# Towards Automatic Identification of Missing Tissues using a Geometric-Learning Correspondence Model

arXiv: https://arxiv.org/abs/2502.11265 · HTML: https://mlsanity.com/paper/2502.11265/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2025.

## Abstract (arXiv)

Missing tissue presents a big challenge for dose mapping, e.g., in the reirradiation setting. We propose a pipeline to identify missing tissue on intra-patient structure meshes using a previously trained geometric-learning correspondence model. For our application, we relied on the prediction discrepancies between forward and backward correspondences of the input meshes, quantified using a correspondence-based Inverse Consistency Error (cICE). We optimised the threshold applied to cICE to identify missing points in a dataset of 35 simulated mandible resections. Our identified threshold, 5.5 mm, produced a balanced accuracy score of 0.883 in the training data, using an ensemble approach. This pipeline produced plausible results for a real case where ~25% of the mandible was removed after a surgical intervention. The pipeline, however, failed on a more extreme case where ~50% of the mandible was removed. This is the first time geometric-learning modelling is proposed to identify missing points in corresponding anatomy.