Segmentation of Pulmonary Nodules in CT images

Pulmonary nodules can be associated with several pathologies, such as lung cancer, which is the one of the main causes of death globally. The high incidence of pulmonary nodules reinforces the importance of a continuous monitoring of the patients’ lung health, thus emphasizing the role of an early detection and characterization, considered crucial to enable possibly life-saving interventions. Computer-Aided Diagnosis systems can assist the physicians in their daily tasks, by helping them interpret medical imaging results, such as Computed Tomography (CT) scans. Through out the monitoring process, there is a need to segment the pulmonary nodules, thus defining their boundaries as a way to not only precisely locate them, but also characterize them. Several approaches can be implemented to fulfill this task, facing the numerous challenges that come with a medical image segmentation exercise: among them, the noise, blur and low contrast of the images, and the overall complexity and diversity of anatomical structures, expressed by a variability in size, shape, and texture. This work seeks to overcome these challenges and achieve good results for pulmonary nodule segmentation by employing methods which illustrate conventional and Deep Learning based approaches, in order to compare their performance, and find which one is most likely to possibly contribute to a more accurate and early diagnosis. While conventional techniques are based on knowledge representation, deducing new information using logical rules and a set of beliefs, Deep Learning approaches carry out data-driven modeling, by observing a large amount of data to extract knowledge from it. The selected conventional approach is mostly based on a Local Convergence Filter, due to its ability to deal with noisy and low-contrast images; this filter maximizes the convergence degree of the gradient vectors within a support region toward a central pixel of interest. More specifically, the Sliding Band Filter (SBF) was employed because of its support region a band of fixed width whose position is adjusted in each radial direction -, that grants this filter shape flexibility and a selective response by ignoring the behavior of the gradient at the center of the object. The conventional algorithm estimates the center of nodule, gets the corresponding support points (matching the border coordinates), and then refines the result to achieve a segmentation mask. The Deep Learning based methodologies view this pixel-wise semantic segmentation task as a binary classification exercise, deciding which coordinates of the image belong to the lesion. In this work, two Fully Convolutional Networks were used, whose encoder-decoder structures respectively downsample and upsample the feature maps, and yield symmetric architectures. The first model was the U-Net, developed initially for biomedical segmentation, and thus able to tackle the large size of medical images, as well as the limited amount of annotated training data. Through skip connections, this network allows the combination of contextual information captured in the encoder with the upsampled feature maps in the decoder, thus obtaining more comprehensive spatial knowledge. The second implementation is a hybrid between the U-Net and the SegNet, designated by SegU-Net, which takes advantage of the U-Net’s structure and replaces its upsampling method (upconvolutions) by the SegNet’s max unpooling layers, ideally enhancing the network’s