An Improved Neural Segmentation Method Based on U-NET

TL;DR

This paper introduces an enhanced U-NET neural network for segmentation that incorporates residual connections, resulting in faster training, fewer parameters, and improved segmentation accuracy for neural applications.

Contribution

The paper proposes a novel deepening of U-NET with residual networks, achieving better performance and efficiency over existing segmentation models.

Findings

Fewer training parameters than SegNet

Shorter training time than SegNet

Significant improvement in segmentation accuracy

Abstract

Neural segmentation has a great impact on the smooth implementation of local anesthesia surgery. At present, the network for the segmentation includes U-NET [1] and SegNet [2]. U-NET network has short training time and less training parameters, but the depth is not deep enough. SegNet network has deeper structure, but it needs longer training time, and more training samples. In this paper, we propose an improved U-NET neural network for the segmentation. This network deepens the original structure through importing residual network. Compared with U-NET and SegNet, the improved U-NET network has fewer training parameters, shorter training time and get a great improvement in segmentation effect. The improved U-NET network structure has a good application scene in neural segmentation.