An Attention Learning-Enabled 3D Conditional Generative Adversarial Network for Lung Nodule Segmentation

Early lung cancer detection using lung nodule segmentation can enhance patient survival. In computer vision applications like medical image analysis, convolutional neural networks (CNNs) outperform standard image processing methods. Convolutional neural network-based medical image segmentation methods have exhibited state-of-the-art performance, but they still face limitations overfitting and poor performance due to data scarcity and class imbalance. This study proposes an attention learning-enabled 3D conditional generative adversarial network for lung nodule segmentation that learns nodule-focused data distribution to boost accuracy. The proposed network utilizes attention learning modules (ALMs) in generator part based on U-Net as well as the discriminator based on a simple classification network. It empowered the model to distinguish ground truth from falsified segmentation. Also, patch-based training reduces overfitting. The LUNA16 dataset is used to perform extensive lung nodule segmentation experiments. The proposed model's Boundary F1-score, average Jaccard Index, and Dice Similarity Coefficient are 0.9868, 0.9716, and 0.9762 respectively, outperforming various state-of-the-art models. This model has remarkable lung nodule segmentation performance and can help doctors diagnose lung nodules early by assessing their size, shape, and other properties.