Brain MRI Tumour Localization and Segmentation Through Deep Learning

被引:0
|
作者
Davar, Somayeh [1 ]
Fevens, Thomas [1 ]
机构
[1] Concordia Univ, Dept Comp Sci & Software Engn, Montreal, PQ, Canada
来源
2024 IEEE 67TH INTERNATIONAL MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS, MWSCAS 2024 | 2024年
关键词
Deep Learning; Brain tumour Segmentation; Channel Attention; Spatial attention; U-Net; Medical Imaging Convolutional Neural Networks;
D O I
10.1109/MWSCAS60917.2024.10658897
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The segmentation of magnetic resonance imaging (MRI) is an important task in medical imaging, particularly for brain MRIs, where accurate segmentation of anatomical structures, often challenged by uneven shapes and fuzzy boundaries of tumours, is difficult to achieve. Thus, automating reliable segmentation of the region of interest (ROI) is essential in medical imaging. This work introduces an automated approach for brain tumour segmentation, leveraging a Deep Convolutional Neural Network (CNN) with a focus on effectively segmenting brain tumours within T1-weighted contrast-enhanced MRI (CE-MRI) images. The proposed method is first performed on the classified images to localize the tumour regions of interest(ROIs). In the next stage, the algorithm contours the concentrated tumour boundary for the segmentation process, which contains the network and attention module, both spatial and channel. To evaluate the overall system's performance, precision, recall, Jaccard index, and dice similarity coefficient (DSC) were calculated, where we achieved 0.8757, 0.8804, 0.8624, and 0.8995, respectively. Our approach demonstrates promising results compared to previous methods using the same database.
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页码:782 / 786
页数:5
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