IEEE Trans Med Imaging. 2017 Sep;36(9):1876-1886. doi: 10.1109/TMI.2017.2695227. Epub 2017 Apr 18.
Automatic skin lesion segmentation in dermoscopic images is a challenging task due to the low contrast between lesion and the surrounding skin, the irregular and fuzzy lesion borders, the existence of various artifacts, and various imaging acquisition conditions. In this paper, we present a fully automatic method for skin lesion segmentation by leveraging 19-layer deep convolutional neural networks that is trained end-to-end and does not rely on prior knowledge of the data. We propose a set of strategies to ensure effective and efficient learning with limited training data. Furthermore, we design a novel loss function based on Jaccard distance to eliminate the need of sample re-weighting, a typical procedure when using cross entropy as the loss function for image segmentation due to the strong imbalance between the number of foreground and background pixels. We evaluated the effectiveness, efficiency, as well as the generalization capability of the proposed framework on two publicly available databases. One is from ISBI 2016 skin lesion analysis towards melanoma detection challenge, and the other is the PH2 database. Experimental results showed that the proposed method outperformed other state-of-the-art algorithms on these two databases. Our method is general enough and only needs minimum pre- and post-processing, which allows its adoption in a variety of medical image segmentation tasks.
自动进行皮肤病变分割是一个极具挑战性的任务，这是因为病变与周围皮肤之间对比度低、病变边界不规则且模糊、存在各种伪影以及各种成像采集条件。在本文中，我们提出了一种基于 19 层深度卷积神经网络的全自动皮肤病变分割方法，该方法端到端训练，不依赖于数据的先验知识。我们提出了一系列策略，以确保在有限的训练数据下进行有效和高效的学习。此外，我们设计了一种新的基于 Jaccard 距离的损失函数，以消除对样本重新加权的需求，这是在使用交叉熵作为图像分割的损失函数时的典型步骤，因为前景和背景像素的数量存在很强的不平衡。我们在两个公开可用的数据库上评估了所提出框架的有效性、效率以及泛化能力。一个是来自 ISBI 2016 皮肤病变分析以实现黑色素瘤检测挑战赛的数据库，另一个是 PH2 数据库。实验结果表明，所提出的方法在这两个数据库上优于其他最先进的算法。我们的方法足够通用，只需要最小的预处理和后处理，这使其可以应用于各种医学图像分割任务。
