Comparative Analysis of Brain Tumor Image Segmentation Performance of 2D U-Net and 3D U-Nets with Alternative Normalization Methods
- Authors
- 김태준; 김영재; 김광기
- Issue Date
- Jun-2024
- Publisher
- 한국멀티미디어학회
- Keywords
- Brain Image Segmentation; Machine Learning; Deep Learning; Tumor
- Citation
- Journal of Multimedia Information System, v.11, no.2, pp 157 - 166
- Pages
- 10
- Journal Title
- Journal of Multimedia Information System
- Volume
- 11
- Number
- 2
- Start Page
- 157
- End Page
- 166
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/92050
- DOI
- 10.33851/JMIS.2024.11.2.157
- ISSN
- 2383-7632
- Abstract
- Advancements in deep learning-based brain tumor image segmentation have significantly contributed to the rapid and accurate diagnosis of brain tumors. U-Net, a deep learning model used for brain tumor image segmentation, serves as the basic architecture for many such models. Although U-Nets are categorized into two-dimensional (2D) and three-dimensional (3D) models, it remains unclear which model is more effective for brain tumor image segmentation. Therefore, this study compared the performances of 2D and 3D U-Net models for brain tumor image segmentation. In this study, for 2D U-Net, we employed batch normalization. For the 3D models, three variants with distinct normalization techniques were used: 3D BN U-Net with batch normalization, 3D GN U-Net with group normalization, and 3D IN U-Net with instance normalization. The dataset consisted of brain MRI images from 600 patients with brain tumors and expert-labeled mask images.
Experiments were conducted using 5-fold cross-validation, and the results revealed that the 3D GN and IN models outperformed the 2D model. In conclusion, for U-Net models in brain tumor image segmentation, the 3D GN and IN U-Net models, which replaced batch normal ization with group normalization or instance normalization, proved to be the most effective
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/92050)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.