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

ABSTRACT:

Rationale and objectives

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.

Materials and methods

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 (includin

SUBMITTER: Tustison NJ 

PROVIDER: S-EPMC6397788 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature