# UA-Net: Uncertainty-Aware Network for TRISO Image Semantic Segmentation

Source: [UA-Net: Uncertainty-Aware Network for TRISO Image Semantic Segmentation](<https://www.sota2.com/research/paper/69e5b30d7d0172140ec7dfc9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-21T05:00:04.784Z

Authors: Kyle Lucke, Zuzanna Krajewska-Travar, Shoukun Sun, Lu Cai, John D. Stempien, Min Xian

Published: 2026-04-16T21:48:20.000Z

## Abstract

Tristructural isotropic (TRISO)-coated particle fuels undergo dimensional changes and chemical reactions during high-temperature neutron irradiation. Post-irradiation materialography helps understand processes that impact fuel performance, such as coating integrity and fission product retention. Conventionally, experts manually evaluate features in thousands of cross sections of sub-mm-sized samples, which is tedious and subjective. In this work, we propose UA-Net, a deep learning framework that segments five characteristic regions of TRISO fuel micrographs and generates an uncertainty map for predictions. The model uses a multi-stage pretraining strategy, starting with general image representations learned from ImageNet, followed by fine-tuning on TRISO micrographs from various irradiation experiments and AGR-5/6/7 particle cross sections. A meta-model for uncertainty prediction is integrated to identify small defects in TRISO images. UA-Net was evaluated on a test set of 102 images, achieving mean Intersection over Union (mIoU) and mean Precision (mP) of 95.5% and 97.3%, respectively. The meta-model achieved a specificity of 91.8% and sensitivity of 93.5%, demonstrating strong performance in detecting misclassifications. The model was also applied to new TRISO images for qualitative evaluation, showing high accuracy in extracting layer regions.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Semantic Segmentation on AGR 5/6/7](<https://www.sota2.com/research/sota/semantic-segmentation-on-agr-5-6-7.md>): Semantic segmentation; AGR 5/6/7; IoU (BG): 97.2.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.15542>)

[PDF](<https://arxiv.org/pdf/2604.15542.pdf>)