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A novel center-based deep contrastive metric learning method for the detection of polymicrogyria in pediatric brain
Lingfeng Zhang1, Nishard Abdeen2, Jochen Lang1
1School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, K1N 6N5, Canada.
Abstract:
Polymicrogyria (PMG) is a disorder of cortical organization mainly seen in children, which can be associated with seizures, developmental delay and motor weakness. PMG is typically diagnosed on magnetic resonance imaging (MRI) but some cases can be challenging to detect even for experienced radiologists. In this study, we create an open pediatric MRI dataset (PPMR) containing both PMG and control cases from the Children's Hospital of Eastern Ontario (CHEO), Ottawa, Canada. The differences between PMG and control MRIs are subtle and the true distribution of the features of the disease is unknown. This makes automatic detection of potential PMG cases in MRI difficult. To enable the automatic detection of potential PMG cases, we propose an anomaly detection method based on a novel center-based deep contrastive metric learning loss function (cDCM). Despite working with a small and imbalanced dataset our method achieves 88.07% recall at 71.86% precision. This will facilitate a computer-aided tool for radiologists to select potential PMG MRIs. To the best of our knowledge, our research is the first to apply machine learning techniques to identify PMG solely from MRI. Our code is available at: https://github.com/RichardChangCA/Deep-Contrastive-Metric-Learning-Method-to-Detect-Polymicrogyria-in-Pediatric-Brain-MRI. Our pediatric MRI dataset is available at: https://www.kaggle.com/datasets/lingfengzhang/pediatric-polymicrogyria-mri-dataset.
Insights
Polymicrogyria (PMG), a brain disorder in children, is difficult to detect on MRIs. This study introduces a new AI method for automatic PMG detection, improving diagnostic accuracy for radiologists.
Area of Science:
- Neuroscience
- Medical Imaging
- Artificial Intelligence
Background:
- Polymicrogyria (PMG) is a cortical malformation affecting children, leading to developmental delays and motor deficits.
- Diagnosis relies on MRI, but subtle cases pose challenges even for expert radiologists.
- Existing diagnostic methods lack automated tools for early and accurate PMG identification.
Purpose of the Study:
- To develop an automated method for detecting polymicrogyria (PMG) in pediatric brain MRIs.
- To create and share an open-access pediatric MRI dataset for PMG research.
- To establish a baseline for machine learning-based PMG detection using MRI.
Main Methods:
- Creation of the Pediatric Polymicrogyria MRI (PPMR) dataset from CHEO, Ottawa.
- Implementation of a novel anomaly detection approach using center-based deep contrastive metric learning (cDCM).
- Evaluation of the cDCM method on a small, imbalanced dataset.
Main Results:
- The cDCM method achieved 88.07% recall and 71.86% precision in detecting PMG.
- This research represents the first application of machine learning for PMG identification solely from MRI.
- The developed methods and dataset are publicly available to aid further research and clinical tools.
Conclusions:
- The proposed cDCM method shows promise for computer-aided detection of PMG in pediatric MRIs.
- The open dataset and code will facilitate advancements in diagnosing this challenging neurological disorder.
- This work paves the way for AI-assisted radiological interpretation in pediatric neurodevelopmental disorders.
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