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

• URL: http://arxiv.org/abs/2502.11265v1
• Date: Sun, 16 Feb 2025 20:43:53 GMT
• Title: Towards Automatic Identification of Missing Tissues using a Geometric-Learning Correspondence Model
• Abstract summary: We propose a pipeline to identify missing tissue on intra-patient structure meshes.<n>This is the first time geometric-learning modelling is proposed to identify missing points in corresponding anatomy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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