Semantic segmentation of tumor from 3D Structural MRI using U-Net Autoencoder
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.
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| Ngôn ngữ: | en_US |
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Brac University
2021
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| Truy cập trực tuyến: | http://hdl.handle.net/10361/14459 |
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10361-144592022-01-26T10:21:47Z Semantic segmentation of tumor from 3D Structural MRI using U-Net Autoencoder Farzana, Maisha Any, Md. Jahid Hossain Parvez, Mohammad Zavid Reza, Tanzim Department of Computer Science and Engineering, Brac University Brain Tumor Semantic Segmentation MRI CNN Pre-processing Autoencoder U-Net Architecture This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. Cataloged from PDF version of thesis. Includes bibliographical references (pages 37-40). Automated semantic segmentation of brain tumors from 3D MRI images plays a significant role in medical image processing. Early detection of these brain tumors is highly requisite for the treatment, screening, diagnosis and surgical pre-planning of the anomalies. The physicians normally follow the manual way of delineation to process the diagnosis of tumors which is time consuming, requires too much knowledge of anatomy and is too much expensive. To resolve these limitations, convolutional neural network (CNN) based autoencoder model is proposed which performs automated segmentation of brain tumors from 3D MRI brain images. Several algorithms such as image normalization, image augmentation, image binarization are used for data pre-processing. Furthermore, autoencoder based U-Net architecture is developed to extract the key features of the tumor and train the model. Later on, the model is applied to the new 3D MRI brain images to test the accuracy of it by segementing the tumor region. The proposed model enables enhancing the performance and accuracy of semantic segmentation of brain tumor as compare to the other existing models. Applying the proposed method, the accuracy is obtained upto 96.06% considering the 66 subjects. Finally, this approach is a well-structured model for segmenting the tumor region from MRI brain images which may assist the physicians for providing therapy and better treatment to the patient. Maisha Farzana Md. Jahid Hossain Any B. Computer Science 2021-06-01T06:28:33Z 2021-06-01T06:28:33Z 2020 2020-03 Thesis ID: 16101108 ID: 16101164 http://hdl.handle.net/10361/14459 en_US 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. 40 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
en_US |
| topic |
Brain Tumor Semantic Segmentation MRI CNN Pre-processing Autoencoder U-Net Architecture |
| spellingShingle |
Brain Tumor Semantic Segmentation MRI CNN Pre-processing Autoencoder U-Net Architecture Farzana, Maisha Any, Md. Jahid Hossain Semantic segmentation of tumor from 3D Structural MRI using U-Net Autoencoder |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. |
| author2 |
Parvez, Mohammad Zavid |
| author_facet |
Parvez, Mohammad Zavid Farzana, Maisha Any, Md. Jahid Hossain |
| format |
Thesis |
| author |
Farzana, Maisha Any, Md. Jahid Hossain |
| author_sort |
Farzana, Maisha |
| title |
Semantic segmentation of tumor from 3D Structural MRI using U-Net Autoencoder |
| title_short |
Semantic segmentation of tumor from 3D Structural MRI using U-Net Autoencoder |
| title_full |
Semantic segmentation of tumor from 3D Structural MRI using U-Net Autoencoder |
| title_fullStr |
Semantic segmentation of tumor from 3D Structural MRI using U-Net Autoencoder |
| title_full_unstemmed |
Semantic segmentation of tumor from 3D Structural MRI using U-Net Autoencoder |
| title_sort |
semantic segmentation of tumor from 3d structural mri using u-net autoencoder |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/14459 |
| work_keys_str_mv |
AT farzanamaisha semanticsegmentationoftumorfrom3dstructuralmriusingunetautoencoder AT anymdjahidhossain semanticsegmentationoftumorfrom3dstructuralmriusingunetautoencoder |
| _version_ |
1814309426003181568 |