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Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation: A Multi-center, Multi-vendor, and
Joshua R Astley1,2, Alberto M Biancardi1, Paul J C Hughes1
1POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK.
Journal of Magnetic Resonance Imaging : JMRI
|February 17, 2023
Summary
A novel 3D convolutional neural network (CNN) achieves accurate proton MRI lung segmentation. This deep learning model demonstrates robustness across diverse pathologies, acquisition parameters, and imaging centers.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Radiology
Background:
- Deep learning, specifically convolutional neural networks (CNNs), has become the standard for proton (¹H)-MRI lung segmentation.
- Previous CNN studies were limited by single-center data and restricted acquisition parameters.
Purpose of the Study:
- To develop a generalizable CNN for ¹H-MRI lung segmentation.
- The model aims for robustness against various pathologies, acquisition protocols, vendors, and imaging centers.
Main Methods:
- A retrospective study utilized 809 ¹H-MRI scans from 258 participants with pulmonary pathologies and 31 healthy participants.
- 2D and 3D CNNs were trained and compared against the spatial fuzzy c-means (SFCM) method and manual segmentations.
- Performance was assessed using Dice similarity coefficient (DSC), average boundary Hausdorff distance (Average HD), and relative error (XOR) on testing and external validation datasets.
Main Results:
- The 3D CNN significantly outperformed the 2D CNN and SFCM.
- On the testing set, the 3D CNN achieved a median DSC of 0.961, Average HD of 1.63 mm, and XOR of 0.079.
- On external validation data, the 3D CNN yielded a median DSC of 0.973, Average HD of 1.11 mm, and XOR of 0.054.
Conclusions:
- The developed 3D CNN accurately segments lungs in ¹H-MRI.
- The model demonstrates significant robustness to variations in disease pathology, MRI sequence, vendor, and imaging center.
