  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 to biomedical image segmentation applications.
In this paper, we propose three different multi-scale dense connections (MDC)
for the encoder, the decoder of U-shaped architectures, and across them. Based
on three dense connections, we propose a multi-scale densely connected U-Net
(MDU-Net) for biomedical image segmentation. MDU-Net directly fuses the
neighboring feature maps with different scales from both higher layers and
lower layers to strengthen feature propagation in the current layer.
Multi-scale dense connections, which contain shorter connections between layers
close to the input and output, also make a much deeper U-Net possible. Besides,
we introduce quantization to alleviate the potential overfitting in dense
connections, and further improve the segmentation performance. We evaluate our
proposed model on the MICCAI 2015 Gland Segmentation (GlaS) dataset. The three
MDC 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 obviously
improves the segmentation performance of original U-Net.
