ArticleInternational journal of computer assisted radiology and surgery2022
Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks.
Article in International journal of computer assisted radiology and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Automated detection of cephalometric landmarks using deep neural patchworks.Dento maxillo facial radiology · 2023Trial
- From Assistance to Autonomy: Acceptability of Progressive Artificial Intelligence Integration in Facial Reconstructive Surgery-Protocol for Within-Subjects Vignette Experiment Among Romanian Adults.Healthcare (Basel, Switzerland) · 2026Article
- Artificial Intelligence-enabled Automatic Computed Tomography Segmentation for Craniomaxillofacial Surgery Using a Real Surgical Database.Plastic and reconstructive surgery. Global open · 2026Article
- Deep learning model for automated segmentation of sphenoid sinus and middle skull base structures in CBCT volumes using nnU-Net v2.Oral radiology · 2026Article
- Automated orofacial virtual patient creation using two cohorts of MSCT vs. CBCT scans.Head & face medicine · 2025Article
- The Medical Segmentation Decathlon without a Doctorate.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Deep learning to predict cardiovascular mortality from aortic disease in heavy smokers.NPJ cardiovascular health · 2024Article
- Surgical Navigation in the Anterior Skull Base Using 3-Dimensional Endoscopy and Surface Reconstruction.JAMA otolaryngology-- head & neck surgery · 2024Article
- Article
- Deep learning segmentation results in precise delineation of the putamen in multiple system atrophy.European radiology · 2023Article
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10 authors.
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Abstract
purposeComputer-assisted techniques play an important role in craniomaxillofacial surgery. As segmentation of three-dimensional medical imaging represents a cornerstone for these procedures, the present study was aiming at investigating a deep learning approach for automated segmentation of head CT scans.
methodsThe deep learning approach of this study was based on the patchwork toolbox, using a multiscale stack of 3D convolutional neural networks. The images were split into nested patches using a fixed 3D matrix size with decreasing physical size in a pyramid format of four scale depths. Manual segmentation of 18 craniomaxillofacial structures was performed in 20 CT scans, of which 15 were used for the training of the deep learning network and five were used for validation of the results of automated segmentation. Segmentation accuracy was evaluated by Dice similarity coefficient (DSC), surface DSC, 95% Hausdorff distance (95HD) and average symmetric surface distance (ASSD).
resultsMean for DSC was 0.81 ± 0.13 (range: 0.61 [mental foramen] - 0.98 [mandible]). Mean Surface DSC was 0.94 ± 0.06 (range: 0.87 [mental foramen] - 0.99 [mandible]), with values > 0.9 for all structures but the mental foramen. Mean 95HD was 1.93 ± 2.05 mm (range: 1.00 [mandible] - 4.12 mm [maxillary sinus]) and for ASSD, a mean of 0.42 ± 0.44 mm (range: 0.09 [mandible] - 1.19 mm [mental foramen]) was found, with values < 1 mm for all structures but the mental foramen.
conclusionIn this study, high accuracy of automated segmentation of a variety of craniomaxillofacial structures could be demonstrated, suggesting this approach to be suitable for the incorporation into a computer-assisted craniomaxillofacial surgery workflow. The small amount of training data required and the flexibility of an open source-based network architecture enable a broad variety of clinical and research applications.
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