ArticlePloS one2020
Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning techniques for isointense infant brain tissue segmentation: a systematic literature review.Frontiers in medicine · 2023Pooled it
- Artificial Intelligence Algorithm-Based Lumbar and Spinal MRI for Evaluation of Efficacy of Chinkuei Shin Chewan Decoction on Lumbar Spinal Stenosis.Contrast media & molecular imaging · 2021Trial
- Machine learning fusion for glioma tumor detection.Scientific reports · 2025Article
- A review of applications of automated ventricular parcellation from magnetic resonance imaging of the brain.Frontiers in neurology · 2025Review
- Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches.Bioengineering (Basel, Switzerland) · 2024Review
- A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images.Neuroinformatics · 2024Article
- Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN.Physical and engineering sciences in medicine · 2024Article
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- Improved robustness for deep learning-based segmentation of multi-center myocardial perfusion cardiovascular MRI datasets using data-adaptive uncertainty-guided space-time analysis.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2024Article
- A novel Gateaux derivatives with efficient DCNN-Resunet method for segmenting multi-class brain tumor.Medical & biological engineering & computing · 2023Article
- High frame rate deformation analysis of knee cartilage by spiral dualMRI and relaxation mapping.Magnetic resonance in medicine · 2023Article
- Artificial Intelligence-Assisted Evaluation of the Spatial Relationship between Brain Arteriovenous Malformations and the Corticospinal Tract to Predict Postsurgical Motor Defects.AJNR. American journal of neuroradiology · 2023Article
- Machine Learning-Enabled Determination of Diffuseness of Brain Arteriovenous Malformations from Magnetic Resonance Angiography.Translational stroke research · 2022Article
- SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans.Sensors (Basel, Switzerland) · 2022Article
- Automatic segmentation and volumetric assessment of internal organs and fatty tissue: what are the benefits?Magma (New York, N.Y.) · 2022Article
- Effect of Different Nursing Interventions on Discharged Patients with Cardiac Valve Replacement Evaluated by Deep Learning Algorithm-Based MRI Information.Contrast media & molecular imaging · 2022Article
- Correction: Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture.PloS one · 2022Article
- BrainSec: Automated Brain Tissue Segmentation Pipeline for Scalable Neuropathological Analysis.IEEE access : practical innovations, open solutions · 2022Article
- Improving across-dataset brain tissue segmentation for MRI imaging using transformer.Frontiers in neuroimaging · 2022Article
Corrections and comments
- Erratum issued
- Erratum issued
Authors and funding
3 authors.
Funding
Abstract
Accurate segmentation of brain magnetic resonance imaging (MRI) is an essential step in quantifying the changes in brain structure. Deep learning in recent years has been extensively used for brain image segmentation with highly promising performance. In particular, the U-net architecture has been widely used for segmentation in various biomedical related fields. In this paper, we propose a patch-wise U-net architecture for the automatic segmentation of brain structures in structural MRI. In the proposed brain segmentation method, the non-overlapping patch-wise U-net is used to overcome the drawbacks of conventional U-net with more retention of local information. In our proposed method, the slices from an MRI scan are divided into non-overlapping patches that are fed into the U-net model along with their corresponding patches of ground truth so as to train the network. The experimental results show that the proposed patch-wise U-net model achieves a Dice similarity coefficient (DSC) score of 0.93 in average and outperforms the conventional U-net and the SegNet-based methods by 3% and 10%, respectively, for on Open Access Series of Imaging Studies (OASIS) and Internet Brain Segmentation Repository (IBSR) dataset.
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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.