Lung cancer is the leading cause of cancer-related deaths worldwide. According to the American Cancer Society, early diagnosis of pulmonary nodules in computed tomography (CT) scans can improve the five-year survival rate up to 70% with proper treatment planning. In this article, we propose an attribute-driven Generative Adversarial Network (ADGAN) for synthesis and multiclass classification of Pulmonary Nodules. A self-attention U-Net (SaUN) architecture is proposed to improve the generation mechanism of the network. The generator is designed with two modules, namely, self-attention attribute module (SaAM) and a self-attention spatial module (SaSM). SaAM generates a nodule image based on given attributes whereas SaSM specifies the nodule region of the input image to be altered. A reconstruction loss along with an attention localization loss (AL) is used to produce an attention map prioritizing the nodule regions. To avoid resemblance between a generated image and a real image, we further introduce an adversarial loss containing a regularization term based on KL divergence. The discriminator part of the proposed model is designed to achieve the multiclass nodule classification task. Our proposed approach is validated over two challenging publicly available datasets, namely LIDC-IDRI and LUNGX. Exhaustive experimentation on these two datasets clearly indicate that we have achieved promising classification accuracy as compared to other state-of-the-art methods.

Pulmonary fissure segmentation is important for localization of lung lesions which include nodules at respective lobar territories. This can be very useful for diagnosis as well as treatment planning. In this paper, we propose a novel coarse-to-fine fissure segmentation approach by proposing a Multi-View Deep Learning driven Iterative WaterShed Algorithm (MDL-IWS). Coarse fissure segmentation obtained from multi-view deep learning yields incomplete fissure volume of interest (VOI) with additional false positives. An iterative watershed algorithm (IWS) is presented to achieve fine segmentation of fissure surfaces. As a part of the IWS algorithm, surface fitting is used to generate a more accurate fissure VOI with substantial reduction in false positives. Additionally, a weight map is used to reduce the over-segmentation of watershed in subsequent iterations. Experiments on the publicly available LOLA11 dataset clearly reveal that our method outperforms several state-of-the-art competitors.

This letter introduces a unified framework for accurate segmentation of five different types of pulmonary nodules, namely, solid, juxtapleural, juxtavascular, part solid and ground glass by designing a contrast-adaptive shape-driven level set algorithm. Most of the existing methods have targeted segmenting few specific types of nodules. Variability of shapes along with poor contrast make pulmonary nodule segmentation an extremely challenging problem. To deal with low contrast, a contrast-adaptive term, based on intensities, is incorporated to guide the evolution of level set. A shape term is further introduced for accurate segmentation of different pulmonary nodules having varying shapes. Experiments on the publicly available LIDC/IDRI dataset clearly reveal that our method achieves promising results as compared to several state-of-the-art competitors.

Accurate pulmonary nodule segmentation, an essential pre-requisite in every computer-aided diagnosis (CAD) system, significantly helps in the risk assessment of lung cancer. In this paper, we propose a synergistic combination of deep learning and shape driven level sets for automated and accurate lung nodule segmentation. A coarse-to-fine solution is adopted, where, a deep fully convolutional network is employed to obtain coarse segmentation. To achieve fine segmentation, shape driven evolution of level sets is designed. The seed points for initializing the level sets are obtained from the coarse segmentation of deep network in an automated manner. Perimeter and circularity of the evolving contours are employed for guiding the evolution of level sets. Experiments on the publicly available LIDC/IDRI dataset clearly reveal that our method outperforms several state-of-the-art competitors as well as its constituent parts, i.e., deep network and level set, when applied in isolation. (C) 2019 Elsevier B.V. All rights reserved.

Pulmonary fissure detection is an important step for lung lobe segmentation which is necessary for accurate diagnostics and surgical planning. Automatic detection of fissures in CT images is a challenging task due to varying intensity, pathological deformation and noisy acquisitions. In this paper, we propose a novel fissure line detection technique using eigen analysis of the hessian matrix and an exhaustive sample consensus (ESAC) based line fitting in small overlapping windows. The idea behind using the line fitting technique is that the fissure line appears as piece-wise linear segment in a small window. As opposed to RANSAC, the point selection mechanism in the proposed method chooses all combination of data points exhaustively. This approach reduces the possibility of missing the possible candidate points for a fissure line. Our main contribution lies in detection of the fissure line without using any training data as well as any template matching model. The performance of our method is validated on the publicly available LOLA11 database. Comparisons with some existing approaches on this database indicate the advantage of the proposed solution.