Segmentation of Pulmonary Nodules Using SegNet and Adversarial Networks
Authors: Cheppamkuzhi Vinod C , D D. MENAKA 
Published: May 2022

BackgroundLung cancer is seen as one of the most common lung diseases in common people. For the patients having symptoms, the presence of lung nodule is checked by using various imaging techniques. Pulmonary nodules are detected in most of the cases having symptoms. But identifying the type of the nodule and the categorization still remains as a challenge. After confirming the presence of nodule (benign or malignant) it takes other several steps to identify its characteristics. Improved imaging methods produce results within a short span of time. Research works are going on to increase the overall efficiency of the system. The proposed system consider authentic data source for the study. The benign and malignant samples are considered for the generation of realistic large image set. The generation of large data set with the help of generative adversarial network (GAN) is the first part of the work. The generated images by GAN can’t be differentiated from the original images even by a trained radiologist. This proves the importance of images generated by GAN. GAN is able to generate 1024x1024 resolution for natural images. Real data images are used to fine tune the SegNet output. Through transfer learning these weights are transferred to the system for segmentation of the images. The training process use real and generated images, which improve efficiency of the network. The original data from LUNA 16 is used to further generate benign and malignant samples using GAN. A total of 440 images and its augmented images are used for training GAN and generated 100,1000 images. Hence the overall efficiency of the system gets improved. To verify the same various combinations and method are considered and tabulated various parameters. Methods with SegNet, GAN and other combinations are evaluated to verify the efficiency of the system. Receiver operating characteristics also plotted and compared the area under the curve for verification of the result. Results Proposed method having GAN along with image augmentation is compared with other methods. GAN with SegNet shows very small deviation from the proposed method. This clearly shows the advantage of using GAN for generating images for nodule segmentation. The method is also compared with other Encoder-Decoder architectures. The performance comparison is tabulated. Finally the receiver operating characteristic (ROC) curve of SegNet while training the model with GAN and without GAN is also investigated. The Area Under the Curve (AUC) for SegNet with GAN is found to be 0.851 while that of SegNet is 0.76. The proposed method stood ahead in all respect, considered.ConclusionThe proposed work with the help of available data set manages to generate sufficient amounts of data with the help of GAN. Perfect samples are considered here for the generation of data. Hence the data generated assures the quality and that is used for training the network. For training the GAN, 440 images and augmented images are used. 100,1000 images generated and are used for training the SegNet architecture. The segmentation shows improved accuracy in the case of benign as it has a perfect shape compared to malignant samples. Through the evaluation of the parameters it is clear that the GAN along with image augmentation shows better performance.