Evidence map›Paper›PMID 41605959›Full record

ArticleScientific data2026

PMCanalSeg: A dataset for automatic segmentation of the pterygopalatine and mandibular canals from 3D CBCT images.

Guohui Li, Yinan Lu, Guomin Wu, Lin Wang, Rui Ma

Abstract readDataset
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Guohui LiCollege of Computer Science and Technology, Jilin University, Changchun, 130012, China.
Yinan LuCollege of Computer Science and Technology, Jilin University, Changchun, 130012, China.
Guomin WuDepartment of Oral, Plastic and Aesthetic Surgery, Hospital of Stomatology, Jilin University, Changchun, 130021, Jilin Province, China.
Lin WangDepartment of Oral, Plastic and Aesthetic Surgery, Hospital of Stomatology, Jilin University, Changchun, 130021, Jilin Province, China. linwang0461@jlu.edu.cn.ORCID http://orcid.org/0009-0009-6524-2338
Rui MaSchool of Artificial Intelligence, Jilin University, Changchun, 130012, Jilin Province, China. ruim@jlu.edu.cn.ORCID http://orcid.org/0000-0002-3477-1466

Funding

National Natural Science Foundation of China (National Science Foundation of China) No. 62202199
6 · The paper itself

Abstract

In orthognathic surgery, accurate segmentation of the pterygopalatine and mandibular canals in maxillofacial cone beam computed tomography (CBCT) scans is crucial. It provides critical information to prevent nerve damage during surgery and significantly reduces the risk of surgical complications. However, the high cost of data collection, strict patient privacy protection, and ethical constraints have hampered the performance of existing deep learning methods for pterygopalatine and mandibular canals segmentation, limiting their practical applicability in clinical settings. To address this challenge and advance the development of pterygopalatine and mandibular canal segmentation techniques in maxillofacial CBCT scans, we carefully constructed and made publicly available a large dataset for pterygopalatine and mandibular canal segmentation in maxillofacial CBCT scans. This dataset includes 191 patient cases and comprehensively covers the key anatomical structures of the maxillary pterygopalatine canal and the mandibular canal, both of which are crucial in orthognathic surgery. Notably, this dataset is the first to include data on the maxillary pterygopalatine canal, filling a significant gap in this field. The release of this dataset will greatly accelerate the development of deep learning-based segmentation methods, provide clinicians with more accurate reconstruction tools, and ultimately improve the safety and efficiency of surgical procedures.

Indexed as

Cone-Beam Computed TomographyImaging, Three-DimensionalMandibular CanalPterygopalatine FossaHumans

Identifiers

PMID41605959
PMCPMC12957367

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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.