ArticleNeuroImage2020
CAST: A multi-scale convolutional neural network based automated hippocampal subfield segmentation toolbox.
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.
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
10 citing papers in PubMed.
- Convolutional Neural Networks for the segmentation of hippocampal structures in postmortem MRI scans.Journal of neuroscience methods · 2025Article
- A paired dataset of multi-modal MRI at 3 Tesla and 7 Tesla with manual hippocampal subfield segmentations.Scientific data · 2025Article
- A deep neural network for adaptive spatial smoothing of task fMRI data.Frontiers in neuroimaging · 2025Article
- DSnet: a new dual-branch network for hippocampus subfield segmentation.Scientific reports · 2024Article
- DMCA-GAN: Dual Multilevel Constrained Attention GAN for MRI-Based Hippocampus Segmentation.Journal of digital imaging · 2023Article
- A fast and robust hippocampal subfields segmentation: HSF revealing lifespan volumetric dynamics.Frontiers in neuroinformatics · 2023Article
- High-resolution atlasing and segmentation of the subcortex: Review and perspective on challenges and opportunities created by machine learning.NeuroImage · 2022Review
- A Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis Derived From Deep Learning-Based Segmentation of T1w-MRI.Frontiers in neurology · 2022Article
- Analysis of hippocampal subfields in sickle cell disease using ultrahigh field MRI.NeuroImage. Clinical · 2021Article
- A Review of Publicly Available Automatic Brain Segmentation Methodologies, Machine Learning Models, Recent Advancements, and Their Comparison.Annals of neurosciences · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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
Identifiers
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.