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.
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机译:我们提出IW-Net,一个深入的学习模型,允许计算机断层摄影图像中的肺结节的自动和交互分割。 IW-Net由两个块组成:第一个提供自动分段,第二个块通过分析Nodule的边界中用户引入的2个点来校正它。为此目的,提出了一种物理启发了将用户输入考虑到帐户的权重映射,其既以特征映射和系统的损耗函数使用。我们的方法在公共LIDC-IDRI数据集上进行了广泛评估,在那里我们实现了0.55个交叉口的最先进的性能,而ONION vs 0.59间观察员协议。此外,我们表明IW-Net允许纠正小结节的分割,对于适当的患者推荐决定,以及改善挑战性非实心结节的细分,因此可能是增加早期诊断的重要工具肺癌。
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