PU-NET Deep Learning Architecture for Gliomas Brain Tumor Segmentation in Magnetic Resonance Images
Automatic medical image segmentation is one of the main tasks for many organs and pathology structures delineation. It is also a crucial technique in the posterior clinical examination of brain tumors, like applying radiotherapy or tumor restrictions. Various image segmentation techniques have been...
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| Format: | Article |
| Language: | English |
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Slovenian Society for Stereology and Quantitative Image Analysis
2023-11-01
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| Series: | Image Analysis and Stereology |
| Subjects: | |
| Online Access: | https://www.ias-iss.org/ojs/IAS/article/view/2879 |
| _version_ | 1797404343553490944 |
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| author | Yamina Azzi Abdelouhab Moussaoui Mohand-Tahar Kechadi |
| author_facet | Yamina Azzi Abdelouhab Moussaoui Mohand-Tahar Kechadi |
| author_sort | Yamina Azzi |
| collection | DOAJ |
| description | Automatic medical image segmentation is one of the main tasks for many organs and pathology structures delineation. It is also a crucial technique in the posterior clinical examination of brain tumors, like applying radiotherapy or tumor restrictions. Various image segmentation techniques have been proposed and applied to different image types. Recently, it has been shown that the deep learning approach accurately segments images, and its implementation is usually straightforward. In this paper, we proposed a novel approach, called PU-NET, for automatic brain tumor segmentation in multi-modal magnetic resonance images (MRI). We introduced an input processing block to a customized fully convolutional network derived from the U-Net network to handle the multi-modal inputs. We performed experiments over the Brain Tumor Segmentation (BRATS) dataset collected in 2018 and achieved Dice scores of 90.5%, 82.7%, and 80.3% for the whole tumor, tumor core, and enhancing tumor classes, respectively. This study provides promising results compared to the deep learning methods used in this context. |
| first_indexed | 2024-03-09T02:53:37Z |
| format | Article |
| id | doaj.art-e5f81545878640ce8fab77636e500970 |
| institution | Directory Open Access Journal |
| issn | 1580-3139 1854-5165 |
| language | English |
| last_indexed | 2024-03-09T02:53:37Z |
| publishDate | 2023-11-01 |
| publisher | Slovenian Society for Stereology and Quantitative Image Analysis |
| record_format | Article |
| series | Image Analysis and Stereology |
| spelling | doaj.art-e5f81545878640ce8fab77636e5009702023-12-05T08:17:32ZengSlovenian Society for Stereology and Quantitative Image AnalysisImage Analysis and Stereology1580-31391854-51652023-11-0142319720610.5566/ias.28791956PU-NET Deep Learning Architecture for Gliomas Brain Tumor Segmentation in Magnetic Resonance ImagesYamina Azzi0https://orcid.org/0000-0001-5871-8793Abdelouhab MoussaouiMohand-Tahar KechadiUniversity of Ferhat Abbes Setif 1 Setif, AlgeriaAutomatic medical image segmentation is one of the main tasks for many organs and pathology structures delineation. It is also a crucial technique in the posterior clinical examination of brain tumors, like applying radiotherapy or tumor restrictions. Various image segmentation techniques have been proposed and applied to different image types. Recently, it has been shown that the deep learning approach accurately segments images, and its implementation is usually straightforward. In this paper, we proposed a novel approach, called PU-NET, for automatic brain tumor segmentation in multi-modal magnetic resonance images (MRI). We introduced an input processing block to a customized fully convolutional network derived from the U-Net network to handle the multi-modal inputs. We performed experiments over the Brain Tumor Segmentation (BRATS) dataset collected in 2018 and achieved Dice scores of 90.5%, 82.7%, and 80.3% for the whole tumor, tumor core, and enhancing tumor classes, respectively. This study provides promising results compared to the deep learning methods used in this context.https://www.ias-iss.org/ojs/IAS/article/view/2879image segmentationdeep learningu-netgliomasbrain tumor |
| spellingShingle | Yamina Azzi Abdelouhab Moussaoui Mohand-Tahar Kechadi PU-NET Deep Learning Architecture for Gliomas Brain Tumor Segmentation in Magnetic Resonance Images Image Analysis and Stereology image segmentation deep learning u-net gliomas brain tumor |
| title | PU-NET Deep Learning Architecture for Gliomas Brain Tumor Segmentation in Magnetic Resonance Images |
| title_full | PU-NET Deep Learning Architecture for Gliomas Brain Tumor Segmentation in Magnetic Resonance Images |
| title_fullStr | PU-NET Deep Learning Architecture for Gliomas Brain Tumor Segmentation in Magnetic Resonance Images |
| title_full_unstemmed | PU-NET Deep Learning Architecture for Gliomas Brain Tumor Segmentation in Magnetic Resonance Images |
| title_short | PU-NET Deep Learning Architecture for Gliomas Brain Tumor Segmentation in Magnetic Resonance Images |
| title_sort | pu net deep learning architecture for gliomas brain tumor segmentation in magnetic resonance images |
| topic | image segmentation deep learning u-net gliomas brain tumor |
| url | https://www.ias-iss.org/ojs/IAS/article/view/2879 |
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