ArticleInternational journal of computer assisted radiology and surgery2017
A pilot study for segmentation of pharyngeal and sino-nasal airway subregions by automatic contour initialization.
Article in International journal of computer assisted radiology and surgery, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of the techniques for automatic segmentation of the human upper airway using volumetric images.Medical & biological engineering & computing · 2023Pooled it
- Spine-anchored active contour-based renal spatial prioritization for precision-constrained volumetric segmentation of bilateral nephric tumors via spine-guided 3D deep encoders in abdominopelvic tomographic data.Radiological physics and technology · 2026Article
- Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images.Dento maxillo facial radiology · 2024Article
- Deep Learning Models for Automatic Upper Airway Segmentation and Minimum Cross-Sectional Area Localisation in Two-Dimensional Images.Bioengineering (Basel, Switzerland) · 2023Article
- The Evolution and Application of Artificial Intelligence in Rhinology: A State of the Art Review.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2023Review
- Artificial Intelligence as an Aid in CBCT Airway Analysis: A Systematic Review.Life (Basel, Switzerland) · 2022Review
- Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images.Scientific reports · 2022Article
- Segmentation procedures for the assessment of paranasal sinuses volumes.The neuroradiology journal · 2021Review
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Authors and funding
8 authors.
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Abstract
purposeThe objective of the present study is to put forward a novel automatic segmentation algorithm to segment pharyngeal and sino-nasal airway subregions on 3D CBCT imaging datasets.
methodsA fully automatic segmentation of sino-nasal and pharyngeal airway subregions was implemented in MATLAB programing environment. The novelty of the algorithm is automatic initialization of contours in upper airway subregions. The algorithm is based on boundary definitions of the human anatomy along with shape constraints with an automatic initialization of contours to develop a complete algorithm which has a potential to enhance utility at clinical level. Post-initialization; five segmentation techniques: Chan-Vese level set (CVL), localized Chan-Vese level set (LCVL), Bhattacharya distance level set (BDL), Grow Cut (GC), and Sparse Field method (SFM) were used to test the robustness of automatic initialization.
resultsPrecision and F-score were found to be greater than 80% for all the regions with all five segmentation methods. High precision and low recall were observed with BDL and GC techniques indicating an under segmentation. Low precision and high recall values were observed with CVL and SFM methods indicating an over segmentation. A Larger F-score value was observed with SFM method for all the subregions. Minimum F-score value was observed for naso-ethmoidal and sphenoidal air sinus region, whereas a maximum F-score was observed in maxillary air sinuses region. The contour initialization was more accurate for maxillary air sinuses region in comparison with sphenoidal and naso-ethmoid regions.
conclusionThe overall F-score was found to be greater than 80% for all the airway subregions using five segmentation techniques, indicating accurate contour initialization. Robustness of the algorithm needs to be further tested on severely deformed cases and on cases with different races and ethnicity for it to have global acceptance in Katradental radKatraiology workflow.
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