Semantic Segmentation for Preoperative Planning in Transcatheter Aortic Valve Replacement
This work addresses preoperative planning for TAVR surgeries, providing incremental improvements in segmentation accuracy for medical doctors.
The paper tackles the problem of supporting preoperative planning for transcatheter aortic valve replacement (TAVR) by using semantic segmentation models to make anatomical structures measurable in CT scans, achieving a +1.27% Dice increase in performance through an adaptation to the loss function.
When preoperative planning for surgeries is conducted on the basis of medical images, artificial intelligence methods can support medical doctors during assessment. In this work, we consider medical guidelines for preoperative planning of the transcatheter aortic valve replacement (TAVR) and identify tasks, that may be supported via semantic segmentation models by making relevant anatomical structures measurable in computed tomography scans. We first derive fine-grained TAVR-relevant pseudo-labels from coarse-grained anatomical information, in order to train segmentation models and quantify how well they are able to find these structures in the scans. Furthermore, we propose an adaptation to the loss function in training these segmentation models and through this achieve a +1.27% Dice increase in performance. Our fine-grained TAVR-relevant pseudo-labels and the computed tomography scans we build upon are available at https://doi.org/10.5281/zenodo.16274176.