Pulmonary CT Images Segmentation using CNN and UNet Models of Deep Learning

Abstract

Image Segmentation performs segregation of distinct segments of an image. Lung segmentation separate different elements of thoracic region. It is an essential prerequisite to several analysis tasks performed on the Computed Tomography (CT) images of lungs. Computational complexity is greatly reduced only when the required area is segregated from the entire CT image. Automated segmentation facilitates quick processing since it requires relatively less time to process more images. Conventional computer based segmentation methods require extensive support for determining the features. Users develop the features and provide to the system which then utilize those features to delineate the required regions. Recent advancements in deep learning showed optimal results in solving numerous image recognition and segmentation problems. The significant characteristic of deep learning is that the model itself learns the features from the input images and then apply the learned features to process new images. The most successful model of deep learning is Convolutional Neural Network (CNN) has outperformed earlier techniques for image recognition, object and face detection and is considered to be the most successful architecture of deep learning. CNN has also been applied for segmentation tasks. In this proposed work, CNN and UNet models have been implemented to evaluate the processing of medical images. The focus of the work is on CT images of lungs. Results obtained on the lungs dataset of 267 images on CNN is 81.34% and UNet is 82.61%. Thus U-Net has improved the dice coefficient by 1.27%. The experiments show that UNet model outperforms CNN model to segment the lung fields in CT images.