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ASE-Net for Segment...
ASE-Net for Segmentation of Post-operative Glioblastoma and Patient-specific Fine-tuning for Segmentation Refinement of Follow-up MRI Scans
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Kundu, Swagata (författare)
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- Banerjee, Subhashis (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Centre for Image Analysis
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- Breznik, Eva (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Centre for Image Analysis
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- Toumpanakis, Dimitrios (författare)
- Uppsala universitet,Radiologi
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- Wikström, Johan, Professor, 1964- (författare)
- Uppsala universitet,Radiologi
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- Strand, Robin, 1978- (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Centre for Image Analysis
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Kumar Dhara, Ashis (författare)
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(creator_code:org_t)
- Springer, 2024
- 2024
- Engelska.
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Ingår i: SN computer science. - : Springer. - 2661-8907. ; 5:106
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Volumetric quantification of tumors is usually done manually by radiologists requiring precious medical time and suffering from inter-observer variability. An automatic tool for accurate volume quantification of post-operative glioblastoma would reduce the workload of radiologists and improve the quality of follow-up monitoring and patient care. This paper deals with the 3-D segmentation of post-operative glioblastoma using channel squeeze and excitation based attention gated network (ASE-Net). The proposed deep neural network has a 3-D encoder and decoder based architecture with channel squeeze and excitation (CSE) blocks and attention blocks. The CSE block reduces the dependency on space information and put more emphasize on the channel information. The attention block suppresses the feature maps of irrelevant background and helps highlighting the relevant feature maps. The Uppsala university data set used has post-operative follow-up MRI scans for fifteen patients. A patient specific fine-tuning approach is used to improve the segmentation results for each patient. ASE-Net is also cross-validated with BraTS-2021 data set. The mean dice score of five-fold cross validation results with BraTS-2021 data set for enhanced tumor is 0.8244. The proposed network outperforms the competing networks like U-Net, Attention U-Net and Res U-Net. On the Uppsala University glioblastoma data set, the mean Dice score obtained with the proposed network is 0.7084, Hausdorff Distance-95 is 7.14 and the mean volumetric similarity achieved is 0.8579. With fine-tuning the pre-trained network, the mean dice score improved to 0.7368, Hausdorff Distance-95 decreased to 6.10 and volumetric similarity improved to 0.8736. ASE-Net outperforms the competing networks and can be used for volumetric quantification of post-operative glioblastoma from follow-up MRI scans. The network significantly reduces the probability of over segmentation.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Nyckelord
- Computerized Image Processing
- Datoriserad bildbehandling
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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