Artificially-generated consolidations and balanced augmentation increase performance of U-net for lung parenchyma segmentation on MR images.

ABSTRACT:

Purpose

To improve automated lung segmentation on 2D lung MR images using balanced augmentation and artificially-generated consolidations for training of a convolutional neural network (CNN).

Materials and methods

From 233 healthy volunteers and 100 patients, 1891 coronal MR images were acquired. Of these, 1666 images without consolidations were used to build a binary semantic CNN for lung segmentation and 225 images (187 without consolidations, 38 with consolidations) were used for testing. To increase CNN performance of segmenting lung parenchyma with consolidations, balanced augmentation was performed and artificially-generated consolidations were added to all training images. The proposed CNN (CNNBal/Cons) was compared to two other CNNs: CNNUnbal/NoCons-without balanced

SUBMITTER: Crisosto C 

PROVIDER: S-EPMC10168553 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature