ArticleJournal of digital imaging2020
A Deep Convolutional Neural Network for Annotation of Magnetic Resonance Imaging Sequence Type.
Article in Journal of digital imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- A knowledge-guided dual-path framework for automated liver MRI series classification.Abdominal radiology (New York) · 2026Article
- Automated PET tracer classification in multi-site, multi-cohort studies using deep learning.EJNMMI research · 2026Article
- Automated identification of MRI series using a hierarchical modular machine-learning pipeline.European radiology experimental · 2026Article
- Standardizing Heterogeneous MRI Series Description Metadata Using Large Language Models.Journal of imaging informatics in medicine · 2026Article
- Optimizing MRI sequence classification performance: insights from domain shift analysis.European radiology · 2025Article
- Automated characterization of abdominal MRI exams using deep learning.Scientific reports · 2025Article
- Automated Classification of Body MRI Sequences Using Convolutional Neural Networks.Academic radiology · 2025Article
- Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About It.Diagnostics (Basel, Switzerland) · 2025Review
- MRISeqClassifier: A Deep Learning Toolkit for Precise MRI Sequence Classification.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Classification of Multi-Parametric Body MRI Series Using Deep Learning.IEEE journal of biomedical and health informatics · 2024Article
- Utilizing convolutional neural networks for discriminating cancer and stromal cells in three-dimensional cell culture images with nuclei counterstain.Journal of biomedical optics · 2024Article
- Article
- Article
- Brain MRI sequence and view plane identification using deep learning.Frontiers in neuroinformatics · 2024Article
- Deep Learning-based Identification of Brain MRI Sequences Using a Model Trained on Large Multicentric Study Cohorts.Radiology. Artificial intelligence · 2024Article
- A Lightweight Deep Learning Framework for Automatic MRI Data Sorting and Artifacts Detection.Journal of medical systems · 2023Article
- Improving the Automatic Classification of Brain MRI Acquisition Contrast with Machine Learning.Journal of digital imaging · 2023Article
- Big Data Analysis and Prediction System Based on Improved Convolutional Neural Network.Computational intelligence and neuroscience · 2022Article
- Design and Implementation of Interactive Platform for Operation and Maintenance of Multimedia Information System Based on Artificial Intelligence and Big Data.Computational intelligence and neuroscience · 2022Article
- Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients.Frontiers in neuroimaging · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
The explosion of medical imaging data along with the advent of big data analytics has launched an exciting era for clinical research. One factor affecting the ability to aggregate large medical image collections for research is the lack of infrastructure for automated data annotation. Among all imaging modalities, annotation of magnetic resonance (MR) images is particularly challenging due to the non-standard labeling of MR image types. In this work, we aimed to train a deep neural network to annotate MR image sequence type for scans of brain tumor patients. We focused on the four most common MR sequence types within neuroimaging: T1-weighted (T1W), T1-weighted post-gadolinium contrast (T1Gd), T2-weighted (T2W), and T2-weighted fluid-attenuated inversion recovery (FLAIR). Our repository contains images acquired using a variety of pulse sequences, sequence parameters, field strengths, and scanner manufacturers. Image selection was agnostic to patient demographics, diagnosis, and the presence of tumor in the imaging field of view. We used a total of 14,400 two-dimensional images, each visualizing a different part of the brain. Data was split into train, validation, and test sets (9600, 2400, and 2400 images, respectively) and sets consisted of equal-sized groups of image types. Overall, the model reached an accuracy of 99% on the test set. Our results showed excellent performance of deep learning techniques in predicting sequence types for brain tumor MR images. We conclude deep learning models can serve as tools to support clinical research and facilitate efficient database management.
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.