Liver Segmentation in Time-resolved C-arm CT Volumes Reconstructed from Dynamic Perfusion Scans using Time Separation Technique
| Autor: | Soumick Chatterjee, Hana Haseljić, Robert Frysch, Vojtěch Kulvait, Vladimir Semshchikov, Bennet Hensen, Frank Wacker, Inga Brüsch, Thomas Werncke, Oliver Speck, Andreas Nürnberger, Georg Rose |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) FOS: Electrical engineering electronic engineering information engineering Computer Science - Computer Vision and Pattern Recognition FOS: Physical sciences Medical Physics (physics.med-ph) Electrical Engineering and Systems Science - Image and Video Processing Physics - Medical Physics Machine Learning (cs.LG) |
| Zdroj: | IEEE 1-7 (2022). doi:10.1109/IPAS55744.2022.10052849 2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS) : [Proceedings]-IEEE, 2022.-ISBN 978-1-6654-6219-8-doi:10.1109/IPAS55744.2022.10052849 2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS) : [Proceedings]-IEEE, 2022.-ISBN 978-1-6654-6219-8-doi:10.1109/IPAS55744.2022.100528492022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS), Genova, Italy, 2022-12-05-2022-12-07 |
| Popis: | Perfusion imaging is a valuable tool for diagnosing and treatment planning for liver tumours. The time separation technique (TST) has been successfully used for modelling C-arm cone-beam computed tomography (CBCT) perfusion data. The reconstruction can be accompanied by the segmentation of the liver - for better visualisation and for generating comprehensive perfusion maps. Recently introduced Turbolift learning has been seen to perform well while working with TST reconstructions, but has not been explored for the time-resolved volumes (TRV) estimated out of TST reconstructions. The segmentation of the TRVs can be useful for tracking the movement of the liver over time. This research explores this possibility by training the multi-scale attention UNet of Turbolift learning at its third stage on the TRVs and shows the robustness of Turbolift learning since it can even work efficiently with the TRVs, resulting in a Dice score of 0.864$\pm$0.004. |
| Databáze: | OpenAIRE |
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