Convolutional Neural Networks with Template-Based Data Augmentation for Functional Lung Image Quantification

Academic Radiology
Nicholas J TustisonKun Qing

Abstract

We propose an automated segmentation pipeline based on deep learning for proton lung MRI segmentation and ventilation-based quantification which improves on our previously reported methodologies in terms of computational efficiency while demonstrating accuracy and robustness. The large data requirement for the proposed framework is made possible by a novel template-based data augmentation strategy. Supporting this work is the open-source ANTsRNet-a growing repository of well-known deep learning architectures first introduced here. Deep convolutional neural network (CNN) models were constructed and trained using a custom multilabel Dice metric loss function and a novel template-based data augmentation strategy. Training (including template generation and data augmentation) employed 205 proton MR images and 73 functional lung MRI. Evaluation was performed using data sets of size 63 and 40 images, respectively. Accuracy for CNN-based proton lung MRI segmentation (in terms of Dice overlap) was left lung: 0.93 ± 0.03, right lung: 0.94 ± 0.02, and whole lung: 0.94 ± 0.02. Although slightly less accurate than our previously reported joint label fusion approach (left lung: 0.95 ± 0.02, right lung: 0.96 ± 0.01, and whole lung: 0.96 ± 0....Continue Reading

Citations

Sep 7, 2020·Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine·Corin WillersPhilipp Latzin
Feb 27, 2021·Progress in Nuclear Magnetic Resonance Spectroscopy·Helen MarshallJim M Wild
Apr 21, 2021·The British Journal of Radiology·Joshua R AstleyBilal A Tahir
Apr 29, 2021·Scientific Reports·Nicholas J TustisonBrian B Avants
Sep 25, 2019·Radiology. Artificial Intelligence·Bradley J Erickson

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