Tumor Segmentation Based on Deeply Supervised MultiScale UNet Although deep learning has achieved great success in the field of medical image processing, the existing deep learning based medical image segmentation solutions still cannot obtain satisfactory performances for abdominal small organs and lesions due to their small object size and shapevariability. In this work, a Deeply Supervised MultiScale UNet (DSMS UNet) is proposed for more accurate segmentation performances on abdominal small organs images. DSMS UNet integrate the existing UNet model with a restoration decoder module and some multiscale convolution modules. Our experiment results demonstrate that the proposed DSMS UNet approach has much better segmentation performances than the stateoftheart baselines.

An Enhanced UNet for Brain Tumor Segmentation The Convolutional Neural Network (CNN) has been in rapid development these years. It is applied to many fields. Among these, brain tumor segmentation is one of foci. Researchers delves into this with different ways. Some researchers use 3D CNN to handle the brain images for tumor segmentation, and others use 2D CNN modified from models of natural images in semantic segmentation to process separate slices of brain images. Based on properties of brain images and the inherent contradiction of semantic segmentation, we chose 2D UNet as basic model and modified the skip connection in it to propose our model. To verify effect of our model, we introduced our brain image dataset and made experiments on it and BraTS 2017 dataset. Experiments shows that proposed model outperforms the original UNet.