# Deep-Learning-Based Image Segmentation Integrated with Optical Microscopy for Automatically Searching for Two-Dimensional Materials

arXiv: https://arxiv.org/abs/1910.12750 · HTML: https://mlsanity.com/paper/1910.12750/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2019.

## Built on by (most cited first, 17 of 17)

- [Two-Dimensional Quantum Material Identification via Self-Attention and Soft-labeling in Deep Learning](https://mlsanity.com/paper/2205.15948/) (2022)
- [SAMa: Material-aware 3D Selection and Segmentation](https://mlsanity.com/paper/2411.19322/) (2024)
- [MaskTerial: A Foundation Model for Automated 2D Material Flake Detection](https://mlsanity.com/paper/2412.09333/) (2024)
- [Zero-shot Autonomous Microscopy for Scalable and Intelligent Characterization of 2D Materials](https://mlsanity.com/paper/2504.10281/) (2025)
- [varphi-Adapt: A Physics-Informed Adaptation Learning Approach to 2D Quantum Material Discovery](https://mlsanity.com/paper/2507.05184/) (2025)
- [QuPAINT: Physics-Aware Instruction Tuning Approach to Quantum Material Discovery](https://mlsanity.com/paper/2602.17478/) (2026)
- [Machine Learning-based Automatic Graphene Detection with Color Correction for Optical Microscope Images](https://mlsanity.com/paper/2103.13495/) (2021)
- [Computer Vision Methods for the Microstructural Analysis of Materials: The State-of-the-art and Future Perspectives](https://mlsanity.com/paper/2208.04149/) (2022)
- [CLIFF: Continual Learning for Incremental Flake Features in 2D Material Identification](https://mlsanity.com/paper/2508.17261/) (2025)
- [Deep-learning Assisted Detection and Quantification of (oo)cysts of Giardia and Cryptosporidium on Smartphone Microscopy Images](https://mlsanity.com/paper/2304.05339/) (2023)
- [Transfer Learning for Microstructure Segmentation with CS-UNet: A Hybrid Algorithm with Transformer and CNN Encoders](https://mlsanity.com/paper/2308.13917/) (2023)
- [Rapid morphology characterization of two-dimensional TMDs and lateral heterostructures based on deep learning](https://mlsanity.com/paper/2503.00470/) (2025)
- [OpenQlaw: An Agentic AI Assistant for Analysis of 2D Quantum Materials](https://mlsanity.com/paper/2603.17043/) (2026)
- [Identification and classification of exfoliated graphene flakes from microscopy images using a hierarchical deep convolutional neural network](https://mlsanity.com/paper/2203.15252/) (2022)
- [Bellybutton: Accessible and Customizable Deep-Learning Image Segmentation](https://mlsanity.com/paper/2309.00058/) (2023)
- [TACoS: Weakly Supervised Learning of Two-Dimensional Materials from Scribble Annotations to Precise Segmentation](https://mlsanity.com/paper/2607.07169/) (2026)
- [QuPAINT: Physics-Aware Multimodal Reasoning for Quantum Material Characterization](https://mlsanity.com/paper/2609.12202/) (2026)

## Abstract (arXiv)

Deep-learning algorithms enable precise image recognition based on high-dimensional hierarchical image features. Here, we report the development and implementation of a deep-learning-based image segmentation algorithm in an autonomous robotic system to search for two-dimensional (2D) materials. We trained the neural network based on Mask-RCNN on annotated optical microscope images of 2D materials (graphene, hBN, MoS2, and WTe2). The inference algorithm is run on a 1024 x 1024 px2 optical microscope images for 200 ms, enabling the real-time detection of 2D materials. The detection process is robust against changes in the microscopy conditions, such as illumination and color balance, which obviates the parameter-tuning process required for conventional rule-based detection algorithms. Integrating the algorithm with a motorized optical microscope enables the automated searching and cataloging of 2D materials. This development will allow researchers to utilize unlimited amounts of 2D materials simply by exfoliating and running the automated searching process.