Korotina, Ekaterina (2022) Exploring and comparing data selection methods in the pre-processing step of a deep learning framework for automatic tumor segmentation on PET-CT images. Master's Thesis / Essay, Biomedical Engineering.
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Abstract
Automatic segmentation of primary tumors in oropharyngeal cancer patients using PET/CT images and deep learning has the potential to improve radiation oncology workflows. However, 2D tumor segmentation using deep learning is a data imbalance problem and a method of PET and CT slice selection affects the convergence of the deep learning model. The aim of the current project was to find a way to select sequences to improve the performance of the existing deep learning segmentation model. To select the 'right amount' of sequences without tumor in an unsupervised manner, clustering methods were explored. The trained clustering algorithms were used to group the training and validation data of the existing segmentation model in into clusters. The performance of the proposed method was assessed using the existing segmentation model. The promising results of the proposed data selection method were confirmed by improved metrics of the segmentation model (mean dice score coefficient, precision and recall).
| Item Type: | Thesis (Master's Thesis / Essay) |
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| Supervisor name: | Ooijen, P.M.A. van |
| Degree programme: | Biomedical Engineering |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 23 Aug 2022 10:42 |
| Last Modified: | 23 Aug 2022 10:42 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/28303 |
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