ArticleInternational journal of computer assisted radiology and surgery2020
Automatic CT image segmentation of maxillary sinus based on VGG network and improved V-Net.
Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 54 citations in OpenAlex.
- Comparisons of artificial intelligence automated segmentation techniques to manual segmentation techniques of the maxilla and maxillary sinus for CT or cone-beam CT scans-a systematic review.Dento maxillo facial radiology · 2025Pooled it
- A systematic review of the techniques for automatic segmentation of the human upper airway using volumetric images.Medical & biological engineering & computing · 2023Pooled it
- Automated detection and classification of maxillary sinus variations using slice-based and full-volume CBCT deep learning models.BMC oral health · 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
- A deep learning framework for automated dental segmentation and diagnostic report generation from cone-beam computed tomography.Head & face medicine · 2025Article
- Sex-Based Bias in Artificial Intelligence-Based Segmentation Models in Clinical Oncology.Clinical oncology (Royal College of Radiologists (Great Britain)) · 2025Review
- A platform combining automatic segmentation and automatic measurement of the maxillary sinus and adjacent structures.Clinical oral investigations · 2025Article
- Deep Learning for Automated Prediction of Sphenoid Sinus Pneumatization in Computed TomographyCurrent medical imaging · 2025Article
- Automatic maxillary sinus segmentation and pathology classification on cone-beam computed tomographic images using deep learning.BMC oral health · 2024Article
- Automatic planning of maxillary anterior dental implant based on prosthetically guided and pose evaluation indicator.International journal of computer assisted radiology and surgery · 2024Article
- Article
- Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images.Dento maxillo facial radiology · 2024Article
- Surgical Navigation in the Anterior Skull Base Using 3-Dimensional Endoscopy and Surface Reconstruction.JAMA otolaryngology-- head & neck surgery · 2024Article
- Automatic segmentation of orbital wall from CT images via a thin wall region supervision-based multi-scale feature search network.International journal of computer assisted radiology and surgery · 2023Article
- A Scoping Review of Artificial Intelligence Research in Rhinology.American journal of rhinology & allergy · 2023Article
- Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases.Diagnostics (Basel, Switzerland) · 2022Review
- Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks.International journal of computer assisted radiology and surgery · 2022Article
- Deep learning-based fully automatic segmentation of the maxillary sinus on cone-beam computed tomographic images.Scientific reports · 2022Article
- Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images.Scientific reports · 2022Article
- Computed Tomography Texture Features and Risk Factor Analysis of Postoperative Recurrence of Patients with Advanced Gastric Cancer after Radical Treatment under Artificial Intelligence Algorithm.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
purposeThe analysis of the maxillary sinus (MS) can provide an assessment for many clinical diagnoses, so accurate CT image segmentation of the MS is essential. However, common segmentation methods are mainly done by experienced doctors manually, and there are some challenges such as low efficiency and precision. As for automatic methods, the initial seed points and adjustment of various parameters are required, which will affect the segmentation efficiency. Thus, accurate, efficient, and automatic segmentation method of MS is critical to promote the clinical application.
methodsThis paper proposed an automatic CT image segmentation method of MS based on VGG network and improved V-Net. The VGG network was established to classify CT slices, which can avoid the failure of CT slice segmentation without MS. Then, we proposed the improved V-Net based on edge supervision for segmenting MS regions more effectively. The edge loss was integrated into the loss of the improved V-Net, which could reduce region misjudgment and improve the automatic segmentation performance.
resultsFor the classification of CT slices with MS and without MS, the VGG network had a classification accuracy of 97.04 ± 2.03%. In the segmentation, our method obtained a better result, in which the segmentation Dice reached 94.40 ± 2.07%, the Iou (intersection over union) was 90.05 ± 3.26%, and the precision was 94.72 ± 2.64%. Compared with U-Net and V-Net, it reduced region misjudgment significantly and improved the segmentation accuracy. By analyzing the error map of 3D reconstruction, it was mainly distributed in ± 1 mm, which demonstrated that our result was quite close to the ground truth.
conclusionThe segmentation of the MS can be realized efficiently, accurately, and automatically by our method. Meanwhile, it not only has a better segmentation result, but also improves the doctor's work efficiency, which will have significant impact on clinical applications in the future.
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Registered trials
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.