000			01881nam a22001577a 4500
082			_a629.8
100			_aKhan, Hussain Nasir _9119525
245			_aMultimodal 3D-MRI Brain Tumor Segmentation via Modified 3D U-Net with Transformer / _cHussain Nasir Khan
264			_aIslamabad : _bSMME- NUST; _c2023.
300			_a61p. _bSoft Copy _c30cm
520			_aAccurate brain tumor segmentation in 3D magnetic resonance imaging (MRI) scans is pivotal for medical diagnosis and treatment planning. This master’s thesis introduces an advanced approach for multimodal 3D-MRI brain tumor segmentation. Our methodology combines a modified 3D U-Net architecture with Transformer-based self-attention, and dilated convolution layers. By leveraging multimodal information from T1-weighted, T2-weighted, T1 Contrast Enhanced (T1CE) Imaging and FLAIR MRI sequences within the BraTS 2023 dataset, our method significantly enhances segmentation precision. The modified 3D U-Net with Transformer and dilated convolution layers enables effective capture of both local and global contextual information, facilitating the identification of complex structures and long-range dependencies within volumetric MRI data. Thorough experimentation and evaluation, including comparisons with 3D U-Net and its variants, highlight the superiority of our proposed model in terms of brain tumor delineation accuracy. These findings emphasize the potential of this hybrid architecture to advance medical image analysis, providing substantial benefits to patient care.
650			_aMS Robotics and Intelligent Machine Engineering _9119486
700			_aSupervisor : Dr. Kashif Javed _9119487
856			_uhttp://10.250.8.41:8080/xmlui/handle/123456789/39246
942			_2ddc _cTHE
999			_c607213 _d607213