Atrous convolution aided integrated framework for lung nodule segmentation and classification

Lung cancer has been recognized as the most life-threatening cancer all over the world. Appropriate detection of lung nodule using Computed Tomography (CT) images helps in early stage recognition of lung cancer. Different computer-aided algorithms play an important role in the early diagnosis of lung cancer and can increase the five-year survival rate of lung cancer patients. However, due to structural similarity, manually recognizing the ma-lignant nodule from the benign is time-consuming and challenging task. Recently different deep learning (DL) based Computer-aided diagnosis (CADx) systems have been developed for lung nodule characterization. In this work, an integrated nodule segmentation and characterization framework has been developed using the concept of atrous convolution. The proposed Atrous Convolution-based Convolutional Neural Network (ATCNN) framework can segment and characterize lung nodules by capturing multi-scale features from the HRCT images. Different variants of the ATCNN framework have been analyzed for lung nodule characterization. Among them, ATCNN with a two-layer atrous pyramid and residual connections (ATCNN2PR) has demonstrated the highest classification performance indices for nodule characterization. The new ATCNN2PR framework has obtained an average Dice Similarity Coefficient (DSC), Jaccard Index (JI), and Boundary F1 (BF) score of 0.9715, 0.9520, and 0.9584 for nodule segmentation and sensitivity, specificity, accuracy of 95.84%, 96.89%, and 95.97% for lung nodule characterization on LIDC-IDRI dataset. The proposed automatic trainable end-to-end system has out-performs other competing frameworks by capturing multi-scale features from High-Resolution Computed To-mography (HRCT) nodule images.

Automated summary

Lung cancer is a life-threatening cancer and detecting lung nodules using CT images helps in early recognition. Different computer-aided algorithms can increase the survival rate of lung cancer patients. However, manually recognizing malignant nodules from benign ones is difficult. Deep learning-based CADx systems have been developed for nodule characterization.