ArticleFrontiers in neuroinformatics2019
Dilated Dense U-Net for Infant Hippocampus Subfield Segmentation.
Article in Frontiers in neuroinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Deep learning for multi-modal medical image segmentation: a survey and comparative study.Brain imaging and behavior · 2025Article
- Convolutional Neural Networks for the segmentation of hippocampal structures in postmortem MRI scans.Journal of neuroscience methods · 2025Article
- DSnet: a new dual-branch network for hippocampus subfield segmentation.Scientific reports · 2024Article
- Attention-gated 3D CapsNet for robust hippocampal segmentation.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Article
- Volumetric Analysis of Amygdala and Hippocampal Subfields for Infants with Autism.Journal of autism and developmental disorders · 2023Article
- Densely Convolutional Spatial Attention Network for nuclei segmentation of histological images for computational pathology.Frontiers in oncology · 2023Article
- A fast and robust hippocampal subfields segmentation: HSF revealing lifespan volumetric dynamics.Frontiers in neuroinformatics · 2023Article
- Bayesian deep learning outperforms clinical trial estimators of intracerebral and intraventricular hemorrhage volume.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2022Article
- A novel deep learning based hippocampus subfield segmentation method.Scientific reports · 2022Article
- U-Net combined with multi-scale attention mechanism for liver segmentation in CT images.BMC medical informatics and decision making · 2021Article
- Automated thalamic nuclei segmentation using multi-planar cascaded convolutional neural networks.Magnetic resonance imaging · 2020Article
- CAST: A multi-scale convolutional neural network based automated hippocampal subfield segmentation toolbox.NeuroImage · 2020Article
- Deep learning in fracture detection: a narrative review.Acta orthopaedica · 2020Review
- Nuclear Segmentation in Histopathological Images Using Two-Stage Stacked U-Nets With Attention Mechanism.Frontiers in bioengineering and biotechnology · 2020Article
- Multi-atlas label fusion with random local binary pattern features: Application to hippocampus segmentation.Scientific reports · 2019Article
- A Novel Approach for Manual Segmentation of the Amygdala and Hippocampus in Neonate MRI.Frontiers in neuroscience · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Accurate and automatic segmentation of infant hippocampal subfields from magnetic resonance (MR) images is an important step for studying memory related infant neurological diseases. However, existing hippocampal subfield segmentation methods were generally designed based on adult subjects, and would compromise performance when applied to infant subjects due to insufficient tissue contrast and fast changing structural patterns of early hippocampal development. In this paper, we propose a new fully convolutional network (FCN) for infant hippocampal subfield segmentation by embedding the dilated dense network in the U-net, namely DUnet. The embedded dilated dense network can generate multi-scale features while keeping high spatial resolution, which is useful in fusing the low-level features in the contracting path with the high-level features in the expanding path. To further improve the performance, we group every pair of convolutional layers with one residual connection in the DUnet, and obtain the Residual DUnet (ResDUnet). Experimental results show that our proposed DUnet and ResDUnet improve the average Dice coefficient by 2.1 and 2.5% for infant hippocampal subfield segmentation, respectively, when compared with the classic 3D U-net. The results also demonstrate that our methods outperform other state-of-the-art methods.
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.