PSDI & Royce Materials Data Summit

Automated nanoparticle segmentation of TEM images and generation of synthetic databases with TEMPOS

29 Jul 2026, 15:00

3h

2A.034

Tutorial (invited) Tutorial Room 1 Tutorials

Description

This hands-on tutorial introduces TEMPOS, an open-source platform for automated nanoparticle segmentation and morphometric analysis in TEM images. Participants will learn how to install and run TEMPOS, apply the pre-trained Mask R-CNN model to their own micrographs, interpret per-particle outputs (size, shape descriptors, spatial distributions), and fine-tune the model on a small custom dataset. The session combines short conceptual segments with guided live demonstrations and self-paced exercises, and is designed to leave attendees with a working installation, sample notebooks, and a clear path to integrating TEMPOS into their own analysis workflows.

Learning outcomes

By the end of the tutorial, participants will be able to:

1. Explain, at a conceptual level, how instance-segmentation networks (Mask R-CNN) differ from semantic segmentation and classical thresholding, and why this matters for touching or overlapping nanoparticles.
2. Install and configure TEMPOS in a reproducible Python environment (conda or container) and verify the installation against a reference dataset.
3. Run inference on TEM micrographs using the pre-trained model and adjust key parameters (confidence threshold, NMS, tile size) for their own image conditions.
4. Interpret TEMPOS output: extract per-particle masks, equivalent diameters, aspect ratios, and population-level distributions, and export results to standard formats (CSV, JSON, OME-TIFF).
5. Annotate a small dataset and fine-tune the model on a domain-specific particle type, evaluating performance with appropriate metrics (mAP, IoU).
6. Identify common failure modes (low contrast, beam damage artefacts, agglomeration) and apply targeted mitigations.
7. Locate documentation, the GitHub repository, and community resources for ongoing use.

Tutorial Structure

Please note that breaks are included in this 3-hour tutorial

Part 1 — Context and theory

• Motivation: bottlenecks in manual nanoparticle analysis, reproducibility, the autonomous-TEM context.
• Instance segmentation in a nutshell: bounding boxes, masks, Mask R-CNN architecture without the maths.
• What TEMPOS is and what it isn't: scope, supported imaging modes, current limitations.

Part 2 — Installation and first run

• Walk-through of installation via pip/conda and the containerised release; verifying the environment.
• Guided inference on a provided reference micrograph; reading the output structure.

Part 3 — Hands-on inference and analysis

• Participants run TEMPOS on a provided dataset (or, optionally, their own)
• Parameter sweeps: confidence threshold, tile size, overlap; effects on recall and precision.
• Extracting morphometrics, plotting size distributions, exporting for downstream analysis.

Part 4 — Fine-tuning on custom data

• Annotation workflow with a lightweight labelling tool; what makes a good training set.
• Launching a fine-tuning run on a small pre-annotated dataset; monitoring training.
• Evaluating the fine-tuned model and comparing against the baseline.

Part 5 — Failure modes, integration, and Q&A

• Common pitfalls and how to diagnose them; integration sketches (Jupyter, scripted pipelines, autonomous loops).
• Resources, roadmap, and Q&A.