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首页> 外文期刊>IEEE Transactions on Medical Imaging >Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance
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Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance

机译：使用具有Jaccard距离的深度完全卷积网络自动进行皮肤病变分割

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摘要

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.
机译：由于病变与周围皮肤之间的对比度低，病变边界不规则和模糊，存在各种伪影以及各种成像采集条件，因此在皮肤镜图像中自动进行皮肤病变分割是一项艰巨的任务。在本文中，我们提出了一种利用19层深度卷积神经网络对皮肤病变进行分割的全自动方法，该方法是端到端训练的，并且不依赖于数据的先验知识。我们提出了一套策略，以确保在有限的培训数据的基础上进行有效和高效的学习。此外，我们设计了一种基于Jaccard距离的新颖损失函数，消除了对样本重新加权的需要，这是将交叉熵用作图像分割的损失函数时的典型过程，因为前景像素和背景像素数量之间的不平衡性很强。我们在两个可公开获得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。一个来自ISBI 2016皮肤病灶分析，以应对黑色素瘤检测挑战，另一个是PH2数据库。实验结果表明，该方法在这两个数据库上均优于其他最新算法。我们的方法足够通用，只需要最少的预处理和后处理，就可以在各种医学图像分割任务中采用。

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