iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network
iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network
复制标题
DOI：
10.1038/s41598-019-48004-8
复制
发表时间：
2019-08-12
影响因子：
4.6
通讯作者：
Campilho, Aurelio
中科院分区：
文献类型：
--
作者：
Aresta, Guilherme;Jacobs, Colin;Campilho, Aurelio
我们提出了iW-Net，这是一种深度学习模型，可以对计算机断层扫描图像中的肺结节进行自动和交互式分割。iW-Net由两个模块组成：第一个模块提供自动分割，第二个模块允许通过分析用户在结节边界中引入的2个点进行校正。为此，提出了一种物理启发的权重图，考虑到用户输入，这是用来作为一个功能图和系统的损失函数。我们的方法在公共LIDC-IDRI数据集上进行了广泛的评估，在那里我们实现了0.55交叉点对联合的最先进性能，而观察者间的一致性为0.59。此外，我们还表明，iW-Net可以纠正小结节的分割，这对于正确的患者转诊决策至关重要，并且可以改善具有挑战性的非实性结节的分割，因此可能是提高肺癌早期诊断的重要工具。
We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images. iW-Net is composed of two blocks: the first one provides an automatic segmentation and the second one allows to correct it by analyzing 2 points introduced by the user in the nodule's boundary. For this purpose, a physics inspired weight map that takes the user input into account is proposed, which is used both as a feature map and in the system's loss function. Our approach is extensively evaluated on the public LIDC-IDRI dataset, where we achieve a state-of-the-art performance of 0.55 intersection over union vs the 0.59 inter-observer agreement. Also, we show that iW-Net allows to correct the segmentation of small nodules, essential for proper patient referral decision, as well as improve the segmentation of the challenging non-solid nodules and thus may be an important tool for increasing the early diagnosis of lung cancer.