ArticleNeuroinformatics2019
Fast and Precise Hippocampus Segmentation Through Deep Convolutional Neural Network Ensembles and Transfer Learning.
Article in Neuroinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- InnerEye-HS: a disease-agnostic clinical tool for hippocampal segmentation.Brain communications · 2026Article
- Deep learning for Alzheimer's disease: advances in classification, segmentation, subtyping, and explainability.Biomedical engineering online · 2025Review
- NeuroAgeFusionNet an ensemble deep learning framework integrating CNN, transformers, and GNN for robust brain age estimation using MRI scans.Scientific reports · 2025Article
- Mesoscale connectivity of the human hippocampus and fimbria revealed by ex vivo diffusion MRI.NeuroImage · 2025Article
- Automated segmentation of midbrain nuclei using deep learning and multisequence MRI: A longitudinal study on iron accumulation with age.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- DMCA-GAN: Dual Multilevel Constrained Attention GAN for MRI-Based Hippocampus Segmentation.Journal of digital imaging · 2023Article
- Fine scale hippocampus morphology variation cross 552 healthy subjects from age 20 to 80.Frontiers in neuroscience · 2023Article
- Hippocampus Segmentation Using U-Net Convolutional Network from Brain Magnetic Resonance Imaging (MRI).Journal of digital imaging · 2022Article
- Deep learning for necrosis detection using canine perivascular wall tumour whole slide images.Scientific reports · 2022Article
- Operationalising fairness in medical AI adoption: detection of early Alzheimer's disease with 2D CNN.BMJ health & care informatics · 2022Article
- Identifying epilepsy based on machine-learning technique with diffusion kurtosis tensor.CNS neuroscience & therapeutics · 2022Article
- A novel deep learning based hippocampus subfield segmentation method.Scientific reports · 2022Article
- Automated Segmentation of Midbrain Structures in High-Resolution Susceptibility Maps Based on Convolutional Neural Network and Transfer Learning.Frontiers in neuroscience · 2022Article
- A domain adaptation benchmark for T1-weighted brain magnetic resonance image segmentation.Frontiers in neuroinformatics · 2022Article
- Charting the human amygdala development across childhood and adolescence: Manual and automatic segmentation.Developmental cognitive neuroscience · 2021Article
- Advanced Kidney Volume Measurement Method Using Ultrasonography with Artificial Intelligence-Based Hybrid Learning in Children.Sensors (Basel, Switzerland) · 2021Article
- Combined Atlas and Convolutional Neural Network-Based Segmentation of the Hippocampus from MRI According to the ADNI Harmonized Protocol.Sensors (Basel, Switzerland) · 2021Article
- Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review.Journal of imaging · 2021Review
- Hippocampus segmentation on epilepsy and Alzheimer's disease studies with multiple convolutional neural networks.Heliyon · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automatic segmentation of the hippocampus from 3D magnetic resonance imaging mostly relied on multi-atlas registration methods. In this work, we exploit recent advances in deep learning to design and implement a fully automatic segmentation method, offering both superior accuracy and fast result. The proposed method is based on deep Convolutional Neural Networks (CNNs) and incorporates distinct segmentation and error correction steps. Segmentation masks are produced by an ensemble of three independent models, operating with orthogonal slices of the input volume, while erroneous labels are subsequently corrected by a combination of Replace and Refine networks. We explore different training approaches and demonstrate how, in CNN-based segmentation, multiple datasets can be effectively combined through transfer learning techniques, allowing for improved segmentation quality. The proposed method was evaluated using two different public datasets and compared favorably to existing methodologies. In the EADC-ADNI HarP dataset, the correspondence between the method's output and the available ground truth manual tracings yielded a mean Dice value of 0.9015, while the required segmentation time for an entire MRI volume was 14.8 seconds. In the MICCAI dataset, the mean Dice value increased to 0.8835 through transfer learning from the larger EADC-ADNI HarP dataset.
Indexed as
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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.