Melanoma is one kind of dangerous cancer that has been increasing rapidly in the world. Initial diagnosis is essential to survival, but often the disease is diagnosed in the fatal stage. The rapid growth of skin cancers raises a huge demand for accurate automatic skin lesion segmentation. While deep learning techniques, i.e., convolutional neural network (CNN), have been widely used for precise segmentation, the existing densely connected network (DenseNet) and residual network (ResNet)–based encoder-decoder architectures used non-biomedical features for skin lesion tasks. The complexity of tuned parameters, small information in the pre-trained features, and the lack of multi-scale information degrade the performance of skin lesion segmentation. To address these issues, we present encoder-decoder–based CNN for skin lesion segmentation, based on the widely used UNet architecture. We exploit the benefit of combining DenseNet and ResNet to improve the performance of skin lesion segmentation. In the encoder path, atrous spatial pyramid pooling (ASPP) is used to generate multi-scale features from different dilation rates. We used dense skip connection to combine the feature maps of both encoder and decoder paths. We evaluate our approach on ISIC 2018 dataset and achieve competitive performance as compared to other state-of-the-art approaches. Compared to the previous UNet approaches, our method gains a high Jaccard index, Dice, accuracy, and sensitivity. We think that this progress is mainly due to the combined architecture of DenseNet, ResNet, ASPP, and dense skip connection that preserve the contextual information in the encoder-decoder paths. We utilized the combined benefits of both recent DenseNet and ResNet architectures. We used ASPP to exploit multi-scale contextual information by adopting multiple dilation rates. We also implemented dense skip connections for better recovery of fine-grained information of target objects. In the future, we believe that this approach will be helpful to other medical image segmentation tasks.

黑色素瘤是一种危险的癌症，在世界范围内迅速增加。初步诊断对生存至关重要，但通常会在致命阶段诊断出该疾病。皮肤癌的快速发展提出了对精确的自动皮肤病变分割的巨大需求。虽然深度学习技术（即卷积神经网络（CNN））已广泛用于精确分割，但是基于密集连接网络（DenseNet）和残差网络（ResNet）的现有编码器-解码器体系结构使用非生物医学特征来治疗皮肤病变任务。调整参数的复杂性，预先训练的特征中的信息少以及缺乏多尺度信息会降低皮肤病变分割的性能。为了解决这些问题，我们提出了基于编码器-解码器的CNN用于皮肤病变分割，基于广泛使用的UNet架构。我们利用DenseNet和ResNet相结合的优势来改善皮肤病变分割的性能。在编码器路径中，使用无空间金字塔金字塔池（ASPP）从不同的膨胀率生成多尺度特征。我们使用密集跳过连接来组合编码器和解码器路径的特征图。与其他最新方法相比，我们在ISIC 2018数据集上评估了我们的方法并获得了竞争表现。与以前的UNet方法相比，我们的方法获得了较高的Jaccard指数，Dice，准确性和灵敏度。我们认为，这一进展主要是由于DenseNet，ResNet，ASPP和密集跳过连接的组合体系结构将上下文信息保留在编码器-解码器路径中。我们利用了最新的DenseNet和ResNet架构的综合优势。我们使用ASPP通过采用多种扩张率来利用多尺度上下文信息。我们还实现了密集的跳过连接，以更好地恢复目标对象的细粒度信息。将来，我们相信这种方法将有助于其他医学图像分割任务。