Liu, Li and Xia, Kaijian (2024) BTIS-Net: Efficient 3D U-Net for Brain Tumor Image Segmentation. IEEE Access, 12. pp. 133392-133405. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2024.3460797.
Full text not available from this repository.Abstract
Brain tumor segmentation techniques are essential for the precise delineation of tumors and normal brain tissues which is essential for the guidance of surgical intervention and clinical decisions. However, for resource-constrained clinical environments, more efficient and lightweight segmentation models are needed so that they can be applied in real-time for surgical navigation and clinical decision-making. To tackle this issue, the proposed study introduces a very effective 3D U-Net model that is specifically designed for brain tumor image segmentation. This study presents the primary contributions as follows: 3D depth separable convolution is introduced to decrease the number of model training parameters, hence enhancing the overall efficiency of the model. The dilated dense residual block is designed to expand the sensory field, allowing the network to grasp a broader range of features and structures within the input data. As a result, the model's performance and generalization ability to handle complex tasks are improved. The integration of the confusion area segmentation module enhances the model's capability to discern intricate image details and edges, thereby augmenting the overall segmentation efficacy. Evaluation of the proposed BTIS-Net involves experimentation on two widely recognized datasets, namely BraTS 2019 and BraTS 2021. The Dice similarity coefficient, Positive predictive value, and Sensitivity exhibit average improvements of 5.68, 5.38, and 2.14, respectively. Additionally, the Hausdorff distance is reduced by an average of 2.71. The experimental results validate the efficient segmentation performance of the BTIS-Net model, showcasing exceptional outcomes even under resource constraints.
| Item Type: | Article |
|---|---|
| Funders: | Engineering Technology Research and Development Center of Higher Vocational Colleges in Jiangsu Province (11), Excellent Science and Technology Innovation Team of Higher Vocational Colleges in Jiangsu Province (3), Cultivation Project of Demonstration Virtual Simulation Training Base in Jiangsu Province (30), Dual-qualified Master Teacher Studio of Vocational Education in Jiangsu Province (31), High-level KeyProfessional Construction Project in Jiangsu Province (17), Excellent Young Key Teachers of ''Blue and GreenProject'' of Higher Vocational Colleges in Jiangsu Province (27) |
| Uncontrolled Keywords: | Image segmentation; Tumors; Brain modeling; Three-dimensional displays; Convolutional neural networks; Magnetic resonance imaging; Solid modeling; Brain cancer; 3D U-Net; 3D depth separable convolution; brain tumor image segmentation; confusion area segmentation; dilated dense residual block |
| Subjects: | R Medicine > R Medicine (General) T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Biomedical Engineering Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 22 Nov 2024 04:35 |
| Last Modified: | 22 Nov 2024 04:35 |
| URI: | http://eprints.um.edu.my/id/eprint/47095 |
Actions (login required)
 |
View Item |
