Atten‐SEVNETR for volumetric segmentation of glioblastoma and interactive refinement to limit over‐segmentation
Abstract 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 err...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Image Processing |
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| Online Access: | https://doi.org/10.1049/ipr2.13218 |
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| author | Swagata Kundu Dimitrios Toumpanakis Johan Wikstrom Robin Strand Ashis Kumar Dhara |
| author_facet | Swagata Kundu Dimitrios Toumpanakis Johan Wikstrom Robin Strand Ashis Kumar Dhara |
| author_sort | Swagata Kundu |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-94a285fc57444cabbcf8491dc28fc6e2 |
| institution | OA Journals |
| issn | 1751-9659 1751-9667 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj-art-94a285fc57444cabbcf8491dc28fc6e22025-08-20T02:35:53ZengWileyIET Image Processing1751-96591751-96672024-12-0118144928494310.1049/ipr2.13218Atten‐SEVNETR for volumetric segmentation of glioblastoma and interactive refinement to limit over‐segmentationSwagata Kundu0Dimitrios Toumpanakis1Johan Wikstrom2Robin Strand3Ashis Kumar Dhara4Electrical Engineering Department National Institute of Technology Durgapur Durgapur West Bengal IndiaDepartment of Surgical Sciences Neuroradiology Uppsala University Uppsala SwedenDepartment of Surgical Sciences Neuroradiology Uppsala University Uppsala SwedenDepartment of Information Technology Centre for Image Analysis Uppsala University Uppsala SwedenElectrical Engineering Department National Institute of Technology Durgapur Durgapur West Bengal IndiaAbstract 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.https://doi.org/10.1049/ipr2.13218interactive correctionpost‐operative glioblastomavision transformervolumetric segmentation |
| spellingShingle | Swagata Kundu Dimitrios Toumpanakis Johan Wikstrom Robin Strand Ashis Kumar Dhara Atten‐SEVNETR for volumetric segmentation of glioblastoma and interactive refinement to limit over‐segmentation IET Image Processing interactive correction post‐operative glioblastoma vision transformer volumetric segmentation |
| title | Atten‐SEVNETR for volumetric segmentation of glioblastoma and interactive refinement to limit over‐segmentation |
| title_full | Atten‐SEVNETR for volumetric segmentation of glioblastoma and interactive refinement to limit over‐segmentation |
| title_fullStr | Atten‐SEVNETR for volumetric segmentation of glioblastoma and interactive refinement to limit over‐segmentation |
| title_full_unstemmed | Atten‐SEVNETR for volumetric segmentation of glioblastoma and interactive refinement to limit over‐segmentation |
| title_short | Atten‐SEVNETR for volumetric segmentation of glioblastoma and interactive refinement to limit over‐segmentation |
| title_sort | atten sevnetr for volumetric segmentation of glioblastoma and interactive refinement to limit over segmentation |
| topic | interactive correction post‐operative glioblastoma vision transformer volumetric segmentation |
| url | https://doi.org/10.1049/ipr2.13218 |
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