Pyramid pooling enhanced ResUNet for accurate 3D brain image segmentation
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
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Brac University
2024
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| Online Access: | http://hdl.handle.net/10361/22665 |
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10361-226652024-04-24T21:05:10Z Pyramid pooling enhanced ResUNet for accurate 3D brain image segmentation Mollah, Md. Shawon Ahmed, Farhan Tanvir Chowdhury, Mahjabin Ahmed, Iftekhar Hasan, S. M. Rakib Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Segmentation U-Net ResUnet Volumetric CNN Convolutions Tumors 3D image DeeplabV3+ Pyramid pooling 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 and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 35-37). "Medical picture segmentation is important for clinical applications because it can offer valuable information on disease identification. With the inclusion of deep learning techniques, the original U-Net and ResUnet architecture has demonstrated excellent performance in 2D medical picture segmentation issues. However, it is still challenging to extend the U-Net to handle 3D volumetric medical images. This thesis proposed a redesigned ResUnet architecture with a hybrid model called pyramid pooling with enhanced ResUNet fusion with ResUnet and dialated spatial pyramid pooling from DeepLabV3+. Therefore, CNNs will effectively aid us in addressing the 3D segmentation problem. Accurate segmentation of 3D brain pictures is critical in neuroimaging research because it allows for exact anatomical localization and quantitative analysis. In this paper, we introduce a novel framework for robust and high-fidelity 3D brain image segmentation that combines the capability of Dilated Spatial Pyramid Pooling (DSPP) with the Residual U-Net (ResUNet) architecture. The ResUNet’s DSPP module improves multi-scale feature representation by aggregating information across several spatial resolutions, allowing the network which represent feature context. This integration tackles the issues given by complicated brain architecture as well as the unpredictability in picture quality that is frequent in real-world datasets in a synergistic manner. The model can comprehend complex patterns and recognize minute details in medical images thanks to attention processes, residual connections, and feature fusion methods. Brain tumors are divided in the research into medical images where the clinical data or benchmark datasets will be used to assess the proposed model. In order to assess segmentation accuracy and contrast it with cutting-edge techniques, The Dice similarity coefficient metrics will be used. This paper will create a novel and efficient 3D image segmentation framework using a modified ResUNet architecture and enhanced pyramid pooling from DeeplabV3+." Md. Shawon Mollah Farhan Tanvir Ahmed Mahjabin Chowdhury Iftekhar Ahmed S. M. Rakib Hasan B.Sc. in Computer Science 2024-04-24T06:21:38Z 2024-04-24T06:21:38Z 2023 2023-09 Thesis ID 19201103 ID 19201107 ID 19201110 ID 19201097 ID 22241038 http://hdl.handle.net/10361/22665 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. 37 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Segmentation U-Net ResUnet Volumetric CNN Convolutions Tumors 3D image DeeplabV3+ Pyramid pooling Image processing--Digital techniques. Neural networks (Computer science) |
| spellingShingle |
Segmentation U-Net ResUnet Volumetric CNN Convolutions Tumors 3D image DeeplabV3+ Pyramid pooling Image processing--Digital techniques. Neural networks (Computer science) Mollah, Md. Shawon Ahmed, Farhan Tanvir Chowdhury, Mahjabin Ahmed, Iftekhar Hasan, S. M. Rakib Pyramid pooling enhanced ResUNet for accurate 3D brain image segmentation |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. |
| author2 |
Alam, Md. Golam Rabiul |
| author_facet |
Alam, Md. Golam Rabiul Mollah, Md. Shawon Ahmed, Farhan Tanvir Chowdhury, Mahjabin Ahmed, Iftekhar Hasan, S. M. Rakib |
| format |
Thesis |
| author |
Mollah, Md. Shawon Ahmed, Farhan Tanvir Chowdhury, Mahjabin Ahmed, Iftekhar Hasan, S. M. Rakib |
| author_sort |
Mollah, Md. Shawon |
| title |
Pyramid pooling enhanced ResUNet for accurate 3D brain image segmentation |
| title_short |
Pyramid pooling enhanced ResUNet for accurate 3D brain image segmentation |
| title_full |
Pyramid pooling enhanced ResUNet for accurate 3D brain image segmentation |
| title_fullStr |
Pyramid pooling enhanced ResUNet for accurate 3D brain image segmentation |
| title_full_unstemmed |
Pyramid pooling enhanced ResUNet for accurate 3D brain image segmentation |
| title_sort |
pyramid pooling enhanced resunet for accurate 3d brain image segmentation |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/22665 |
| work_keys_str_mv |
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