Evidence map›Paper›PMID 40629331›Full record

ArticleBMC oral health2025

Comparison of 2D, 2.5D, and 3D segmentation networks for mandibular canals in CBCT images: a study on public and external datasets.

Su Yang, Jong Soo Jeong, Dahyun Song, Ji Yong Han, Sang-Heon Lim, Sujeong Kim, Ji-Yong Yoo, Jun-Min Kim, Jo-Eun Kim, Kyung-Hoe Huh and 3 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

13 authors.

Su Yang *Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826, South Korea.
Jong Soo Jeong *Department of Dentistry, School of Dentistry, Seoul National University, Seoul, 08826, South Korea.
Dahyun SongInterdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National University, Seoul, 08826, South Korea.
Ji Yong HanInterdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National University, Seoul, 08826, South Korea.
Sang-Heon LimInterdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National University, Seoul, 08826, South Korea.
Sujeong KimInterdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National University, Seoul, 08826, South Korea.
Ji-Yong YooDepartment of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826, South Korea.
Jun-Min KimDepartment of Electronics and Information Engineering, Hansung University, Seoul, 02876, South Korea.
Jo-Eun KimDepartment of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea.
Kyung-Hoe HuhDepartment of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea.
Sam-Sun LeeDepartment of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea.
Min-Suk HeoDepartment of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea.
Won-Jin YiDepartment of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826, South Korea. wjyi@snu.ac.kr.

Funding

National Research Foundation of Korea 2023R1A2C200532611the Ministry of Trade Industry & Energy (MOTIE, Korea) RS-2025-14322975
6 · The paper itself

Abstract

The purpose of this study was to compare the performances of 2D, 2.5D, and 3D CNN-based segmentation networks, along with a 3D vision transformer-based segmentation network, for segmenting mandibular canals (MCs) on the public and external CBCT datasets under the same GPU memory capacity. We also performed ablation studies for an image-cropping (IC) technique and segmentation loss functions. 3D-UNet showed the highest segmentation performance for the MC than those of 2D and 2.5D segmentation networks on public test datasets, achieving 0.569 ± 0.107, 0.719 ± 0.092, 0.664 ± 0.131, and 0.812 ± 0.095 in terms of JI, DSC, PR, and RC, respectively. On the external test dataset, 3D-UNet achieved 0.564 ± 0.092, 0.716 ± 0.081, 0.812 ± 0.087, and 0.652 ± 0.103 in terms of JI, DSC, PR, and RC, respectively. The IC technique and multi-planar Dice loss improved the boundary details and structural connectivity of the MC from the mental foramen to the mandibular foramen. The 3D-UNet demonstrated superior segmentation performance for the MC by learning 3D volumetric context information for the entire MC in the CBCT volume.

Indexed as

Cone-Beam Computed TomographyImage Processing, Computer-AssistedImaging, Three-DimensionalMandibleNeural Networks, ComputerHumans3D segmentation networkCBCT imageDeep learningImage segmentationMandibular canal

Identifiers

PMID40629331
PMCPMC12235980

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.