI) images is usually nonuniform with blurred boundaries. A deep learning-based infarct region segmentation framework is developed in this paper to address the segmentation difficulties. The proposed solution is an encoder-decoder network that includes a hybrid block model for efficient multiscale feature extraction. An in-house DWI image dataset was created to evaluate this automated stroke lesion segmentation scheme. Through massive experiments, accurate segmentation results were obtained, which outperformed many competitive methods both qualitatively and quantitatively. Our stroke lesion segmentation system is potential in providing a decent tool to facilitate preclinical stroke investigation using DWI images.Clinical Relevance- This facilitates neuroscientists the investigation of a new scoring system with less examination time and better inter-rater reliability, which helps to understand the function of specific brain areas underlying neuroimaging signatures clinically. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/638198 | ISBN: | 979-8-3503-2447-1 | DOI: | 10.1109/EMBC40787.2023.10340278 |
顯示於: | 工程科學及海洋工程學系 |
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