Deep learning for segmentation of ischemic stroke lesions from MRI scans
Convolutional Neural Networks (CNN) such as U-Net have been extensively used for medical image segmentation tasks. However, CNNs have limitations in learning global feature representations in images due to their local receptive fields. This prompted researchers to look at other methods to improve...
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sg-ntu-dr.10356-1664552023-05-05T15:41:29Z Deep learning for segmentation of ischemic stroke lesions from MRI scans Yuen, Hing Yee Jagath C Rajapakse School of Computer Science and Engineering ASJagath@ntu.edu.sg Engineering::Computer science and engineering Convolutional Neural Networks (CNN) such as U-Net have been extensively used for medical image segmentation tasks. However, CNNs have limitations in learning global feature representations in images due to their local receptive fields. This prompted researchers to look at other methods to improve the modelling of global dependencies. In recent years, transformer-like architectures with self-attention mechanisms for modelling long-range dependencies have shown better performance than CNN-based architectures in computer vision applications. In this project, deep learning techniques involving transformer-like architectures were explored for ischemic stroke lesion segmentation from Magnetic Resonance Imaging (MRI) scans. Specifically, this study focused on using the hybrid CNN-Transformer network, UNet Transformers (UNETR), to compare with pure CNN architectures. Furthermore, to address the issue of transformers needing large amounts of labelled data to perform well, the self-supervised learning method, Self-Distillation with No Labels (DINO), was utilised for network pre-training. In order for the DINO approach to work on the 3D-based UNETR architecture, we have modified the method to accept 3D images as input. We demonstrated that with the pre-trained DINO weights, the ischemic stroke lesion segmentation performance of UNETR improved when supervised learning was performed on the Anatomical Tracings of Lesions After Stroke - Release 1.2 (ATLASR1.2) dataset. Bachelor of Engineering Science (Computer Science) 2023-05-02T06:31:54Z 2023-05-02T06:31:54Z 2023 Final Year Project (FYP) Yuen, H. Y. (2023). Deep learning for segmentation of ischemic stroke lesions from MRI scans. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166455 https://hdl.handle.net/10356/166455 en application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering Yuen, Hing Yee Deep learning for segmentation of ischemic stroke lesions from MRI scans |
description |
Convolutional Neural Networks (CNN) such as U-Net have been extensively used for medical
image segmentation tasks. However, CNNs have limitations in learning global feature
representations in images due to their local receptive fields. This prompted researchers to
look at other methods to improve the modelling of global dependencies. In recent years,
transformer-like architectures with self-attention mechanisms for modelling long-range
dependencies have shown better performance than CNN-based architectures in computer
vision applications.
In this project, deep learning techniques involving transformer-like architectures were
explored for ischemic stroke lesion segmentation from Magnetic Resonance Imaging (MRI)
scans. Specifically, this study focused on using the hybrid CNN-Transformer network, UNet
Transformers (UNETR), to compare with pure CNN architectures. Furthermore, to address
the issue of transformers needing large amounts of labelled data to perform well, the
self-supervised learning method, Self-Distillation with No Labels (DINO), was utilised for
network pre-training. In order for the DINO approach to work on the 3D-based UNETR
architecture, we have modified the method to accept 3D images as input.
We demonstrated that with the pre-trained DINO weights, the ischemic stroke lesion
segmentation performance of UNETR improved when supervised learning was performed on
the Anatomical Tracings of Lesions After Stroke - Release 1.2 (ATLASR1.2) dataset. |
author2 |
Jagath C Rajapakse |
author_facet |
Jagath C Rajapakse Yuen, Hing Yee |
format |
Final Year Project |
author |
Yuen, Hing Yee |
author_sort |
Yuen, Hing Yee |
title |
Deep learning for segmentation of ischemic stroke lesions from MRI scans |
title_short |
Deep learning for segmentation of ischemic stroke lesions from MRI scans |
title_full |
Deep learning for segmentation of ischemic stroke lesions from MRI scans |
title_fullStr |
Deep learning for segmentation of ischemic stroke lesions from MRI scans |
title_full_unstemmed |
Deep learning for segmentation of ischemic stroke lesions from MRI scans |
title_sort |
deep learning for segmentation of ischemic stroke lesions from mri scans |
publisher |
Nanyang Technological University |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/166455 |
_version_ |
1765213822929338368 |