1 INTRODUCTION

2 OVERVIEW OF DEEP NEURAL NETWORKS  
2.1 Convolutional Neural Networks (CNNs)  
2.2 Recurrent Neural Networks (RNNs) and the LSTM  
2.3 Encoder-Decoder and Auto-Encoder Models  
2.4 Generative Adversarial Networks (GANs)  

3 DL-BASED IMAGE SEGMENTATION MODELS  
3.1 Fully Convolutional Networks  
3.2 Convolutional Models With Graphical Models  
3.3 Encoder-Decoder Based Models  
3.4 Multi-Scale and Pyramid Network Based Models  
3.5 R-CNN Based Models (for Instance Segmentation)  
3.6 Dilated Convolutional Models and DeepLab Family  
3.7 Recurrent Neural Network Based Models  
3.8 Attention-Based Models  
3.9 Generative Models and Adversarial Training  
3.10 CNN Models With Active Contour Models  
3.11 Other Models  

4 IMAGE SEGMENTATION DATASETS  
4.1 2D Datasets  
4.2 2.5D Datasets  
4.3 3D Datasets  

5 PERFORMANCE REVIEW  
5.1 Metrics For Segmentation Models  
5.2 Quantitative Performance of DL-Based Models  

6 CHALLENGES AND OPPORTUNITIES  
6.1 More Challenging Datasets  
6.2 Interpretable Deep Models  
6.3 Weakly-Supervised and Unsupervised Learning  
6.4 Real-time Models for Various Applications  
6.5 Memory Efficient Models  
6.6 3D Point-Cloud Segmentation  
6.7 Application Scenarios  

7 CONCLUSIONS