Evidence map›Paper›PMID 39417954›Full record

ArticleNeuroinformatics2024

A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images.

Jiaxuan Wang, Rui Gong, Shahrokh Heidari, Mitchell Rogers, Toshiki Tani, Hiroshi Abe, Noritaka Ichinohe, Alexander Woodward, Patrice J Delmas

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neuroinformatics, 2024. 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

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

9 authors.

Jiaxuan WangIntelligent Vision Systems Lab, The University of Auckland, Auckland, New Zealand.
Rui GongTheoretical Biology Group, Department of Creative Research, Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji, Okazaki, Aichi, 444-8787, Japan.
Shahrokh HeidariIntelligent Vision Systems Lab, The University of Auckland, Auckland, New Zealand.
Mitchell RogersIntelligent Vision Systems Lab, The University of Auckland, Auckland, New Zealand.
Toshiki TaniLaboratory for Molecular Analysis of Higher Brain Function, RIKEN Center for Brain Science, Wako, Japan.
Hiroshi AbeLaboratory for Molecular Analysis of Higher Brain Function, RIKEN Center for Brain Science, Wako, Japan.
Noritaka IchinoheDepartment of Ultrastructural Research, National Center of Neurology and Psychiatry, Tokyo, Japan.
Alexander WoodwardIntelligent Vision Systems Lab, The University of Auckland, Auckland, New Zealand.
Patrice J DelmasIntelligent Vision Systems Lab, The University of Auckland, Auckland, New Zealand. p.delmas@auckland.ac.nz.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Characterizing the anatomical structure and connectivity between cortical regions is a critical step towards understanding the information processing properties of the brain and will help provide insight into the nature of neurological disorders. A key feature of the mammalian cerebral cortex is its laminar structure. Identifying these layers in neuroimaging data is important for understanding their global structure and to help understand the connectivity patterns of neurons in the brain. We studied Nissl-stained and myelin-stained slice images of the brain of the common marmoset (Callithrix jacchus), which is a new world monkey that is becoming increasingly popular in the neuroscience community as an object of study. We present a novel computational framework that first acquired the cortical labels using AI-based tools followed by a trained deep learning model to segment cerebral cortical layers. We obtained a Euclidean distance of

Indexed as

Cerebral CortexDeep LearningAnimalsCallithrixMicroscopyPlatyrrhiniStaining and LabelingArtificial intelligenceCell microscopyCerebral cortex laminar structureComputer visionDeep learningHistological dataImage segmentationNeuroanatomyNeuroscience

Identifiers

PMID39417954
PMCPMC11579130

What Socratic 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 Socratic graph.