Comparative Analysis of Brain Tumor Image Segmentation Performance of 2D U-Net and 3D U-Nets with Alternative Normalization Methods
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김태준 | - |
dc.contributor.author | 김영재 | - |
dc.contributor.author | 김광기 | - |
dc.date.accessioned | 2024-07-21T13:00:24Z | - |
dc.date.available | 2024-07-21T13:00:24Z | - |
dc.date.issued | 2024-06 | - |
dc.identifier.issn | 2383-7632 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/92050 | - |
dc.description.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 | - |
dc.format.extent | 10 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | 한국멀티미디어학회 | - |
dc.title | Comparative Analysis of Brain Tumor Image Segmentation Performance of 2D U-Net and 3D U-Nets with Alternative Normalization Methods | - |
dc.type | Article | - |
dc.identifier.doi | 10.33851/JMIS.2024.11.2.157 | - |
dc.identifier.bibliographicCitation | Journal of Multimedia Information System, v.11, no.2, pp 157 - 166 | - |
dc.identifier.kciid | ART003098126 | - |
dc.description.isOpenAccess | N | - |
dc.citation.endPage | 166 | - |
dc.citation.startPage | 157 | - |
dc.citation.title | Journal of Multimedia Information System | - |
dc.citation.volume | 11 | - |
dc.citation.number | 2 | - |
dc.publisher.location | 대한민국 | - |
dc.subject.keywordAuthor | Brain Image Segmentation | - |
dc.subject.keywordAuthor | Machine Learning | - |
dc.subject.keywordAuthor | Deep Learning | - |
dc.subject.keywordAuthor | Tumor | - |
dc.description.journalRegisteredClass | kci | - |
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