摘要：

针对黑色素瘤的医学图像分割, 以U-Net为骨干提出一种新的医学图像分割网络SEHC-Net。设计一个感知及边缘增强模块(SEBM)的新结构来处理分割形状不规则、大小多样和边界模糊的黑色素瘤图像。SEBM可以扩大特征的感受野, 增强模型提取目标边缘信息和进一步捕捉像素之间联系的能力。此外, 提出层级补偿模块(HCM)来解决信息拼接过程中长连接导致的信息冗余问题, 以弥补主流分割网络在特征提取阶段不能在空间上下文信息和高级语义信息之间充分平衡的缺陷。同时, 为了缓解由于引入以上两种结构导致的参数量增加问题, 引入GoogleNet中的Inception思想, 减小网络原始的编解码卷积模块中的卷积核尺寸, 从而在降低模型参数量的同时增加模型的宽度和深度, 并增强捕获像素间关联的能力, 提升分割算法的性能。在ISIC2018黑色素瘤数据集上进行验证的结果表明, 所提出的分割算法的交并比(IoU)、敏感度、精确率、Dice系数和准确率分别达到了79.54%、86.29%、90.92%、84.39%和94.83%, 有效提升了黑色素瘤的分割性能。

关键词: 黑色素瘤, 感知及边缘增强模块, 分层补偿模块, 语义信息, 信息补偿

Abstract:

Using U-Net as the backbone, a novel medical image segmentation network called SEHC-Net is proposed for medical image segmentation of melanoma. A new structure named Sense and Edge Boost Module (SEBM) is designed specifically to address the challenges in segmenting melanoma images having irregular shapes, diverse sizes, and blurry boundaries. SEBM can expand the receptive fields of features, which enhances the model's ability to extract the target edge information and further capture the connections between pixels. Additionally, a hierarchical compensation module is proposed to solve the problem of information redundancy caused by long connections during information concatenation. This can compensate for the defect that mainstream segmentation networks cannot fully balance spatial contextual information and high-level semantic information in the feature extraction stage. GoogleNet's Inception is used to reduce the parameter increase by reducing the kernel size and increasing the model depth. The segmentation algorithm is verified on the ISIC2018 melanoma dataset. Experimental results show that the Intersection over Union (IoU), sensitivity, precision, Dice coefficient, and accuracy are 79.54%, 86.29%, 90.92%, 84.39%, and 94.83%, respectively. Therefore, the proposed algorithm can effectively improve the melanoma segmentation performance.

Key words: melanoma, Sense and Edge Boost Module (SEBM), Hierarchical Compensation Module (HCM), semantic information, information compensation