Automatic accurate skin lesion segmentation systems are very helpful for timely diagnosis and treatment of skin cancers. Recently, methods based on convolutional neural networks (CNN) have presented powerful performances and good results in biomedical applications. In the proposed method, a novel structure based on Mask RCNN, a proposed CNN, and a geodesic segmentation method is presented to improve the performance of the skin lesion segmentation. Lesions are detected and segmented by the Mask R-CNN in the first stage. A multi-atrous full convolutional neural network (MAFCNN) is proposed to combine outputs of the Mask RCNN and the input image to present more accurate segmentation results. To modify boundary of the lesion segmented by the MAFCNN, a geodesic segmentation method is used. Some parts of the segmentation result of the proposed CNN are utilized as labeled pixels for the geodesic method. Results demonstrate that using the proposed MAFCNN in a novel structure followed by the geodesic method significantly improves the mean Jaccard value. Experiments on five well-known skin image datasets show that the proposed method outperforms other state-of-the-art methods.

中文翻译：

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基于mask RCNN、Multi Atrous Full-CNN和测地线方法的皮肤病灶分割

自动准确的皮肤病变分割系统对于皮肤癌的及时诊断和治疗非常有帮助。最近，基于卷积神经网络（CNN）的方法在生物医学应用中表现出强大的性能和良好的效果。在所提出的方法中，提出了一种基于Mask RCNN、所提出的CNN和测地线分割方法的新结构，以提高皮肤病变分割的性能。在第一阶段由 Mask R-CNN 检测和分割病变。提出了一种多孔全卷积神经网络（MAFCNN）来结合Mask RCNN的输出和输入图像以呈现更准确的分割结果。为了修改由 MAFCNN 分割的病变边界，使用测地线分割方法。所提出的 CNN 分割结果的某些部分被用作测地线方法的标记像素。结果表明，在新结构中使用所提出的 MAFCNN，然后使用测地线方法显着提高了平均 Jaccard 值。在五个著名的皮肤图像数据集上的实验表明，所提出的方法优于其他最先进的方法。