Evidence map›Paper›PMID 40357297›Full record

ArticleFrontiers in medicine2025

Automatic segmentation and volume measurement of anterior visual pathway in brain 3D-T1WI using deep learning.

Yongliang Han, Haixiang Wang, Qi Luo, Jingjie Wang, Chun Zeng, Qiao Zheng, Linquan Dai, Yiqiu Wei, Qiyuan Zhu, Wenlong Lin and 2 more

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In one paragraph

Article in Frontiers in medicine, 2025. 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

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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. 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

12 authors.

Yongliang Han *Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Haixiang Wang *College of Physics and Electronic Engineering, Chongqing Normal University, Chongqing, China.
Qi LuoDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Jingjie WangDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Chun ZengDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Qiao ZhengDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Linquan DaiDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Yiqiu WeiDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Qiyuan ZhuDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Wenlong LinCollege of Computer and Information Science, Chongqing Normal University, Chongqing, China.
Shaoguo CuiCollege of Computer and Information Science, Chongqing Normal University, Chongqing, China.
Yongmei LiDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Accurate anterior visual pathway (AVP) segmentation is vital for clinical applications, but manual delineation is time-consuming and resource-intensive. We aim to explore the feasibility of automatic AVP segmentation and volume measurement in brain T1-weighted imaging (T1WI) using the 3D UX-Net deep-learning model. Methods: Clinical data and brain 3D T1WI from 119 adults were retrospectively collected. Two radiologists annotated the AVP course in each participant's images. The dataset was randomly divided into training ( Results: The 3D UX-Net achieved the highest DSC (0.893 ± 0.017), followed by Swin SMT (0.888 ± 0.018), 3D U-Net (0.875 ± 0.019), Swin UNETR (0.870 ± 0.017), and UNETR++ (0.861 ± 0.020). For surface distance metrics, 3D UX-Net demonstrated the lowest median ASSD (0.234 mm [0.188-0.273]). The VD of Swin SMT was significantly lower than that of 3D U-Net ( Conclusion: We provide normative values for the automatic MRI measurement of the AVP in adults. The 3D UX-Net model based on brain T1WI achieves high accuracy in segmenting and measuring the volume of the AVP.

Indexed as

convolutional neural networkdeep learningmagnetic resonance imagingmedical image processingoptic nerve

Identifiers

PMID40357297
PMCPMC12066431

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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.