Radiologist is "doctor's doctor", biomedical image segmentation plays a central role in quantitative analysis, clinical diagnosis, and medical intervention. In the light of the fully convolutional networks (FCN) and U-Net, deep convolutional networks (DNNs) have made significant contributions in biomedical image segmentation applications. In this paper, based on U-Net, we propose MDUnet, a multi-scale densely connected U-net for biomedical image segmentation. we propose three different multi-scale dense connections for U shaped architectures encoder, decoder and across them. The highlights of our architecture is directly fuses the neighboring different scale feature maps from both higher layers and lower layers to strengthen feature propagation in current layer. Which can largely improves the information flow encoder, decoder and across them. Multi-scale dense connections, which means containing shorter connections between layers close to the input and output, also makes much deeper U-net possible. We adopt the optimal model based on the experiment and propose a novel Multi-scale Dense U-Net (MDU-Net) architecture with quantization. Which reduce overfitting in MDU-Net for better accuracy. We evaluate our purpose model on the MICCAI 2015 Gland Segmentation dataset (GlaS). The three multi-scale dense connections improve U-net performance by up to 1.8% on test A and 3.5% on test B in the MICCAI Gland dataset. Meanwhile the MDU-net with quantization achieves the superiority over U-Net performance by up to 3% on test A and 4.1% on test B.
翻译:生物医学图象分割在定量分析、临床诊断和医疗干预中发挥着核心作用。鉴于完全革命网络(FCN)和U-Net,深革命网络(DNNS)在生物医学图象分割应用方面做出了重大贡献。在本文中,我们根据U-Net,提出MDUnet,这是一个用于生物医学图象分割的多尺度密集连接的U-net。我们提议为U形结构的编码器、解密器和它们之间建立三种不同的多尺度密集连接。我们建筑的亮点是直接结合来自更高层和低层的相邻比例地貌图,以加强当前层的地貌传播。这在很大程度上可以改善信息流编码、解码和跨层的图象。多尺度的稠密连接,这意味着在接近输入和输出的层之间保持较短的连接。我们采用了基于实验的最佳模型,并提出了一个新的多尺度 U-ENS 3Net(MDU-Net) 结构结构的精度结合了从更高层层层层和低层的图象图象图象图象图象图象图象图象化。通过AAI-MA-S-C-G的精确测试模型,改进了A-G-C-MA-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-ROB-S-S-C-C-S-C-S-S-S-S-S-S-C-S-S-S-S-S-S-S-B-C-C-C-C-C-B-C-B-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-S-C-C-C-C-S-S-S-S-S-S-S-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-B-C-C-C-C-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-C-C-C-C-C-C-