Evidence map›Paper›PMID 42564896›Full record

ArticleFrontiers in neuroinformatics2026

Automatic coarse-to-fine AC-PC localization on CT using registration-guided 3D-UNets.

Sharada Kadaba Sridhar, Alyssa Eastman, Peter Wilson, Shubhendu Mishra, Charles Broadbent, Chip Truwit, Rui Kuang, Uzma Samadani

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In one paragraph

Article in Frontiers in neuroinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Sharada Kadaba SridharDepartment of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, United States.
Alyssa EastmanNeurotrauma Research Lab, Center for Veterans Research and Education, Minneapolis, MN, United States.
Peter WilsonDepartment of Radiation Oncology, University of Minnesota, Minneapolis, MN, United States.
Shubhendu MishraDepartment of Radiation Oncology, University of Minnesota, Minneapolis, MN, United States.
Charles BroadbentNeurotrauma Research Lab, Center for Veterans Research and Education, Minneapolis, MN, United States.
Chip TruwitDepartment of Radiology, University of Minnesota, Minneapolis, MN, United States.
Rui KuangDepartment of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, United States.
Uzma SamadaniDepartment of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Automatically localizing the Anterior Commissure (AC) and Posterior Commissure (PC) is foundational for CT-based algorithmic disease screening, yet robust computational methods for this on CT remain lacking. We developed a registration-guided 3D-UNet framework for CT-based AC-PC localization, demonstrating its utility in computing ventriculomegaly features for Normal Pressure Hydrocephalus (NPH) detection. Methods: Framework development and evaluation were on an internal cohort of scans from patients with NPH, Alzheimer's disease, post-traumatic volume loss, and headache (Veterans Affairs [VA]-Cohort, Results: On 1-mm Conclusion: The proposed registration-guided 3D-UNet framework accurately and automatically localizes the AC-PC on CT despite varied structural degeneration, enabling standardized radiological feature computation. This approach can augment neurodegenerative disease screening on CT, the primary modality for elderly patients evaluated for falls and altered mentation.

Indexed as

anterior commissurecomputed tomographydeep learningimage registrationnormal pressure hydrocephalusposterior commissure

Identifiers

PMID42564896
PMCPMC13445414

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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.