论文标题
用于医学图像中语义分割的两流UNET网络
Two-Stream UNET Networks for Semantic Segmentation in Medical Images
论文作者
论文摘要
语义图像分割的最新进展极大地受益于更深入和更大的卷积神经网络（CNN）模型。与野外的图像分割相比，由于过度拟合，医疗图像本身和现有医疗数据集的属性都阻碍了更深和更大的模型。为此，我们为自动端到端的医学图像分割提出了一种新型的两流UNET架构，其中强度值和梯度向量流（GVF）分别是每个流的两个输入。我们证明，具有更低级别的两流CNN具有不完美的医学图像数据集的大大有益于语义细分。我们提出的两流网络对流行的医学图像分割基准进行了培训和评估，结果与最新技术具有竞争力。该代码将很快发布。
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.
