Data Augmentation Using Generative Adversarial Networks for Multi-Class Segmentation of Lung Confocal IF Images
Daiki Katsuma*, Hiroharu Kawanaka*, V. B. Surya Prasath**,***, and Bruce J. Aronow**,***
*Graduate School of Engineering, Mie University
1577 Kurima-machiya, Tsu, Mie 514-8507, Japan
**Division of Biomedical Informatics, Cincinnati Childrens Hospital Medical Center
3333 Burnet Aveue, Cincinnati, OH 45229, USA
***Department of Pediatrics, University of Cincinnati College of Medicine
Cincinnati, OH 45257, USA
The human lung is a complex organ with high cellular heterogeneity, and its development and maintenance require interactive gene networks and dynamic cross-talk among multiple cell types. We focus on the confocal immunofluorescent (IF) images of lung tissues from the LungMAP database to reveal lung development. Using the current state-of-the-art deep learning-based model, the authors consider obtaining accurate multi-class segmentation of lung confocal IF images. One of the primary bottlenecks in using deep Convolutional Neural Network (CNN) models is the lack of availability of large-scale training or ground-truth segmentation labels. Then, we implement the multi-class segmentation with Generative Adversarial Network (GAN) models to expand the training dataset, improve overall segmentation accuracy, and discuss the effectiveness of created synthetic images in the segmentation of IF images. Consequently, experimental results indicated that 15.1% increased the accuracy of six-class segmentation using Mask R-CNN. In particular, the accuracy of our few data was mainly improved by using our proposed method. Therefore, the synthetic dataset can moderate the imbalanced data and be used for expanding the dataset.
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