Electrical Engineering and Systems Science > Image and Video Processing
arXiv:2411.14418 (eess)
[Submitted on 21 Nov 2024]
Title:Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field
View a PDF of the paper titled Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field, by Lan Jiang and 5 other authors
View PDFHTML (experimental)Abstract:Accurate brain tumor segmentation remains a challenging task due to structural complexity and great individual differences of gliomas. Leveraging the pre-eminent detail resilience of CRF and spatial feature extraction capacity of V-net, we propose a multimodal 3D Volume Generative Adversarial Network (3D-vGAN) for precise segmentation. The model utilizes Pseudo-3D for V-net improvement, adds conditional random field after generator and use original image as supplemental guidance. Results, using the BraTS-2018 dataset, show that 3D-vGAN outperforms classical segmentation models, including U-net, Gan, FCN and 3D V-net, reaching specificity over 99.8%.
| Comments: | 13 pages, 7 figures, Annual Conference on Medical Image Understanding and Analysis (MIUA) 2024 |
| Subjects: | Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV) |
| MSC classes: | 15-11 |
| ACM classes: | I.4.6; I.5.4 |
| Cite as: | arXiv:2411.14418 [eess.IV] |
| (orarXiv:2411.14418v1 [eess.IV] for this version) | |
| https://doi.org/10.48550/arXiv.2411.14418 arXiv-issued DOI via DataCite | |
| Journal reference: | Medical Image Understanding and Analysis (MIUA), Lecture Notes in Computer Science, Springer, vol. 14859, 2024 |
| Related DOI: | https://doi.org/10.1007/978-3-031-66955-2_5 DOI(s) linking to related resources |
Submission history
From: Alessandro Perelli [view email][v1] Thu, 21 Nov 2024 18:52:02 UTC (2,143 KB)
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View a PDF of the paper titled Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field, by Lan Jiang and 5 other authors
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