基于对抗学习和多尺度特征融合的前列腺MR图像分割

前列腺MR图像的自动分割已被广泛应用于前列腺癌的诊断和治疗过程中,然而,由于前列腺的形状变化显著且与相邻组织的对比度低,传统的分割方法仍存在精度低、速度慢等缺点.生成对抗网络GAN在计算机视觉任务中展示出了优越的性能,因此提出了一种使用对抗学习的概念来训练分割网络的方法,实现前列腺MR图像端到端的自动分割.模型框架主要由分割网络和判别网络构成,分割网络生成分割预测图,判别网络判断输入来自真实标签还是分割预测.同时,在分割网络中集成了感受野模块RFB来获取和融合深度特征的多尺度信息,提高特征的识别率和鲁棒性,以提升网络的分割性能.在PROMISE12数据集上的验证结果显示,该模型的DSC和HD分别为89.56％ 和7.65 mm.

Prostate MR image segmentation based on adversarial learning and multi-scale feature fusion

The automatic segmentation of prostate MR images has been widely used in the diagnosis and treatment of prostate cancer. However, due to the significant changes in the shape of the prostate and low contrast with adjacent tissues, traditional segmentation methods still have disadvantages such as low accuracy and slow speed. Generative adversarial networks (GAN) have shown superior performance in computer vision tasks, so this paper proposes a method of training segmentation networks using the concept of adversarial learning to achieve end-to-end automatic segmentation of prostate MR images. The model framework is mainly composed of a segmentation network and a discriminant network. The segmentation network generates a segmentation prediction map, and the discrimination network judges whether the input comes from a real label or a segmentation prediction. At the same time, the receptive field block (RFB) is integrated in the segmentation network to acquire and fuse multi-scale information of deep features, improve the recognition rate and robustness of features, and improve the segmentation performance of the network. Through verification on the PROMISE12 data set, the DSC and HD of the model are 89.56% and 7.65 mm, respectively.