Multi-Class Segmentation of Lung Immunofluorescence Confocal Images Using Deep Learning

Isaka, Shu; Kawanaka, Hiroharu; Aronow, Bruce J.; Prasath, V. B. Surya

doi:10.1109/bibm47256.2019.8983146

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2019

DOI: 10.1109/bibm47256.2019.8983146

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Multi-Class Segmentation of Lung Immunofluorescence Confocal Images Using Deep Learning

Shu Isaka

Hiroharu Kawanaka

Bruce J. Aronow

et al.

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2022

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Data Augmentation Using Generative Adversarial Networks for Multi-Class Segmentation of Lung Confocal IF Images

Katsuma¹,

Kawanaka²,

Prasath³

et al. 2022

JACIII

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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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Data Augmentation Using Generative Adversarial Networks for Multi-Class Segmentation of Lung Confocal IF Images

Katsuma¹,

Kawanaka²,

Prasath³

et al. 2022

JACIII

View full text Add to dashboard Cite

show abstract

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Multi-Class Segmentation of Lung Immunofluorescence Confocal Images Using Deep Learning

Cited by 1 publication

References 12 publications

Data Augmentation Using Generative Adversarial Networks for Multi-Class Segmentation of Lung Confocal IF Images

Data Augmentation Using Generative Adversarial Networks for Multi-Class Segmentation of Lung Confocal IF Images

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