Lossless segmentation of Brain Tumors from MRI images using 3D U-Net
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
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| Định dạng: | Luận văn |
| Ngôn ngữ: | English |
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Brac University
2022
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| Truy cập trực tuyến: | http://hdl.handle.net/10361/17166 |
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10361-17166 |
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10361-171662022-09-05T21:01:42Z Lossless segmentation of Brain Tumors from MRI images using 3D U-Net Farha, Ramisa Nuha, Nigar Sultana Sakib, Syed Nazmus Rafi, Sowat Hossain Khan, Md Sabbir Alam, Md. Ashraful Reza, Md Tanzim Department of Computer Science and Engineering, Brac University 3D CNN FCNs. 3D-Unet Segmentation Volumetric medical images 3D medical image processing Brain 3D MRI Image processing -- Digital techniques. Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 37-39). 2D computer vision and activities related to medical image analysis are remarkably guided with the help of Convolutional Neural networks (CNNs) in recent years. Since a chief portion in the available clinical imaging data is in 3D, we are inspired to further develop 3D CNNs for seeking the advantage of greater spatial context. Despite the fact that many FCNs are previously worked on and built by using various approaches, current 3D approaches still rely on patch processing due to the utilization of GPU memory, which limits the incorporation of bigger context information for improved performance. Using efficient 3D FCNs in MRI images without any data loss would result in more efficient disease detections. In this paper, we propose an approach to an efficient 3D U-net segmentation technique for MRI Images using a lossless preprocessing of an MRI image dataset. Our proposal has the advantage of an impressive reduction of the required GPU memory for 3D Medical Image processing activities and that too, with an enhanced performance which is evaluated by the IoU (Intersection over Union) evaluation metric. Comprehensive experiment results performed with MICCAI BraTS’20 exhibit the viability of the presented strategy. Ramisa Farha Nigar Sultana Nuha Syed Nazmus Sakib Sowat Hossain Rafi Md Sabbir Khan B. Computer Science 2022-09-05T09:33:04Z 2022-09-05T09:33:04Z 2022 2022-01 Thesis ID 18101406 ID 18101143 ID 18101160 ID 18101140 ID 18101274 http://hdl.handle.net/10361/17166 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 39 pages application/pdf Brac University |
| institution |
Brac University |
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Institutional Repository |
| language |
English |
| topic |
3D CNN FCNs. 3D-Unet Segmentation Volumetric medical images 3D medical image processing Brain 3D MRI Image processing -- Digital techniques. Neural networks (Computer science) |
| spellingShingle |
3D CNN FCNs. 3D-Unet Segmentation Volumetric medical images 3D medical image processing Brain 3D MRI Image processing -- Digital techniques. Neural networks (Computer science) Farha, Ramisa Nuha, Nigar Sultana Sakib, Syed Nazmus Rafi, Sowat Hossain Khan, Md Sabbir Lossless segmentation of Brain Tumors from MRI images using 3D U-Net |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Alam, Md. Ashraful |
| author_facet |
Alam, Md. Ashraful Farha, Ramisa Nuha, Nigar Sultana Sakib, Syed Nazmus Rafi, Sowat Hossain Khan, Md Sabbir |
| format |
Thesis |
| author |
Farha, Ramisa Nuha, Nigar Sultana Sakib, Syed Nazmus Rafi, Sowat Hossain Khan, Md Sabbir |
| author_sort |
Farha, Ramisa |
| title |
Lossless segmentation of Brain Tumors from MRI images using 3D U-Net |
| title_short |
Lossless segmentation of Brain Tumors from MRI images using 3D U-Net |
| title_full |
Lossless segmentation of Brain Tumors from MRI images using 3D U-Net |
| title_fullStr |
Lossless segmentation of Brain Tumors from MRI images using 3D U-Net |
| title_full_unstemmed |
Lossless segmentation of Brain Tumors from MRI images using 3D U-Net |
| title_sort |
lossless segmentation of brain tumors from mri images using 3d u-net |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/17166 |
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