基于MultiResUNet-SMIS的皮肤黑色素瘤图像分割

针对现有的皮肤黑色素瘤病灶分割精度不高的问题,结合现有卷积神经网络方法提出皮肤黑色素瘤图像分割方法 MultiResUNet-SMIS.首先,依据皮肤黑色素瘤成像特点,引入不同空洞率的空洞卷积替换普通卷积,在参数量相同的前提下扩大感受野,使网络模型能够适用于多尺度病灶分割任务;其次加入空间和通道注意力机制以重新分配特征权重,扩大感兴趣特征影响,抑制无关特征;最后融合Focal loss与Dice loss提出一种新的loss函数FD loss用于计算回归损失,解决前景背景像素不均衡问题,进一步提高网络模型的分割精度.实验结果表明,MultiResUNet-SMIS在ISIC-2018数据集上的Dice指数、IoU指数以及Acc准确率分别达到了89.47%、82.67%、96.13%,与原MultiResUNet以及UNet、UNet++、DeepLab V3+等主流方法相比, MultiResUNet-SMIS在皮肤黑色素瘤图像分割中具有更好的效果.

Skin Melanoma Image Segmentation Based on MultiResUNet-SMIS

In order to address the problem of low accuracy of skin melanoma lesion segmentation in existing image segmentation methods, a MultiResUNet-SMIS method is proposed based on existing convolution neural network methods. Firstly, according to the imaging characteristics of skin melanoma, the dilation convolution with different dilation rates is introduced to replace the normal convolution, and the receptive field is expanded on the premise of the same parameters so that the model can segment the lesion at multiple scales. Secondly, spatial and channel attention mechanisms are added to the model to redistribute feature weights, expand the influence of features of interest, and suppress irrelevant features. Finally, by combining Focal loss with Dice loss, a new loss function, i.e., FD loss, is proposed to calculate the regression loss and solve the problem of unbalanced foreground and background pixels, so as to further improve the segmentation accuracy of the network model. The experimental results show that Dice, IoU, and Acc of MultiResUNet-SMIS on the ISIC-2018 dataset have reached 89.47%, 82.67%, and 96.13%, respectively, which are better than the original MultiResUNet and mainstream methods such as UNet, UNet++, and DeepLab V3+ in skin melanoma image segmentation.