ArticleTomography (Ann Arbor, Mich.)2016
Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning.
Article in Tomography (Ann Arbor, Mich.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled 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.
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
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- Prediction of Post-Infectious Inflammatory Response Syndrome in Patients with Cryptococcal Meningitis Based on Clinical and Radiomics Data.Diagnostics (Basel, Switzerland) · 2026Article
- Review on brain-computer interface technologies in healthcare.Biophysical reviews · 2023Review
- A holistic overview of deep learning approach in medical imaging.Multimedia systems · 2022Article
- Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto-Encoder Model.Diagnostics (Basel, Switzerland) · 2021Article
- Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.BMJ open · 2021Article
- Multi-Modal Segmentation of 3D Brain Scans Using Neural Networks.Frontiers in neurology · 2021Article
- Deep Learning Based Noise Reduction for Brain MR Imaging: Tests on Phantoms and Healthy Volunteers.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2020Article
- Fellow in a Box: Combining AI and Domain Knowledge with Bayesian Networks for Differential Diagnosis in Neuroimaging.Radiology · 2020Article
- Artificial intelligence for clinical decision support in neurology.Brain communications · 2020Review
- Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement.Neuro-oncology · 2019Article
- Artificial intelligence for precision education in radiology.The British journal of radiology · 2019Review
- Convolutional Neural Network for Automated FLAIR Lesion Segmentation on Clinical Brain MR Imaging.AJNR. American journal of neuroradiology · 2019Article
- Deep learning in medical imaging and radiation therapy.Medical physics · 2019Review
- Artificial intelligence using neural network architecture for radiology (AINNAR): classification of MR imaging sequences.Japanese journal of radiology · 2018Article
- Deep Learning in Neuroradiology.AJNR. American journal of neuroradiology · 2018Review
- Deep Learning in Radiology: Does One Size Fit All?Journal of the American College of Radiology : JACR · 2018Article
- Imaging in neuro-oncology.Therapeutic advances in neurological disorders · 2018Review
- Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys.Journal of digital imaging · 2017Article
- Deep-learning based, automated segmentation of macular edema in optical coherence tomography.Biomedical optics express · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
We present a deep convolutional neural network application based on autoencoders aimed at segmentation of increased signal regions in fluid-attenuated inversion recovery magnetic resonance imaging images. The convolutional autoencoders were trained on the publicly available Brain Tumor Image Segmentation Benchmark (BRATS) data set, and the accuracy was evaluated on a data set where 3 expert segmentations were available. The simultaneous truth and performance level estimation (STAPLE) algorithm was used to provide the ground truth for comparison, and Dice coefficient, Jaccard coefficient, true positive fraction, and false negative fraction were calculated. The proposed technique was within the interobserver variability with respect to Dice, Jaccard, and true positive fraction. The developed method can be used to produce automatic segmentations of tumor regions corresponding to signal-increased fluid-attenuated inversion recovery regions.
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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.