论文标题
U-DET：带有双向特征网络的修改后的U-NET体系结构用于肺结节分段
U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation
论文作者
论文摘要
肺癌的早期诊断和分析涉及计算机断层扫描（CT）图像中精确有效的肺结分段。但是，CT图像中结节的匿名形状，视觉特征和周围环境对肺结节的稳健分割构成了一个具有挑战性的问题。本文提出了一种资源有效的模型体系结构U-DET，这是一种解决手头任务的端到端深度学习方法。它结合了编码器和解码器之间的BI-FPN（双向特征网络）。此外，它使用MISH激活函数和遮罩的级别重量来提高分割效率。该模型对由1186个肺结核组成的公共LUNA-16数据集进行了广泛的培训和评估。 U-DET体系结构的表现优于现有的U-NET模型，骰子相似性系数（DSC）为82.82％，并获得与人类专家相当的结果。
Early diagnosis and analysis of lung cancer involve a precise and efficient lung nodule segmentation in computed tomography (CT) images. However, the anonymous shapes, visual features, and surroundings of the nodule in the CT image pose a challenging problem to the robust segmentation of the lung nodules. This article proposes U-Det, a resource-efficient model architecture, which is an end to end deep learning approach to solve the task at hand. It incorporates a Bi-FPN (bidirectional feature network) between the encoder and decoder. Furthermore, it uses Mish activation function and class weights of masks to enhance segmentation efficiency. The proposed model is extensively trained and evaluated on the publicly available LUNA-16 dataset consisting of 1186 lung nodules. The U-Det architecture outperforms the existing U-Net model with the Dice similarity coefficient (DSC) of 82.82% and achieves results comparable to human experts.
