U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger Philipp Fischer Thomas Brox
Abstract
There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.
中文速览
医学图像分割（biomedical image segmentation）长期面临标注数据极度匮乏的困境，传统滑动窗口卷积网络既慢又难以兼顾定位精度与上下文感知。本文提出了一种名为 U-Net 的全卷积网络，其核心设计是对称的"编码器-解码器"结构：左侧收缩路径逐步提取高层语义特征，右侧扩张路径通过上采样恢复分辨率，并利用跳跃连接将收缩路径的高分辨率特征直接拼接过来，从而同时实现精准定位与丰富上下文理解；配合随机弹性形变等强数据增强策略，网络仅凭极少量标注图像即可完成端到端训练。在 ISBI 神经元结构分割挑战和 2015 年细胞追踪挑战中，U-Net 均大幅超越此前最优方法，其中在 DIC-HeLa 细胞分割任务上交并比（IOU）从46%跃升至77.5%，且对一张512×512图像的推理仅需不到一秒。这项工作证明了在医学图像等数据稀缺场景下，精巧的网络结构与数据增强的组合可以替代海量标注，其简洁高效的设计使其迅速成为医学图像分割领域影响最深远的基础架构之一。
原文 arXiv:1505.04597;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1505.04597v1