Point Cloud Registration for Measuring Shape Dependence of Soft Tissue Deformation by Digital Twins in Head and Neck Surgery

Abstract Introduction: A 2½ D point cloud registration method was developed to generate digital twins of different tissue shapes and resection cavities by applying a machine learning (ML) approach. This demonstrates the feasibility of quantifying soft tissue shifts. Methods: An ML model was trained...

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Bibliographic Details
Published in:Biomedicine hub 2024-01, Vol.9 (1), p.9-15
Main Authors: Monji-Azad, Sara, Männle, David, Hesser, Jürgen, Pohlmann, Jan, Rotter, Nicole, Affolter, Annette, Weis, Cleo Aron, Ludwig, Sonja, Scherl, Claudia
Format: Article
Language:English
Subjects:
artificial intelligence
Cadavers
Cameras
Cold
Computed tomography
digital twin
Digital twins
Head and neck
head and neck surgery
Photogrammetry
point cloud registration
Registration
Research Article
Soft tissues
Software
Surgery
Temperature
tissue shift
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Summary:Abstract Introduction: A 2½ D point cloud registration method was developed to generate digital twins of different tissue shapes and resection cavities by applying a machine learning (ML) approach. This demonstrates the feasibility of quantifying soft tissue shifts. Methods: An ML model was trained using simulated surface scan data obtained from tumor resections in a pig head cadaver model. It hereby uses 438 2½ D scans of the tissue surface. Tissue shift was induced by a temperature change from 7.91 ± 4.1°C to 36.37 ± 1.28°C. Results: Digital twins were generated from various branched and compact resection cavities (RCs) and cut tissues (CT). A temperature increase induced a tissue shift with a significant volume increase of 6 mL and 2 mL in branched and compact RCs, respectively (p = 0.0443; 0.0157). The volumes of branched and compact CT were decreased by 3 and 4 mL (p < 0.001). In the warm state, RC and CT no longer fit together because of the significant tissue deformation. Although not significant, the compact RC showed a greater tissue deformation of 1 μL than the branched RC with 0.5 μL induced by the temperature change (p = 0.7874). The branched and compact CT forms responded almost equally to changes in temperature (p = 0.1461). Conclusions: The simulation experiment of induced soft tissue deformation using digital twins based on 2½ D point cloud models proved that our method helps to quantify shape-dependent tissue shifts.
ISSN:2296-6870
2296-6862
2296-6870
DOI:10.1159/000535421