Automatic skin lesion segmentation in dermoscopy images is challenging due to the diversity of skin lesion characteristics, low contrast between normal skin and lesions, and the existence of many artefacts in the images. To meet these challenges, we propose a novel segmentation topology called FC-DPN, which is built upon a fully convolutional network (FCN) and dual path network (DPN). The DPN inherits the advantages of residual and densely connected paths, enabling effective feature re-usage and re-exploitation. We replace dense blocks in fully convolutional DenseNets (FC-DenseNets) with two kinds of sub-DPN blocks, namely, sub-DPN projection blocks and sub-DPN processing blocks. This framework enables FC-DPN to acquire more representative and discriminative features for more accurate segmentation. Many images in the original ISBI 2017 Skin Lesion Challenge test dataset are given the incorrect or inaccurate ground truths, and these ground truths have been revised. The revised test dataset is called the modified ISBI 2017 Skin Lesion Challenge test dataset. The proposed method achieves an average Dice coefficient of 88.13$\text{\%}$ and a Jaccard index of 80.02$\text{\%}$ on the modified ISBI 2017 Skin Lesion Challenge test dataset and 90.26$\text{\%}$ and 83.51$\text{\%}$, respectively, on the PH2 dataset. Extensive experimental results on the two datasets demonstrate that the proposed method exhibits better performance than FC-DenseNets and other well-established segmentation algorithms.

由于皮肤病变特征的多样性，正常皮肤与病变之间的对比度低以及图像中存在许多伪像，因此在皮肤镜检查图像中自动进行皮肤病变分割具有挑战性。为了应对这些挑战，我们提出了一种称为FC-DPN的新型分段拓扑，该拓扑基于完全卷积网络（FCN）和双路径网络（DPN）构建。DPN继承了残留路径和密集连接路径的优点，从而可以有效地重用和重新利用要素。我们将全卷积DenseNet（FC-DenseNets）中的密集块替换为两种子DPN块，即子DPN投影块和子DPN处理块。此框架使FC-DPN可以获取更具代表性和区分性的功能，以实现更准确的细分。最初的ISBI 2017皮肤病变挑战测试数据集中的许多图像都被赋予了不正确或不正确的地面真相，并且这些地面真相已被修改。修改后的测试数据集称为修改后的ISBI 2017皮肤病变挑战测试数据集。所提出的方法实现了平均骰子系数为88.13$\text{％}$ 雅卡德指数为80.02$\text{％}$ 修改后的ISBI 2017皮肤病变挑战测试数据集和90.26$\text{％}$ 和83.51$\text{％}$分别在PH2数据集上。在两个数据集上的大量实验结果表明，所提出的方法比FC-DenseNets和其他完善的分割算法具有更好的性能。