Cerebrovascular Segmentation Architecture With Channel Attention And Spatial Kernel Filtering For Tof-Mra Images
This thesis introduces a deep learning approach to automatically segment cerebrovascular structures in magnetic resonance angiography (MRA) images. This study utilizes an approach that excels in segmenting the entire vessel structure while placing increased emphasis on accurately capturing small ves...
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| Format: | Thesis |
| Language: | English |
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2024
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| Online Access: | http://eprints.usm.my/63021/ |
| Abstract | Abstract here |
| _version_ | 1855633287652835328 |
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| author | Goni, Mohammad Raihan |
| author_facet | Goni, Mohammad Raihan |
| author_sort | Goni, Mohammad Raihan |
| description | This thesis introduces a deep learning approach to automatically segment cerebrovascular structures in magnetic resonance angiography (MRA) images. This study utilizes an approach that excels in segmenting the entire vessel structure while placing increased emphasis on accurately capturing small vessels (< 5 mm radius). The proposed method was evaluated on the MIDAS dataset, demonstrating its competitive performance with exceptional evaluation results. |
| first_indexed | 2025-12-24T00:13:18Z |
| format | Thesis |
| id | usm-63021 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2025-12-24T00:13:18Z |
| publishDate | 2024 |
| record_format | EPrints |
| record_pdf | Restricted |
| spelling | usm-630212025-10-21T08:56:16Z http://eprints.usm.my/63021/ Cerebrovascular Segmentation Architecture With Channel Attention And Spatial Kernel Filtering For Tof-Mra Images Goni, Mohammad Raihan QA75.5-76.95 Electronic computers. Computer science This thesis introduces a deep learning approach to automatically segment cerebrovascular structures in magnetic resonance angiography (MRA) images. This study utilizes an approach that excels in segmenting the entire vessel structure while placing increased emphasis on accurately capturing small vessels (< 5 mm radius). The proposed method was evaluated on the MIDAS dataset, demonstrating its competitive performance with exceptional evaluation results. 2024-01 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/63021/1/Pages%20from%20MD%20RAIHAN%20GONI%20-%20TESIS.pdf Goni, Mohammad Raihan (2024) Cerebrovascular Segmentation Architecture With Channel Attention And Spatial Kernel Filtering For Tof-Mra Images. Masters thesis, Universiti Sains Malaysia. |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Goni, Mohammad Raihan Cerebrovascular Segmentation Architecture With Channel Attention And Spatial Kernel Filtering For Tof-Mra Images |
| thesis_level | Master |
| title | Cerebrovascular Segmentation Architecture With Channel Attention And Spatial Kernel Filtering For Tof-Mra Images |
| title_full | Cerebrovascular Segmentation Architecture With Channel Attention And Spatial Kernel Filtering For Tof-Mra Images |
| title_fullStr | Cerebrovascular Segmentation Architecture With Channel Attention And Spatial Kernel Filtering For Tof-Mra Images |
| title_full_unstemmed | Cerebrovascular Segmentation Architecture With Channel Attention And Spatial Kernel Filtering For Tof-Mra Images |
| title_short | Cerebrovascular Segmentation Architecture With Channel Attention And Spatial Kernel Filtering For Tof-Mra Images |
| title_sort | cerebrovascular segmentation architecture with channel attention and spatial kernel filtering for tof mra images |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://eprints.usm.my/63021/ |
| work_keys_str_mv | AT gonimohammadraihan cerebrovascularsegmentationarchitecturewithchannelattentionandspatialkernelfilteringfortofmraimages |