# Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey

arXiv: https://arxiv.org/abs/2001.04074 · HTML: https://mlsanity.com/paper/2001.04074/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2020.

## Built on by (most cited first, 28 of 28)

- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [A Review on Deep Learning in UAV Remote Sensing](https://mlsanity.com/paper/2101.10861/) (2021)
- [GEMEL: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge](https://mlsanity.com/paper/2201.07705/) (2022)
- [MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR Images](https://mlsanity.com/paper/2211.03062/) (2022)
- [Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2405.12003/) (2024)
- [Using GANs to Augment Data for Cloud Image Segmentation Task](https://mlsanity.com/paper/2106.03064/) (2021)
- [Deep Learning based Novel Cascaded Approach for Skin Lesion Analysis](https://mlsanity.com/paper/2301.06226/) (2023)
- [Machine learning applications in archaeological practices: a review](https://mlsanity.com/paper/2501.03840/) (2025)
- [Region-Aware CAM: High-Resolution Weakly-Supervised Defect Segmentation via Salient Region Perception](https://mlsanity.com/paper/2506.22866/) (2025)
- [Changes in Gaza: DINOv3-Powered Multi-Class Change Detection for Damage Assessment in Conflict Zones](https://mlsanity.com/paper/2511.19035/) (2025)
- [Multi-modal Scene-compliant User Intention Estimation in Navigation](https://mlsanity.com/paper/2106.06920/) (2021)
- [Hoechst Is All You Need: Lymphocyte Classification with Deep Learning](https://mlsanity.com/paper/2107.04388/) (2021)
- [Group channel pruning and spatial attention distilling for object detection](https://mlsanity.com/paper/2306.01526/) (2023)
- [Enhancing Rock Image Segmentation in Digital Rock Physics: A Fusion of Generative AI and State-of-the-Art Neural Networks](https://mlsanity.com/paper/2311.06079/) (2023)
- [ChannelExplorer: Exploring Class Separability Through Activation Channel Visualization](https://mlsanity.com/paper/2505.04647/) (2025)
- [Window Detection In Facade Imagery: A Deep Learning Approach Using Mask R-CNN](https://mlsanity.com/paper/2107.10006/) (2021)
- [ConAM: Confidence Attention Module for Convolutional Neural Networks](https://mlsanity.com/paper/2110.14369/) (2021)
- [HDAM: Heuristic Difference Attention Module for Convolutional Neural Networks](https://mlsanity.com/paper/2202.09556/) (2022)
- [Efficient textual explanations for complex road and traffic scenarios based on semantic segmentation](https://mlsanity.com/paper/2205.14118/) (2022)
- [ReMark: Receptive Field based Spatial WaterMark Embedding Optimization using Deep Network](https://mlsanity.com/paper/2305.06786/) (2023)
- [Ambulance Demand Prediction via Convolutional Neural Networks](https://mlsanity.com/paper/2306.04994/) (2023)
- [FolkTalent: Enhancing Classification and Tagging of Indian Folk Paintings](https://mlsanity.com/paper/2405.08776/) (2024)
- [Modifying the U-Net's Encoder-Decoder Architecture for Segmentation of Tumors in Breast Ultrasound Images](https://mlsanity.com/paper/2409.00647/) (2024)
- [Convex Distillation: Efficient Compression of Deep Networks via Convex Optimization](https://mlsanity.com/paper/2410.06567/) (2024)
- [Breccia and basalt classification of thin sections of Apollo rocks with deep learning](https://mlsanity.com/paper/2410.21024/) (2024)
- [Artificial Intelligence and Deep Learning Algorithms for Epigenetic Sequence Analysis: A Review for Epigeneticists and AI Experts](https://mlsanity.com/paper/2504.03733/) (2025)
- [Med-SegLens: Latent-Level Model Diffing for Interpretable Medical Image Segmentation](https://mlsanity.com/paper/2602.10508/) (2026)
- [A Benchmark Study of Neural Network Compression Methods for Hyperspectral Image Classification](https://mlsanity.com/paper/2603.04720/) (2026)

## Abstract (arXiv)

From the autonomous car driving to medical diagnosis, the requirement of the task of image segmentation is everywhere. Segmentation of an image is one of the indispensable tasks in computer vision. This task is comparatively complicated than other vision tasks as it needs low-level spatial information. Basically, image segmentation can be of two types: semantic segmentation and instance segmentation. The combined version of these two basic tasks is known as panoptic segmentation. In the recent era, the success of deep convolutional neural networks (CNN) has influenced the field of segmentation greatly and gave us various successful models to date. In this survey, we are going to take a glance at the evolution of both semantic and instance segmentation work based on CNN. We have also specified comparative architectural details of some state-of-the-art models and discuss their training details to present a lucid understanding of hyper-parameter tuning of those models. We have also drawn a comparison among the performance of those models on different datasets. Lastly, we have given a glimpse of some state-of-the-art panoptic segmentation models.