Deep learning for segmentation of brain lesions from CT scans
Intracranial Hemorrhage (ICH) is a brain abnormality that occurs when blood vessels rupture and acute bleeding occurs within the brain. Urgent treatment is treatment as ICH can result in hemorrhagic stroke, which is a potentially fatal and neurologically damaging condition. The most common modality...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/166225 |
| _version_ | 1826123278076346368 |
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| author | Chin, Luke Peng Hao |
| author2 | Jagath C Rajapakse |
| author_facet | Jagath C Rajapakse Chin, Luke Peng Hao |
| author_sort | Chin, Luke Peng Hao |
| collection | NTU |
| description | Intracranial Hemorrhage (ICH) is a brain abnormality that occurs when blood vessels rupture and acute bleeding occurs within the brain. Urgent treatment is treatment as ICH can result in hemorrhagic stroke, which is a potentially fatal and neurologically damaging condition. The most common modality of ICH diagnosis is through Computed Tomography (CT) scans, which require an experienced radiologist to analyse these scans. Hence, the aim of the project is to accelerate the diagnosis process by developing automatic deep learning models to help segment the ICH lesions produced from CT scans. A novel method is proposed, which leverages bounding boxes to help with the segmentation of ICH lesions. Our experiments showed that there were significant improvements in the segmentation results when the lesions underwent these steps compared to direct segmentation, and also provided insights for more ways to improve the segmentation process in the future. |
| first_indexed | 2024-10-01T06:02:02Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/166225 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:02:02Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1662252023-11-29T08:17:59Z Deep learning for segmentation of brain lesions from CT scans Chin, Luke Peng Hao Jagath C Rajapakse School of Computer Science and Engineering ASJagath@ntu.edu.sg Engineering::Computer science and engineering::Computer applications::Life and medical sciences Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Intracranial Hemorrhage (ICH) is a brain abnormality that occurs when blood vessels rupture and acute bleeding occurs within the brain. Urgent treatment is treatment as ICH can result in hemorrhagic stroke, which is a potentially fatal and neurologically damaging condition. The most common modality of ICH diagnosis is through Computed Tomography (CT) scans, which require an experienced radiologist to analyse these scans. Hence, the aim of the project is to accelerate the diagnosis process by developing automatic deep learning models to help segment the ICH lesions produced from CT scans. A novel method is proposed, which leverages bounding boxes to help with the segmentation of ICH lesions. Our experiments showed that there were significant improvements in the segmentation results when the lesions underwent these steps compared to direct segmentation, and also provided insights for more ways to improve the segmentation process in the future. Bachelor of Engineering (Computer Science) 2023-04-27T11:48:32Z 2023-04-27T11:48:32Z 2023 Final Year Project (FYP) Chin, L. P. H. (2023). Deep learning for segmentation of brain lesions from CT scans. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166225 https://hdl.handle.net/10356/166225 en SCSE22-0432 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computer applications::Life and medical sciences Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Chin, Luke Peng Hao Deep learning for segmentation of brain lesions from CT scans |
| title | Deep learning for segmentation of brain lesions from CT scans |
| title_full | Deep learning for segmentation of brain lesions from CT scans |
| title_fullStr | Deep learning for segmentation of brain lesions from CT scans |
| title_full_unstemmed | Deep learning for segmentation of brain lesions from CT scans |
| title_short | Deep learning for segmentation of brain lesions from CT scans |
| title_sort | deep learning for segmentation of brain lesions from ct scans |
| topic | Engineering::Computer science and engineering::Computer applications::Life and medical sciences Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision |
| url | https://hdl.handle.net/10356/166225 |
| work_keys_str_mv | AT chinlukepenghao deeplearningforsegmentationofbrainlesionsfromctscans |