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

摘要

Deep learning models are now widely applied to various biomedical image analysis tasks such as the image segmentation and classification. However, automation of biomedical image analysis with deep learning is challenging since it requires highly specialized knowledge and large amounts of training data. In this work, we detail automatic multi-class segmentations using deep learning models for lung immunofluorescent (IF) confocal images, along with synthetic image generation of lung images for training these models. Analysis of lung imaging data is important for understanding the lung development at the molecular level and cross-sectional IF images are useful in identifying various structures of the lung. We tested multi-class segmentation using deep learning convolutional neural network (CNN) models with overwrap cropping method as preprocessing to make the dataset larger. Further, we generated synthetic images using deep convolution generative synthetic adversarial network (DCGAN) and use them in learned segmentation networks for creating segmentation masks. In terms of deep learning segmentation models, we adapted the state-of-the-art U-Net, SegNet, and DeepLabv3+ based models for multi-class segmentation from lung IF images. Our experimental results on these challenging lung IF images show that the highest dice score for training 98.7%, and testing 87.0% is obtained by an adapted multiclass U-Net method. Further, our synthetic image generation shows promise for future training paradigms in improving the segmentation of various lung structures in IF confocal images.