Arouse-Net: Enhancing Glioblastoma Segmentation in Multi-Parametric MRI with a Custom 3D Convolutional Neural Network and Attention Mechanism
Glioblastoma, a highly aggressive brain tumor, is challenging to diagnose and treat due to its variable appearance and invasiveness. Traditional segmentation methods are often limited by inter-observer variability and the lack of annotated datasets. Addressing these challenges, this study introduces...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/1/160 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841549158093160448 |
|---|---|
| author | Haiyang Li Xiaozhi Qi Ying Hu Jianwei Zhang |
| author_facet | Haiyang Li Xiaozhi Qi Ying Hu Jianwei Zhang |
| author_sort | Haiyang Li |
| collection | DOAJ |
| description | Glioblastoma, a highly aggressive brain tumor, is challenging to diagnose and treat due to its variable appearance and invasiveness. Traditional segmentation methods are often limited by inter-observer variability and the lack of annotated datasets. Addressing these challenges, this study introduces Arouse-Net, a 3D convolutional neural network that enhances feature extraction through dilated convolutions, improving tumor margin delineation. Our approach includes an attention mechanism to focus on edge features, essential for precise glioblastoma segmentation. The model’s performance is benchmarked against the state-of-the-art BRATS test dataset, demonstrating superior results with an over eight times faster processing speed. The integration of multi-modal MRI data and the novel evaluation protocol developed for this study offer a robust framework for medical image segmentation, particularly useful for clinical scenarios where annotated datasets are limited. The findings of this research not only advance the field of medical image analysis but also provide a foundation for future work in the development of automated segmentation tools for brain tumors. |
| format | Article |
| id | doaj-art-8b784b58cf734456b26b6e1e3ce5fab7 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-8b784b58cf734456b26b6e1e3ce5fab72025-01-10T13:18:27ZengMDPI AGMathematics2227-73902025-01-0113116010.3390/math13010160Arouse-Net: Enhancing Glioblastoma Segmentation in Multi-Parametric MRI with a Custom 3D Convolutional Neural Network and Attention MechanismHaiyang Li0Xiaozhi Qi1Ying Hu2Jianwei Zhang3Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaDepartment of Informatics, University of Hamburg, 22527 Hamburg, GermanyGlioblastoma, a highly aggressive brain tumor, is challenging to diagnose and treat due to its variable appearance and invasiveness. Traditional segmentation methods are often limited by inter-observer variability and the lack of annotated datasets. Addressing these challenges, this study introduces Arouse-Net, a 3D convolutional neural network that enhances feature extraction through dilated convolutions, improving tumor margin delineation. Our approach includes an attention mechanism to focus on edge features, essential for precise glioblastoma segmentation. The model’s performance is benchmarked against the state-of-the-art BRATS test dataset, demonstrating superior results with an over eight times faster processing speed. The integration of multi-modal MRI data and the novel evaluation protocol developed for this study offer a robust framework for medical image segmentation, particularly useful for clinical scenarios where annotated datasets are limited. The findings of this research not only advance the field of medical image analysis but also provide a foundation for future work in the development of automated segmentation tools for brain tumors.https://www.mdpi.com/2227-7390/13/1/160brain tumor segmentationdeep neural networksmedical imagingBRATS |
| spellingShingle | Haiyang Li Xiaozhi Qi Ying Hu Jianwei Zhang Arouse-Net: Enhancing Glioblastoma Segmentation in Multi-Parametric MRI with a Custom 3D Convolutional Neural Network and Attention Mechanism Mathematics brain tumor segmentation deep neural networks medical imaging BRATS |
| title | Arouse-Net: Enhancing Glioblastoma Segmentation in Multi-Parametric MRI with a Custom 3D Convolutional Neural Network and Attention Mechanism |
| title_full | Arouse-Net: Enhancing Glioblastoma Segmentation in Multi-Parametric MRI with a Custom 3D Convolutional Neural Network and Attention Mechanism |
| title_fullStr | Arouse-Net: Enhancing Glioblastoma Segmentation in Multi-Parametric MRI with a Custom 3D Convolutional Neural Network and Attention Mechanism |
| title_full_unstemmed | Arouse-Net: Enhancing Glioblastoma Segmentation in Multi-Parametric MRI with a Custom 3D Convolutional Neural Network and Attention Mechanism |
| title_short | Arouse-Net: Enhancing Glioblastoma Segmentation in Multi-Parametric MRI with a Custom 3D Convolutional Neural Network and Attention Mechanism |
| title_sort | arouse net enhancing glioblastoma segmentation in multi parametric mri with a custom 3d convolutional neural network and attention mechanism |
| topic | brain tumor segmentation deep neural networks medical imaging BRATS |
| url | https://www.mdpi.com/2227-7390/13/1/160 |
| work_keys_str_mv | AT haiyangli arousenetenhancingglioblastomasegmentationinmultiparametricmriwithacustom3dconvolutionalneuralnetworkandattentionmechanism AT xiaozhiqi arousenetenhancingglioblastomasegmentationinmultiparametricmriwithacustom3dconvolutionalneuralnetworkandattentionmechanism AT yinghu arousenetenhancingglioblastomasegmentationinmultiparametricmriwithacustom3dconvolutionalneuralnetworkandattentionmechanism AT jianweizhang arousenetenhancingglioblastomasegmentationinmultiparametricmriwithacustom3dconvolutionalneuralnetworkandattentionmechanism |