ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2021
Automatic Segmentation of Mandibular Ramus and Condyles.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2021. 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 localization of mandibular landmarks in the construction of mandibular median sagittal plane.European journal of medical research · 2024Trial
- Can Deep Learning Methods Differentiate Temporomandibular Joint Disorders From Healthy Joints? A 3D Artificial Intelligence Algorithm Study Based on CBCT Images.Journal of oral rehabilitation · 2026Article
- CBCT assisted diagnosis system for temporomandibular joint disc displacement based on deep learning.Progress in orthodontics · 2026Article
- A Deep Learning Approach for Mandibular Condyle Segmentation on Ultrasonography.Journal of imaging informatics in medicine · 2026Article
- Automated condylar seating assessment using a deep learning-based three-step approach.Clinical oral investigations · 2024Article
- Dental Caries Detection and Classification in CBCT Images Using Deep Learning.International dental journal · 2024Article
- The Application of Deep Learning on CBCT in Dentistry.Diagnostics (Basel, Switzerland) · 2023Review
- A Semi-Automatic Approach for Holistic 3D Assessment of Temporomandibular Joint Changes.Journal of personalized medicine · 2023Article
- Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies.Bioengineering (Basel, Switzerland) · 2023Review
- Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
31 authors.
Funding
Abstract
In order to diagnose TMJ pathologies, we developed and tested a novel algorithm, MandSeg, that combines image processing and machine learning approaches for automatically segmenting the mandibular condyles and ramus. A deep neural network based on the U-Net architecture was trained for this task, using 109 cone-beam computed tomography (CBCT) scans. The ground truth label maps were manually segmented by clinicians. The U-Net takes 2D slices extracted from the 3D volumetric images. All the 3D scans were cropped depending on their size in order to keep only the mandibular region of interest. The same anatomic cropping region was used for every scan in the dataset. The scans were acquired at different centers with different resolutions. Therefore, we resized all scans to 512×512 in the pre-processing step where we also performed contrast adjustment as the original scans had low contrast. After the pre-processing, around 350 slices were extracted from each scan, and used to train the U-Net model. For the cross-validation, the dataset was divided into 10 folds. The training was performed with 60 epochs, a batch size of 8 and a learning rate of 2×10
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