ArticleDento maxillo facial radiology2019
Marker-based watershed transform method for fully automatic mandibular segmentation from CBCT images.
Article in Dento maxillo facial radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
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- A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases.Diagnostics (Basel, Switzerland) · 2023Review
- Whole mandibular canal segmentation using transformed dental CBCT volume in Frenet frame.Heliyon · 2023Article
- Effect of voxel size in cone-beam computed tomography on surface area measurements of dehiscences and fenestrations in the lower anterior buccal region.Clinical oral investigations · 2022Article
- Accuracy of advanced deep learning with tensorflow and keras for classifying teeth developmental stages in digital panoramic imaging.BMC medical imaging · 2022Article
- Digital chain for pelvic tumor resection with 3D-printed surgical cutting guides.Frontiers in bioengineering and biotechnology · 2022Article
- Automatic Segmentation of Mandibular Ramus and Condyles.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2021Article
- Automatic mandible segmentation from CT image using 3D fully convolutional neural network based on DenseASPP and attention gates.International journal of computer assisted radiology and surgery · 2021Article
- Mandible Segmentation of Dental CBCT Scans Affected by Metal Artifacts Using Coarse-to-Fine Learning Model.Journal of personalized medicine · 2021Article
- Decision Support Systems in Temporomandibular Joint Osteoarthritis: A review of Data Science and Artificial Intelligence Applications.Seminars in orthodontics · 2021Article
- Robust and Accurate Mandible Segmentation on Dental CBCT Scans Affected by Metal Artifacts Using a Prior Shape Model.Journal of personalized medicine · 2021Article
- Tracking-based deep learning method for temporomandibular joint segmentation.Annals of translational medicine · 2021Article
- A User-Friendly Protocol for Mandibular Segmentation of CBCT Images for Superimposition and Internal Structure Analysis.Journal of clinical medicine · 2021Article
- 3D assessment of mandibular skeletal effects produced by the Herbst appliance.BMC oral health · 2020Article
- Segmentation of dental cone-beam CT scans affected by metal artifacts using a mixed-scale dense convolutional neural network.Medical physics · 2019Article
- Quantification of mandibular sexual dimorphism during adolescence.Journal of anatomy · 2019Article
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9 authors.
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Abstract
objectives: To propose a reliable and practical method for automatically segmenting the mandible from CBCT images.
methods: The marker-based watershed transform is a region-growing approach that dilates or "floods" predefined markers onto a height map whose ridges denote object boundaries. We applied this method to segment the mandible from the rest of the CBCT image. The height map was generated to enhance the sharp decreases of intensity at the mandible/tissue border and suppress noise by computing the intensity gradient image of the CBCT itself. Two sets of markers, "mandible" and "background" were automatically placed inside and outside the mandible, respectively in a novel image using image registration. The watershed transform flooded the gradient image by dilating the markers simultaneously until colliding at watershed lines, estimating the mandible boundary. CBCT images of 20 adolescent subjects were chosen as test cases. Segmentation accuracy of the proposed method was evaluated by measuring overlap (Dice similarity coefficient) and boundary agreement against a well-accepted interactive segmentation method described in the literature.
results: The Dice similarity coefficient was 0.97 ± 0.01 (mean ± SD), indicating almost complete overlap between the automatically and the interactively segmented mandibles. Boundary deviations were predominantly under 1 mm for most of the mandibular surfaces. The errors were mostly from bones around partially erupted wisdom teeth, the condyles and the dental enamels, which had minimal impact on the overall morphology of the mandible.
conclusions: The marker-based watershed transform method produces segmentation accuracy comparable to the well-accepted interactive segmentation approach.
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