Evidence map›Paper›PMID 32474081›Full record

ArticleNeuroImage2020

CAST: A multi-scale convolutional neural network based automated hippocampal subfield segmentation toolbox.

Zhengshi Yang, Xiaowei Zhuang, Virendra Mishra, Karthik Sreenivasan, Dietmar Cordes

Abstract read
In one paragraph

Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
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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

5 authors.

Zhengshi YangCleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, 89106, USA.
Xiaowei ZhuangCleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, 89106, USA.
Virendra MishraCleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, 89106, USA.
Karthik SreenivasanCleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, 89106, USA.
Dietmar CordesCleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, 89106, USA; Department of Psychology and Neuroscience, University of Colorado, Boulder, CO, 80309, USA. Electronic address: cordesd@ccf.org.

Funding

Renewal of Centers of Biomedical Research Excellence (COBRE) (Phase 2) CNTN - ResubmissionP20GM109025 · NIGMS · CLEVELAND CLINIC FOUNDATION · PI Robert J. Fox, Jefferson Kinney · 2015 to 2026
$22.8M
NVEDARC Clinical CoreP20AG068053 · NIA · CLEVELAND CLINIC FOUNDATION · PI WINT, DYLAN · 2020 to 2022
$3.3M
Improving the Detection of Activation in High Resolution fMRI using MultivariateR01EB014284 · NIBIB · TORONTO METROPOLITAN UNIVERSITY · PI CORDES, DIETMAR · 2013 to 2015
$750k
NIA NIH HHS P20 AG068053NIBIB NIH HHS R01 EB014284NIGMS NIH HHS P20 GM109025
6 · The paper itself

Abstract

In this study, we developed a multi-scale Convolutional neural network based Automated hippocampal subfield Segmentation Toolbox (CAST) for automated segmentation of hippocampal subfields. Although training CAST required approximately three days on a single workstation with a high-quality GPU card, CAST can segment a new subject in less than 1 ​min even with GPU acceleration disabled, thus this method is more time efficient than current automated methods and manual segmentation. This toolbox is highly flexible with either a single modality or multiple modalities and can be easily set up to be trained with a researcher's unique data. A 3D multi-scale deep convolutional neural network is the key algorithm used in the toolbox. The main merit of multi-scale images is the capability to capture more global structural information from down-sampled images without dramatically increasing memory and computational burden. The original images capture more local information to refine the boundary between subfields. Residual learning is applied to alleviate the vanishing gradient problem and improve the performance with a deeper network. We applied CAST with the same settings on two datasets, one 7T dataset (the UMC dataset) with only the T2 image and one 3T dataset (the MNI dataset) with both T1 and T2 images available. The segmentation accuracy of both CAST and the state-of-the-art automated method ASHS, in terms of the dice similarity coefficient (DSC), were comparable. CAST significantly improved the reliability of segmenting small subfields, such as CA2, CA3, and the entorhinal cortex (ERC), in terms of the intraclass correlation coefficient (ICC). Both ASHS and manual segmentation process some subfields (e.g. CA2 and ERC) with high DSC values but low ICC values, consequently increasing the difficulty of judging segmentation quality. CAST produces very consistent DSC and ICC values, with a maximal discrepancy of 0.01 (DSC-ICC) across all subfields. The pre-trained model, source code, and settings for the CAST toolbox are publicly available.

Indexed as

Neural Networks, ComputerAdultAlgorithmsAutomationDatabases, FactualDeep LearningFemaleHippocampusHumansImage Processing, Computer-AssistedMachine LearningMagnetic Resonance ImagingMaleMiddle AgedReproducibility of ResultsYoung AdultAutomated segmentationConvolutional neural networkHippocampal subfieldsResidual learning

Identifiers

PMID32474081
PMCPMC7442241

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Registered trials

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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.