# ParticleSeg3D: A Scalable Out-of-the-Box Deep Learning Segmentation Solution for Individual Particle Characterization from Micro CT Images in Mineral Processing and Recycling

arXiv: https://arxiv.org/abs/2301.13319 · HTML: https://mlsanity.com/paper/2301.13319/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 45 references that are in the map.
- First posted on arXiv 30 Jan 2023.

## Builds on (most cited first, 5 of 5)

- [DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation](https://mlsanity.com/paper/1707.00652/) (2017)
- [Deep Learning Based Instance Segmentation in 3D Biomedical Images Using Weak Annotation](https://mlsanity.com/paper/1806.11137/) (2018)
- [VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning](https://mlsanity.com/paper/2106.11480/) (2021)
- [Lung Nodules Detection and Segmentation Using 3D Mask-RCNN](https://mlsanity.com/paper/1907.07676/) (2019)
- [Deep-XFCT: Deep learning 3D-mineral liberation analysis with micro X-ray fluorescence and computed tomography](https://mlsanity.com/paper/2205.13102/) (2022)

## Built on by (most cited first, 3 of 3)

- [nnInteractive: Redefining 3D Promptable Segmentation](https://mlsanity.com/paper/2503.08373/) (2025)
- [ARI3D: A Software for Interactive Quantification of Regions in X-Ray CT 3D Images](https://mlsanity.com/paper/2508.09849/) (2025)
- [Self-Validated Learning for Particle Separation: A Correctness-Based Self-Training Framework Without Human Labels](https://mlsanity.com/paper/2508.16224/) (2025)

## Abstract (arXiv)

Minerals, metals, and plastics are indispensable for a functioning modern society. Yet, their supply is limited causing a need for optimizing ore extraction and recuperation from recyclable materials.Typically, those processes must be meticulously adapted to the precise properties of the processed materials. Advancing our understanding of these materials is thus vital and can be achieved by crushing them into particles of micrometer size followed by their characterization. Current imaging approaches perform this analysis based on segmentation and characterization of particles imaged with computed tomography (CT), and rely on rudimentary postprocessing techniques to separate touching particles. However, their inability to reliably perform this separation as well as the need to retrain methods for each new image, these approaches leave untapped potential to be leveraged. Here, we propose ParticleSeg3D, an instance segmentation method able to extract individual particles from large CT images of particle samples containing different materials. Our approach is based on the powerful nnU-Net framework, introduces a particle size normalization, uses a border-core representation to enable instance segmentation, and is trained with a large dataset containing particles of numerous different sizes, shapes, and compositions of various materials. We demonstrate that ParticleSeg3D can be applied out-of-the-box to a large variety of particle types, including materials and appearances that have not been part of the training set. Thus, no further manual annotations and retraining are required when applying the method to new particle samples, enabling substantially higher scalability of experiments than existing methods. Our code and dataset are made publicly available.