1. 论文标题：Image Segmentation Using Deep Learning:A Survey
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4. 阅读日期 ：
5. 研究背景：scene understanding,medical image analysis, robotic perception, video surveillance, augmented reality, and image compression
6. 方法和性质：
   fully convolutional pixel-labeling networks,encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generativemodels in adversarial settings.

1. Fully convolutional networks
2. Convolutional models with graphical models
3. Encoder-decoder based models
4. Multi-scale and pyramid network based models5) R-CNN based models (for instance segmentation)6) Dilated convolutional models and DeepLab family7) Recurrent neural network based models8) Attention-based models
5. Generative models and adversarial training
6. Convolutional models with active contour models
7. Other models
   

7. 研究结果：
8. 创新点：
9. 数据：
10. 结论：
11. 挑战：
12. 研究展望：
13. 重要性：
14. 写作方法：

• 主要讲各个方法细节，之后再了解