Recent advances of semantic image segmentation greatly benefit from deeper and larger Convolutional Neural Network (CNN) models. Compared to image segmentation in the wild, properties of both medical images themselves and of existing medical datasets hinder training deeper and larger models because of overfitting. To this end, we propose a novel two-stream UNET architecture for automatic end-to-end medical image segmentation, in which intensity value and gradient vector flow (GVF) are two inputs for each stream, respectively. We demonstrate that two-stream CNNs with more low-level features greatly benefit semantic segmentation for imperfect medical image datasets. Our proposed two-stream networks are trained and evaluated on the popular medical image segmentation benchmarks, and the results are competitive with the state of the art. The code will be released soon.
翻译:与野生图像分割相比,医学图像本身和现有医疗数据集的特性都妨碍了对更深、更大型模型的培训。为此,我们提出一个新的双流UNET结构,用于自动端至端医疗图像分割,其中密度值和梯度矢量流(GVF)是每一流的两种输入物。我们证明,具有更低级特征的双流CNN双流CNN极有利于不完善的医疗图像数据集的语义分割。我们提议的双流网络就流行的医疗图像分割基准接受培训和评价,其结果与最新技术竞争。该代码将很快发布。