ArticleIEEE transactions on medical imaging2019
Deep Geodesic Learning for Segmentation and Anatomical Landmarking.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.
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Who cites it
50 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis.La Radiologia medica · 2023Pooled it
- Automated detection of cephalometric landmarks using deep neural patchworks.Dento maxillo facial radiology · 2023Trial
- Automatic identification of anatomical landmarks in three-dimensional computed tomography/cone-beam computed tomography: a scoping review.Frontiers in dental medicine · 2026Review
- Deep Learning-based Detection of Hip Anatomical Landmarks in X-ray Images.The archives of bone and joint surgery · 2026Article
- A personalized automated system of 3D facial soft tissue landmarks annotation based on deep learning and computer vision.BMC oral health · 2025Article
- Trueness of artificial intelligence-based, manual, and global thresholding segmentation protocols for human mandibles.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2025Article
- Development of Artificial Intelligence-Supported Automatic Three-Dimensional Surface Cephalometry.Orthodontics & craniofacial research · 2025Article
- Multimodal deep learning for cephalometric landmark detection and treatment prediction.Scientific reports · 2025Article
- A User-Friendly Software for Automated Knowledge-Based Virtual Surgical Planning in Mandibular Reconstruction.Journal of clinical medicine · 2025Article
- Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study.BMC oral health · 2025Article
- SHAPE: A visual computing pipeline for interactive landmarking of 3D photograms and patient reporting for assessing craniosynostosis.Computers & graphics · 2024Article
- Clinical validity and precision of deep learning-based cone-beam computed tomography automatic landmarking algorithm.Imaging science in dentistry · 2024Article
- Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images.Dento maxillo facial radiology · 2024Article
- Dental Caries Detection and Classification in CBCT Images Using Deep Learning.International dental journal · 2024Article
- Exploring Fracture Patterns: Assessing Representation Methods for Bone Fracture Simulation.Journal of personalized medicine · 2024Article
- Performance of artificial intelligence using cone-beam computed tomography for segmentation of oral and maxillofacial structures: A systematic review and meta-analysis.Journal of clinical and experimental dentistry · 2023Review
- Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives.Healthcare (Basel, Switzerland) · 2023Review
- Geometric learning and statistical modeling for surgical outcomes evaluation in craniosynostosis using 3D photogrammetry.Computer methods and programs in biomedicine · 2023Article
- Joint Cranial Bone Labeling and Landmark Detection in Pediatric CT Images Using Context Encoding.IEEE transactions on medical imaging · 2023Article
- The Application of Deep Learning on CBCT in Dentistry.Diagnostics (Basel, Switzerland) · 2023Review
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Authors and funding
6 authors.
Funding
Abstract
In this paper, we propose a novel deep learning framework for anatomy segmentation and automatic landmarking. Specifically, we focus on the challenging problem of mandible segmentation from cone-beam computed tomography (CBCT) scans and identification of 9 anatomical landmarks of the mandible on the geodesic space. The overall approach employs three inter-related steps. In the first step, we propose a deep neural network architecture with carefully designed regularization, and network hyper-parameters to perform image segmentation without the need for data augmentation and complex post-processing refinement. In the second step, we formulate the landmark localization problem directly on the geodesic space for sparsely-spaced anatomical landmarks. In the third step, we utilize a long short-term memory network to identify the closely-spaced landmarks, which is rather difficult to obtain using other standard networks. The proposed fully automated method showed superior efficacy compared to the state-of-the-art mandible segmentation and landmarking approaches in craniofacial anomalies and diseased states. We used a very challenging CBCT data set of 50 patients with a high-degree of craniomaxillofacial variability that is realistic in clinical practice. The qualitative visual inspection was conducted for distinct CBCT scans from 250 patients with high anatomical variability. We have also shown the state-of-the-art performance in an independent data set from the MICCAI Head-Neck Challenge (2015).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.