| Autor: |
Chatterjee, Soumick, Haseljić, Hana, Frysch, Robert, Kulvait, Vojtěch, Semshchikov, Vladimir, Hensen, Bennet, Wacker, Frank, Brüschx, Inga, Werncke, Thomas, Speck, Oliver, Nürnberger, Andreas, Rose, Georg |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS) |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/IPAS55744.2022.10052849 |
| 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: |
arXiv |
| Externí odkaz: |
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