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.
@article{arxiv.2507.16573,
title = {Semantic Segmentation for Preoperative Planning in Transcatheter Aortic Valve Replacement},
author = {Cedric Zöllner and Simon Reiß and Alexander Jaus and Amroalalaa Sholi and Ralf Sodian and Rainer Stiefelhagen},
journal= {arXiv preprint arXiv:2507.16573},
year = {2025}
}
Comments
Accepted at 16th MICCAI Workshop on Statistical Atlases and Computational Modeling of the Heart (STACOM)