Master's Thesis

Artificial Intelligence (AI) is growing exponentially with novel computational architectures and their cognitive capabilities. AI helps solve complex problems in medical imaging. Lung segmentation is essential since it processes the volumetric information of the lungs, removes the unnecessary areas of the scan, and segments the actual area of the lungs in CT scan. Deep learning is also called deep structured learning that is a part of machine learning. The thesis focuses on deep learning applications to segment lungs and further develop a novel algorithm to make it robust. Supervised learning requires data to train a deep neural network. Deep learning eliminates feature engineering by progressively extracting complex high-level features from available training data. Recently, the deep learning model, such as U-Net, outperforms other network architectures for biomedical image segmentation. In this thesis, two different deep neural networks based on U-Net are proposed for the lung and lung lesion segmentation tasks. The proposed models integrate convolution into the sophisticated Multiscale Recurrent Residual Neural Network based on U-Net. It helps learn spatial dependencies in different dimensions and increases the propagation of spatial information. One of the proposed deep neural networks is trained on the publicly available dataset – LUNA16 and achieves state-of-the-art performance on both LUNA16 (testing set) and VESSEL12 dataset for lung segmentation. Both deep neural network (DNN) and availability of diverse annotated data make the given deep learning based solution robust and generalized for practical use. Even if having sophisticated DNN, scarcity of annotated data challenges the expected outcomes. We are further applying our research to help address medical imaging problems in the current pandemic. Classification of lung CT scans into COVID-19 positive and negative, is an essential task that eases the diagnosis, especially in the absence of other diagnostic tests. Further, having computational power, cloud infrastructure, and peripherals speed up medical imaging based diagnostic tools. Robust segmentation of COVID-19 infected lungs requires rich labeled data. Accurate pixel-level annotation tasks to generate such data are time-consuming, and that delays data preparation. We propose a novel algorithm to generate lesion-like artificial patterns, and U-Net based deep neural network for robust lung segmentation will further help segment COVID-19 lung infection. The pattern generation algorithm generates 2D and 3D patterns to create an enormous amount of synthetic data. This algorithm and DNN give accurate lung segmentation results for highly infected lungs and further provides infection segmentation. This research applies to the preprocessing stages of different applications of deep learning, medical imaging, and data annotation. The proposed work helps the deep neural network to generalize on a given domain to accomplish robust segmentation results in the absence of exact data. AI should never compete with humans and replace but help and assist and collaborate instead. The future of these proposed methods not only depends upon deep learning methods but also depends upon the continuous collaboration with radiologists and the feedback loop. It will go hand in hand benefiting and assisting radiologists to spend more time understanding the patients.