ReviewJournal of anatomy2025
A road map to manual segmentation of cerebral structures.
Review in Journal of anatomy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Artificial intelligence-based quantitative computed tomography assessment of shoulder muscle and its association with outcomes after reverse total shoulder arthroplasty.JSES international · 2026Article
- Substantial evidence against sex differences in bed nucleus of the stria terminalis volume in the human brain.bioRxiv : the preprint server for biology · 2026Article
- Novel multi-task learning for Alzheimer's stage classification using hippocampal MRI segmentation, feature fusion, and nomogram modeling.European journal of medical research · 2025Article
- A road map to manual segmentation of cerebral structures.Journal of anatomy · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Manual segmentation is an essential tool in the researcher's technical arsenal. It is a frequent practice necessary for image analysis in many protocols, especially in neuroimaging and comparative brain anatomy. In the framework of emergence of studies focusing on alternative animal models, manual segmentation procedures play a critical role. Nevertheless, this critical task is often assigned to students, a process that, unfortunately, tends to be time-consuming and repetitive. Well-conducted and well-described segmentation procedures can potentially guide novice and even expert operators and enhance research works' internal and external validity, making it possible to harmonize studies and facilitate data sharing. Furthermore, recent advances in neuroimaging, such as ex vivo imaging or ultra-high-field MRI, enable new acquisition modalities and the identification of minute structures that are barely visible with typical approaches. In this context of increasingly detailed and multimodal brain studies, reflecting on methodology is relevant and necessary. Because it is crucial to implement good practices in manual segmentation per se but also in the description of the segmentation procedures in research papers, we propose a general roadmap for optimizing the technique, its process and the reporting of manual segmentation. For each of them, the relevant elements of the literature have been collected and cited. The article is accompanied by a checklist that the reader can use to verify that the critical steps are being followed.
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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.