1、图像分割的历史

2000年之前，数字图像处理时我们采用方法基于几类：阈值分割、区域分割、边缘分割、纹理特征、聚类等。

2000年到2010年期间， 主要方法有四类：基于图论、聚类、分类以及聚类和分类结合。

2010年至今，神经网络模型的崛起和深度学习的发展，主要涉及到几种模型：

截至到2017年底，我们已经分化出了数以百计的模型结构。当然，经过从技术和原理上考究，我们发现了一个特点，那就是当前最成功的图像分割深度学习技术都是基于一个共同的先驱：FCN（Fully Convolutional Network，全卷积神经网络）。

发展历程

1. 2014年 FCN 模型，主要贡献为在语义分割问题中推广使用端对端卷积神经网络，使用反卷积进行上采样
2. 2015年 U-net 模型，构建了一套完整 的编码解码器
3. 2015年 SegNet 模型，将最大池化转换为解码器来提高分辨率
4. 2015年 Dilated Convolutions（空洞卷积），更广范围内提高了内容的聚合并不降低分辨率
5. 2016年 DeepLab v1&v2
6. 2016年 RefineNet 使用残差连接，降低了内存使用量，提高了模块间的特征融合
7. 2016年 PSPNet 模型
8. 2017年 Large Kernel Matters
9. 2017年 DeepLab V3

以上几种模型可以按照语义分割模型的独有方法进行分类，如专门池化（PSPNet、DeepLab），编码器-解码器架构（SegNet、E-Net），多尺度处理（DeepLab）、条件随机场（CRFRNN）、空洞卷积（DiatedNet、DeepLab）和跳跃连接（FCN）。

 2、UNet-Family

UNet-family

2015

• U-Net: Convolutional Networks for Biomedical Image Segmentation (MICCAI) [paper] [my-pytorch][keras]

2016

• V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation [paper] [caffe][pytorch]
• 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation [paper][pytorch]

2017

• H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes (IEEE Transactions on Medical Imaging)[paper][keras]
• GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network (MICCAI) [paper]

2018

• UNet++: A Nested U-Net Architecture for Medical Image Segmentation (MICCAI) [paper][my-pytorch][keras]
• MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation [paper]
• DUNet: A deformable network for retinal vessel segmentation [paper]
• RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans [paper]
• Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalities [paper]
• Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment [paper]
• Prostate Segmentation using 2D Bridged U-net [paper]
• nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation [paper][pytorch]
• SUNet: a deep learning architecture for acute stroke lesion segmentation and outcome prediction in multimodal MRI [paper]
• IVD-Net: Intervertebral disc localization and segmentation in MRI with a multi-modal UNet [paper]
• LADDERNET: Multi-Path Networks Based on U-Net for Medical Image Segmentation [paper][pytorch]
• Glioma Segmentation with Cascaded Unet [paper]
• Attention U-Net: Learning Where to Look for the Pancreas [paper]
• Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation [paper]
• Concurrent Spatial and Channel ‘Squeeze & Excitation’ in Fully Convolutional Networks [paper]
• A Probabilistic U-Net for Segmentation of Ambiguous Images (NIPS) [paper] [tensorflow]
• AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy [paper]
• 3D RoI-aware U-Net for Accurate and Efficient Colorectal Cancer Segmentation [paper][pytorch]
• Detection and Delineation of Acute Cerebral Infarct on DWI Using Weakly Supervised Machine Learning (Y-Net) (MICCAI) [paper](Page 82)
• Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal [paper]

2019

• MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation [paper][keras]
• U-NetPlus: A Modified Encoder-Decoder U-Net Architecture for Semantic and Instance Segmentation of Surgical Instrument [paper]
• Probability Map Guided Bi-directional Recurrent UNet for Pancreas Segmentation [paper]
• CE-Net: Context Encoder Network for 2D Medical Image Segmentation [paper][pytorch]
• Graph U-Net [paper]
• A Novel Focal Tversky Loss Function with Improved Attention U-Net for Lesion Segmentation (ISBI) [paper]
• ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling [paper]
• Connection Sensitive Attention U-NET for Accurate Retinal Vessel Segmentation [paper]
• CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation [paper]
• W-Net: Reinforced U-Net for Density Map Estimation [paper]
• Automated Segmentation of Pulmonary Lobes using Coordination-guided Deep Neural Networks (ISBI oral) [paper]
• U2-Net: A Bayesian U-Net Model with Epistemic Uncertainty Feedback for Photoreceptor Layer Segmentation in Pathological OCT Scans [paper]
• ScleraSegNet: an Improved U-Net Model with Attention for Accurate Sclera Segmentation (ICB Honorable Mention Paper Award) [paper]
• AHCNet: An Application of Attention Mechanism and Hybrid Connection for Liver Tumor Segmentation in CT Volumes [paper]
• A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities [paper]
• Recurrent U-Net for Resource-Constrained Segmentation [paper]
• MFP-Unet: A Novel Deep Learning Based Approach for Left Ventricle Segmentation in Echocardiography [paper]
• A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation (MICCAI 2019) [paper][pytorch]
• ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data [paper]
• A multi-task U-net for segmentation with lazy labels [paper]
• RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments [paper]
• 3D U2-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation (MICCAI 2019) [paper] [pytorch]
• SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation (MICCAI 2019) [paper]
• 3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI [paper][pytorch] (MICCAI 2019)
• The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN [paper]
• Recurrent U-Net for Resource-Constrained Segmentation [paper] (ICCV 2019)
• Siamese U-Net with Healthy Template for Accurate Segmentation of Intracranial Hemorrhage (MICCAI 2019)

reference

1、图像分割历史:http://www.sohu.com/a/270896638_633698 

2、UNet Family:https://github.com/ShawnBIT/UNet-family