3D-2D distance maps conversion enhances classification of craniosynostosis
Objective: Diagnosis of craniosynostosis using photogrammetric 3D surface scans is a promising radiation-free alternative to traditional computed tomography. We propose a 3D surface scan to 2D distance map conversion enabling the usage of the first convolutional neural networks (CNNs)-based classifi...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
November 2023
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| In: |
IEEE transactions on biomedical engineering
Year: 2023, Volume: 70, Issue: 11, Pages: 3156-3165 |
| ISSN: | 1558-2531 |
| DOI: | 10.1109/TBME.2023.3278030 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1109/TBME.2023.3278030 Verlag, kostenfrei, Volltext: https://ieeexplore.ieee.org/document/10129889/authors#authors 
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| Author Notes: | Matthias Schaufelberger, Christian Kaiser, Reinald Kühle, Andreas Wachter, Frederic Weichel, Niclas Hagen, Friedemann Ringwald, Urs Eisenmann, Jürgen Hoffmann, Michael Engel, Christian Freudlsperger, and Werner Nahm |
| Summary: | Objective: Diagnosis of craniosynostosis using photogrammetric 3D surface scans is a promising radiation-free alternative to traditional computed tomography. We propose a 3D surface scan to 2D distance map conversion enabling the usage of the first convolutional neural networks (CNNs)-based classification of craniosynostosis. Benefits of using 2D images include preserving patient anonymity, enabling data augmentation during training, and a strong under-sampling of the 3D surface with good classification performance. Methods: The proposed distance maps sample 2D images from 3D surface scans using a coordinate transformation, ray casting, and distance extraction. We introduce a CNN-based classification pipeline and compare our classifier to alternative approaches on a dataset of 496 patients. We investigate into low-resolution sampling, data augmentation, and attribution mapping. Results: Resnet18 outperformed alternative classifiers on our dataset with an F1-score of 0.964 and an accuracy of 98.4%. Data augmentation on 2D distance maps increased performance for all classifiers. Under-sampling allowed 256-fold computation reduction during ray casting while retaining an F1-score of 0.92. Attribution maps showed high amplitudes on the frontal head. Conclusion: We demonstrated a versatile mapping approach to extract a 2D distance map from the 3D head geometry increasing classification performance, enabling data augmentation during training on 2D distance maps, and the usage of CNNs. We found that low-resolution images were sufficient for a good classification performance. Significance: Photogrammetric surface scans are a suitable craniosynostosis diagnosis tool for clinical practice. Domain transfer to computed tomography seems likely and can further contribute to reducing ionizing radiation exposure for infants. |
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| Item Description: | Gesehen am 06.02.2024 |
| Physical Description: | Online Resource |
| ISSN: | 1558-2531 |
| DOI: | 10.1109/TBME.2023.3278030 |