Liver Segmentation in Time-resolved C-arm CT Volumes Reconstructed from Dynamic Perfusion Scans using Time Separation Technique

TL;DR

This paper demonstrates that Turbolift learning, when applied to time-resolved C-arm CT volumes reconstructed via the time separation technique, achieves accurate liver segmentation with high Dice scores, aiding in liver movement tracking.

Contribution

It introduces the application of Turbolift learning to time-resolved volumes from TST reconstructions, showing its robustness and effectiveness for liver segmentation.

Findings

Achieved a Dice score of 0.864±0.004 on TRV liver segmentation.

Turbolift learning performs well even with time-resolved volumes from TST.

Enhanced visualization and tracking of liver movement over time.

Abstract

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.