Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran.
Int J Comput Assist Radiol Surg. 2023 Apr;18(4):695-705. doi: 10.1007/s11548-022-02807-9. Epub 2022 Dec 16.
Lung cancer can evolve into one of the deadliest diseases whose early detection is one of the major survival factors. However, early detection is a challenging task due to the unclear structure, shape, and the size of the nodule. Hence, radiologists need automated tools to make accurate decisions.
This paper develops a new approach based on generative adversarial network (GAN) architecture for nodule detection to propose a two-step GAN model containing lung segmentation and nodule localization. The first generator comprises a U-net network, while the second utilizes a mask R-CNN. The task of lung segmentation involves a two-class classification of the pixels in each image, categorizing lung pixels in one class and the rest in the other. The classifier becomes imbalanced due to numerous non-lung pixels, decreasing the model performance. This problem is resolved by using the focal loss function for training the generator. Moreover, a new loss function is developed as the nodule localization generator to enhance the diagnosis quality. Discriminator nets are implemented in GANs as an ensemble of convolutional neural networks (ECNNs), using multiple CNNs and connecting their outputs to make a final decision.
Several experiments are designed to assess the model on the well-known LUNA dataset. The experiments indicate that the proposed model can reduce the error of the state-of-the-art models on the IoU criterion by about 35 and 16% for lung segmentation and nodule localization, respectively.
Unlike recent studies, the proposed method considers two loss functions for generators, further promoting the goal achievements. Moreover, the network of discriminators is regarded as ECNNs, generating rich features for decisions.
肺癌可能发展成为最致命的疾病之一，早期检测是提高患者生存率的主要因素之一。然而，由于结节的结构、形状和大小不明确，早期检测仍然是一个具有挑战性的任务。因此，放射科医生需要自动化的工具来做出准确的决策。
本文提出了一种基于生成对抗网络（GAN）架构的新型结节检测方法，提出了一个两步 GAN 模型，包括肺分割和结节定位。第一个生成器由 U-net 网络组成，第二个生成器利用 mask R-CNN。肺分割任务涉及对每张图像中的像素进行二分类，将肺像素归为一类，其余像素归为另一类。由于存在大量的非肺像素，分类器变得不平衡，降低了模型的性能。通过使用焦点损失函数对生成器进行训练，可以解决这个问题。此外，还开发了一种新的损失函数作为结节定位生成器，以提高诊断质量。GAN 中的鉴别器网络实现为卷积神经网络（ECNNs）的集合，使用多个 CNN 并连接它们的输出来做出最终决策。
设计了多个实验来评估模型在著名的 LUNA 数据集上的性能。实验表明，与最新的模型相比，所提出的模型可以将 IoU 标准下的错误率分别降低 35%和 16%，用于肺分割和结节定位。
与最近的研究不同，该方法为生成器考虑了两个损失函数，进一步促进了目标的实现。此外，鉴别器网络被视为 ECNNs，为决策生成丰富的特征。
