# Understanding Deep Learning Techniques for Image Segmentation

arXiv: https://arxiv.org/abs/1907.06119 · HTML: https://mlsanity.com/paper/1907.06119/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2019.

## Built on by (most cited first, 30 of 38)

- [Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations](https://mlsanity.com/paper/2103.10481/) (2021)
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- [Deep transfer learning for image classification: a survey](https://mlsanity.com/paper/2205.09904/) (2022)
- [Deep Image Matting: A Comprehensive Survey](https://mlsanity.com/paper/2304.04672/) (2023)
- [Label-free segmentation from cardiac ultrasound using self-supervised learning](https://mlsanity.com/paper/2210.04979/) (2022)
- [Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey](https://mlsanity.com/paper/2206.07171/) (2022)
- [Deep Learning in Concealed Dense Prediction](https://mlsanity.com/paper/2504.10979/) (2025)
- [Optimizing Edge Detection for Image Segmentation with Multicut Penalties](https://mlsanity.com/paper/2112.05416/) (2021)
- [Large-batch Optimization for Dense Visual Predictions](https://mlsanity.com/paper/2210.11078/) (2022)
- [Image-free single-pixel segmentation](https://mlsanity.com/paper/2108.10617/) (2021)
- [Synthetic Data for Semantic Image Segmentation of Imagery of Unmanned Spacecraft](https://mlsanity.com/paper/2211.11941/) (2022)
- [A Survey on Multi-Objective based Parameter Optimization for Deep Learning](https://mlsanity.com/paper/2305.10014/) (2023)
- [Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images](https://mlsanity.com/paper/2403.15443/) (2024)
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- [The SAM2-to-SAM3 Gap in the Segment Anything Model Family: Why Prompt-Based Expertise Fails in Concept-Driven Image Segmentation](https://mlsanity.com/paper/2512.06032/) (2025)
- [From Data to Insights: A Comprehensive Survey on Advanced Applications in Thyroid Cancer Research](https://mlsanity.com/paper/2401.03722/) (2024)
- [CS3: Cascade SAM for Sperm Segmentation](https://mlsanity.com/paper/2407.03772/) (2024)
- [Probabilistic U-Net with Kendall Shape Spaces for Geometry-Aware Segmentations of Images](https://mlsanity.com/paper/2410.14017/) (2024)
- [MaskAttn-UNet: A Mask Attention-Driven Framework for Universal Low-Resolution Image Segmentation](https://mlsanity.com/paper/2503.10686/) (2025)
- [Knowledge Consultation for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2503.10693/) (2025)
- [Modern Machine and Deep Learning Systems as a way to achieve Man-Computer Symbiosis](https://mlsanity.com/paper/2101.10534/) (2021)
- [High-Resolution Segmentation of Tooth Root Fuzzy Edge Based on Polynomial Curve Fitting with Landmark Detection](https://mlsanity.com/paper/2103.04258/) (2021)
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- [GuideBP: Guiding Backpropagation Through Weaker Pathways of Parallel Logits](https://mlsanity.com/paper/2104.11620/) (2021)
- [Wound Segmentation with Dynamic Illumination Correction and Dual-view Semantic Fusion](https://mlsanity.com/paper/2207.05388/) (2022)
- [Instance Attack:An Explanation-based Vulnerability Analysis Framework Against DNNs for Malware Detection](https://mlsanity.com/paper/2209.02453/) (2022)

## Abstract (arXiv)

The machine learning community has been overwhelmed by a plethora of deep learning based approaches. Many challenging computer vision tasks such as detection, localization, recognition and segmentation of objects in unconstrained environment are being efficiently addressed by various types of deep neural networks like convolutional neural networks, recurrent networks, adversarial networks, autoencoders and so on. While there have been plenty of analytical studies regarding the object detection or recognition domain, many new deep learning techniques have surfaced with respect to image segmentation techniques. This paper approaches these various deep learning techniques of image segmentation from an analytical perspective. The main goal of this work is to provide an intuitive understanding of the major techniques that has made significant contribution to the image segmentation domain. Starting from some of the traditional image segmentation approaches, the paper progresses describing the effect deep learning had on the image segmentation domain. Thereafter, most of the major segmentation algorithms have been logically categorized with paragraphs dedicated to their unique contribution. With an ample amount of intuitive explanations, the reader is expected to have an improved ability to visualize the internal dynamics of these processes.