Cancer Center, Jiangdu People's Hospital, Yangzhou, Jiangsu, China.
Department of Oncology, Jiangdu People's Hospital, Yangzhou, Jiangsu, China.
Comput Intell Neurosci. 2022 May 17;2022:7124902. doi: 10.1155/2022/7124902. eCollection 2022.
Pulmonary nodules are the early manifestation of lung cancer, which appear as circular shadow of no more than 3 cm on the computed tomography (CT) image. Accurate segmentation of the contours of pulmonary nodules can help doctors improve the efficiency of diagnosis. Deep learning has achieved great success in computer vision. In this study, we propose a novel network for pulmonary nodule segmentation from CT images based on U-NET. The proposed network has two merits: one is that it introduces dense connection to transfer and utilize features. Additionally, the problem of gradient disappearance can be avoided. The second is that it introduces a new loss function which is tolerance on the pixels near the borders of the nodule. Experimental results show that the proposed network at least achieves 1% improvement compared with other state-of-art networks in terms of different criteria.
肺部结节是肺癌的早期表现，在计算机断层扫描（CT）图像上表现为不超过 3cm 的圆形阴影。准确分割肺部结节的轮廓可以帮助医生提高诊断效率。深度学习在计算机视觉领域取得了巨大的成功。在这项研究中，我们提出了一种基于 U-NET 的新型 CT 图像肺部结节分割网络。所提出的网络有两个优点：一是引入密集连接来传输和利用特征。此外，可以避免梯度消失的问题。二是引入了一种新的损失函数，对结节边界附近的像素具有容忍性。实验结果表明，在所提出的网络在不同标准下与其他最先进的网络相比，至少有 1%的改进。
