ArticleMachine learning in medical imaging. MLMI (Workshop)2021
SkullEngine: A Multi-Stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection.
Article in Machine learning in medical imaging. MLMI (Workshop), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- Reproducibility of Three-Dimensional Facial Surface Models Generated from Repeated CT and CBCT Scans: An Ex Vivo Study.Diagnostics (Basel, Switzerland) · 2026Article
- Accuracy and reliability of 3D cephalometric landmark detection with deep learning.European journal of medical research · 2025Article
- Automatic segmentation and landmark detection of 3D CBCT images using semi supervised learning for assisting orthognathic surgery planning.Scientific reports · 2025Article
- Autonomous surgical planning of mandibular angle reduction based on anatomical landmarks and osteotomy plane detection.Scientific reports · 2025Article
- Deep learning segmentation of mandible with lower dentition from cone beam CT.Oral radiology · 2025Article
- Deep ensemble learning-driven fully automated multi-structure segmentation for precision craniomaxillofacial surgery.Frontiers in bioengineering and biotechnology · 2025Article
- Multi-Scale 3D Cephalometric Landmark Detection Based on Direct Regression with 3D CNN Architectures.Diagnostics (Basel, Switzerland) · 2024Article
- TriConvUNeXt: A Pure CNN-Based Lightweight Symmetrical Network for Biomedical Image Segmentation.Journal of imaging informatics in medicine · 2024Article
- Craniomaxillofacial landmarks detection in CT scans with limited labeled data via semi-supervised learning.Heliyon · 2024Article
- Patient-specific reference model estimation for orthognathic surgical planning.International journal of computer assisted radiology and surgery · 2024Article
- Correspondence attention for facial appearance simulation.Medical image analysis · 2024Article
- Deep learning driven segmentation of maxillary impacted canine on cone beam computed tomography images.Scientific reports · 2024Article
- Semi or fully automatic tooth segmentation in CBCT images: a review.PeerJ. Computer science · 2024Article
- Deep learning for 3D cephalometric landmarking with heterogeneous multi-center CBCT dataset.PloS one · 2024Article
- Automatic craniomaxillofacial landmarks detection in CT images of individuals with dentomaxillofacial deformities by a two-stage deep learning model.BMC oral health · 2023Article
- Towards fully automated inner ear analysis with deep-learning-based joint segmentation and landmark detection framework.Scientific reports · 2023Article
- Joint Cranial Bone Labeling and Landmark Detection in Pediatric CT Images Using Context Encoding.IEEE transactions on medical imaging · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Accurate bone segmentation and landmark detection are two essential preparation tasks in computer-aided surgical planning for patients with craniomaxillofacial (CMF) deformities. Surgeons typically have to complete the two tasks manually, spending ~12 hours for each set of CBCT or ~5 hours for CT. To tackle these problems, we propose a multi-stage coarse-to-fine CNN-based framework, called SkullEngine, for high-resolution segmentation and large-scale landmark detection through a collaborative, integrated, and scalable JSD model and three segmentation and landmark detection refinement models. We evaluated our framework on a clinical dataset consisting of 170 CBCT/CT images for the task of segmenting 2 bones (midface and mandible) and detecting 175 clinically common landmarks on bones, teeth, and soft tissues. Experimental results show that SkullEngine significantly improves segmentation quality, especially in regions where the bone is thin. In addition, SkullEngine also efficiently and accurately detect all of the 175 landmarks. Both tasks were completed simultaneously within 3 minutes regardless of CBCT or CT with high segmentation quality. Currently, SkullEngine has been integrated into a clinical workflow to further evaluate its clinical efficiency.
Indexed as
Identifiers
What OpenQuestion holds
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.