ArticleRadiology. Artificial intelligence2022
Interinstitutional Portability of a Deep Learning Brain MRI Lesion Segmentation Algorithm.
Article in Radiology. Artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
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- Multi-reader evaluation of deep learning-based auto-segmentation of eloquent brain arteriovenous malformation on MRA and white matter tractography in stereotactic radiosurgery.Radiation oncology (London, England) · 2026Article
- External Validation of a Winning Artificial Intelligence Algorithm from the RSNA 2022 Cervical Spine Fracture Detection Challenge.AJNR. American journal of neuroradiology · 2025Article
- Accuracy of an nnUNet Neural Network for the Automatic Segmentation of Intracranial Aneurysms, Their Parent Vessels, and Major Cerebral Arteries from MRI-TOF.AJNR. American journal of neuroradiology · 2025Article
- Article
- 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
- Segmentation of Brain Metastases Using Background Layer Statistics (BLAST).AJNR. American journal of neuroradiology · 2023Article
- Paving the Way for Predicting the Progression of Cognitive Decline: The Potential Role of Machine Learning Algorithms in the Clinical Management of Neurodegenerative Disorders.Journal of personalized medicine · 2023Article
- Fast, light, and scalable: harnessing data-mined line annotations for automated tumor segmentation on brain MRI.European radiology · 2023Article
- 3D Capsule Networks for Brain Image Segmentation.AJNR. American journal of neuroradiology · 2023Article
- Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation.Korean journal of radiology · 2023Article
- Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation.Bioengineering (Basel, Switzerland) · 2023Article
- 3D U-Net for automated detection of multiple sclerosis lesions: utility of transfer learning from other pathologies.Frontiers in neuroscience · 2023Article
- Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis.Frontiers in radiology · 2023Review
- Development of Gestational Age-Based Fetal Brain and Intracranial Volume Reference Norms Using Deep Learning.AJNR. American journal of neuroradiology · 2023Article
- Mitigating Bias in Radiology Machine Learning: 2. Model Development.Radiology. Artificial intelligence · 2022Article
- Accurate auto-labeling of chest X-ray images based on quantitative similarity to an explainable AI model.Nature communications · 2022Article
- An Integrated Analysis of Clinical, Genomic, and Imaging Features Reveals Predictors of Neurocognitive Outcomes in a Longitudinal Cohort of Pediatric Cancer Survivors, Enriched with CNS Tumors (Rad ART Pro).Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
purposeTo assess how well a brain MRI lesion segmentation algorithm trained at one institution performed at another institution, and to assess the effect of multi-institutional training datasets for mitigating performance loss. MATERIALS AND
methodsIn this retrospective study, a three-dimensional U-Net for brain MRI abnormality segmentation was trained on data from 293 patients from one institution (IN1) (median age, 54 years; 165 women; patients treated between 2008 and 2018) and tested on data from 51 patients from a second institution (IN2) (median age, 46 years; 27 women; patients treated between 2003 and 2019). The model was then trained on additional data from various sources:
resultsThe U-Net accurately segmented brain MRI lesions across various pathologic conditions. Performance was lower when tested at an external institution (median Dice score, 0.70 [IN2] vs 0.76 [IN1]). Addition of 483 training cases of a single pathologic condition, including from IN2, did not raise performance (median Dice score, 0.72;
conclusionFor brain MRI lesion segmentation, adding a modest amount of relevant training data from an external institution to a previously trained model supported successful application of the model to this external institution.
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