Deep Learning for low-dose Computed Tomography CAD systems

Every year, more than two million people are diagnosed with lung cancer, demonstrating a survival rate of only 20% after 5 years after being diagnosed [1]. This leads to 1.8 million people dying from lung cancer every year, making it the leading cause of cancer-related deaths worldwide [2].

Lung cancer screening (LCS) programs have demonstrated success in decreasing the mortality when diagnosing the cancer in an early more treatable stage. Wide-scale LCS programs, such as the NLST (national lung screening trial), showed the reduction in 20% mortality in high-risk populations [3]. In direct comparison to X-ray radiography, the use of low-dose computed tomography (LDCT) has proven to be more effective for LCS, which is why upcoming nation-wide screening initiatives suggest the use of LDCT [4]. To support manual findings, experts suggest the additional use of computer-aided detection (CADe) as a secondary read to find, measure, and classify pulmonary nodules [4].

Even among experienced radiologists, there is still a moderate to high amount of inter-reader variability, depending on the size and density of individual pulmonary nodules, which affect patient management [5], but can also carry over to the both the truthing and performance of supervised learning methods. The first part of this thesis includes the assessment of the influence of the dose level (and thus the image noise) of different thoracic CT acquisitions onto the performance of different CAD systems (detection and segmentation) w.r.t the characteristics of individual pulmonary findings. Images with different dose levels will be provided. In the next step, based on the simulated dose levels, a neural network to denoise the thoracic LDCT images should be implemented. During the last years, various approaches have been introduced to apply denoising in the spatial domain, a transformed domain, and by utilizing convolutional neural networks (CNNs) and generative adversarial networks (GANs) [6] [7]. Within this work, the conventional dose level of the acquisition (ideally a full-dose scan) serves as the ground truth for the supervised learning.