Multimodal 3D-MRI Brain Tumor Segmentation via Modified 3D U-Net with Transformer / (Record no. 607213)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01881nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 629.8 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Khan, Hussain Nasir |
| 245 ## - TITLE STATEMENT | |
| Title | Multimodal 3D-MRI Brain Tumor Segmentation via Modified 3D U-Net with Transformer / |
| Statement of responsibility, etc. | Hussain Nasir Khan |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2023. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 61p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Accurate brain tumor segmentation in 3D magnetic resonance imaging (MRI) scans is<br/>pivotal for medical diagnosis and treatment planning. This master’s thesis introduces an<br/>advanced approach for multimodal 3D-MRI brain tumor segmentation. Our methodology<br/>combines a modified 3D U-Net architecture with Transformer-based self-attention, and dilated<br/>convolution layers. By leveraging multimodal information from T1-weighted, T2-weighted, T1<br/>Contrast Enhanced (T1CE) Imaging and FLAIR MRI sequences within the BraTS 2023 dataset,<br/>our method significantly enhances segmentation precision. The modified 3D U-Net with<br/>Transformer and dilated convolution layers enables effective capture of both local and global<br/>contextual information, facilitating the identification of complex structures and long-range<br/>dependencies within volumetric MRI data. Thorough experimentation and evaluation, including<br/>comparisons with 3D U-Net and its variants, highlight the superiority of our proposed model in<br/>terms of brain tumor delineation accuracy. These findings emphasize the potential of this hybrid<br/>architecture to advance medical image analysis, providing substantial benefits to patient care. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Robotics and Intelligent Machine Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Kashif Javed |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/39246">http://10.250.8.41:8080/xmlui/handle/123456789/39246</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 12/07/2023 | 629.8 | SMME-TH-933 | Thesis |