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Atten‐SEVNETR for volumetric segmentation of glioblastoma and interactive refinement to limit over‐segmentation.

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Názov: Atten‐SEVNETR for volumetric segmentation of glioblastoma and interactive refinement to limit over‐segmentation.
Autori: Kundu, Swagata, Toumpanakis, Dimitrios, Wikstrom, Johan, Strand, Robin, Dhara, Ashis Kumar
Zdroj: IET Image Processing (Wiley-Blackwell); 12/11/2024, Vol. 18 Issue 14, p4928-4943, 16p
Predmety: MACHINE learning, TRANSFORMER models, CONVOLUTIONAL neural networks, GRAPHICAL user interfaces, GLIOBLASTOMA multiforme
Abstrakt: Precise localization and volumetric segmentation of glioblastoma before and after surgery are crucial for various clinical purposes, including post‐surgery treatment planning, monitoring tumour recurrence, and creating radiotherapy maps. Manual delineation is time‐consuming and prone to errors, hence the adoption of automated 3D quantification methods using deep learning algorithms from MRI scans in recent times. However, automated segmentation often leads to over‐segmentation or under‐segmentation of tumour regions. Introducing an interactive deep‐learning tool would empower radiologists to rectify these inaccuracies by adjusting the over‐segmented and under‐segmented voxels as needed. This paper proposes a network named Atten‐SEVNETR, that has a combined architecture of vision transformers and convolutional neural networks (CNN). This hybrid architecture helps to learn the input volume representation in sequences and focuses on the global multi‐scale information. An interactive graphical user interface is also developed where the initial 3D segmentation of glioblastoma can be interactively corrected to remove falsely detected spurious tumour regions. Atten‐SEVNETR is trained on BraTS training dataset and tested on BraTS validation dataset and on Uppsala University post‐operative glioblastoma dataset. The methodology outperformed state‐of‐the‐art networks like nnFormer, SwinUNet, and SwinUNETR. The mean dice score achieved is 0.7302, and the mean Hausdorff distance‐95 got is 7.78 mm for the Uppsala University dataset. [ABSTRACT FROM AUTHOR]
Copyright of IET Image Processing (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáza: Biomedical Index
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  Data: Atten‐SEVNETR for volumetric segmentation of glioblastoma and interactive refinement to limit over‐segmentation.
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  Data: <searchLink fieldCode="AR" term="%22Kundu%2C+Swagata%22">Kundu, Swagata</searchLink><br /><searchLink fieldCode="AR" term="%22Toumpanakis%2C+Dimitrios%22">Toumpanakis, Dimitrios</searchLink><br /><searchLink fieldCode="AR" term="%22Wikstrom%2C+Johan%22">Wikstrom, Johan</searchLink><br /><searchLink fieldCode="AR" term="%22Strand%2C+Robin%22">Strand, Robin</searchLink><br /><searchLink fieldCode="AR" term="%22Dhara%2C+Ashis+Kumar%22">Dhara, Ashis Kumar</searchLink>
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  Data: IET Image Processing (Wiley-Blackwell); 12/11/2024, Vol. 18 Issue 14, p4928-4943, 16p
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  Data: <searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br /><searchLink fieldCode="DE" term="%22TRANSFORMER+models%22">TRANSFORMER models</searchLink><br /><searchLink fieldCode="DE" term="%22CONVOLUTIONAL+neural+networks%22">CONVOLUTIONAL neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22GRAPHICAL+user+interfaces%22">GRAPHICAL user interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22GLIOBLASTOMA+multiforme%22">GLIOBLASTOMA multiforme</searchLink>
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  Data: Precise localization and volumetric segmentation of glioblastoma before and after surgery are crucial for various clinical purposes, including post‐surgery treatment planning, monitoring tumour recurrence, and creating radiotherapy maps. Manual delineation is time‐consuming and prone to errors, hence the adoption of automated 3D quantification methods using deep learning algorithms from MRI scans in recent times. However, automated segmentation often leads to over‐segmentation or under‐segmentation of tumour regions. Introducing an interactive deep‐learning tool would empower radiologists to rectify these inaccuracies by adjusting the over‐segmented and under‐segmented voxels as needed. This paper proposes a network named Atten‐SEVNETR, that has a combined architecture of vision transformers and convolutional neural networks (CNN). This hybrid architecture helps to learn the input volume representation in sequences and focuses on the global multi‐scale information. An interactive graphical user interface is also developed where the initial 3D segmentation of glioblastoma can be interactively corrected to remove falsely detected spurious tumour regions. Atten‐SEVNETR is trained on BraTS training dataset and tested on BraTS validation dataset and on Uppsala University post‐operative glioblastoma dataset. The methodology outperformed state‐of‐the‐art networks like nnFormer, SwinUNet, and SwinUNETR. The mean dice score achieved is 0.7302, and the mean Hausdorff distance‐95 got is 7.78 mm for the Uppsala University dataset. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of IET Image Processing (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1049/ipr2.13218
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        Text: English
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      – SubjectFull: GLIOBLASTOMA multiforme
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      – TitleFull: Atten‐SEVNETR for volumetric segmentation of glioblastoma and interactive refinement to limit over‐segmentation.
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              Text: 12/11/2024
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