Training Data and Models for the paper: Data-efficient U-Net for Segmentation of Carbide Microstructures in SEM Images of Steel Alloys

DOI

This dataset contains scanning electron microscopy (SEM) images of steel alloys, including paired secondary electron (SE2) and in-lens (InLens) channels, with corresponding binary segmentation labels. The data supports full reproduction of results presented in the referenced manuscript.

 

Dataset Description

Content: 13 pairs of SEM images of two reactor pressure vessel (RPV) steels:


    JFL: IAEA reference RPV base metal steel
    ANP-10: Western type RPV steel


Acquisition:

    JFL: Zeiss NVision 40 microscope
    ANP-10: Zeiss Ultra 55 microscope
    Both SE and InLens detectors used simultaneously.


Resolution: 2048 × 1404 pixels per image

    2048 px width corresponds to 14.3 µm (JFL) or 11.5 µm (ANP-10).

Using the dataset to reproduce the results of the manuscript

Download the zip file into the data/ subdirectory of the code repository and extract the archive:

cd data/ unzip data.zip

Dataset Structure

These directories contain the relevant data for the manuscript:

cloud/ ├-─ preprocessed/ │   ├── hold-out/ │   ├── images/ │   └── labels/ ├── processed_tiles/ │   ├── images/ │   └── labels/ ├── tb_logs/ │   ├── unet_model/

Preprocessed

pre-processed whole images and corresponding labels

Processed Tiles

tiled images and labels

tb_logs

trained model weights

Identifier
DOI https://doi.org/10.14278/rodare.4124
Related Identifier IsIdenticalTo https://www.hzdr.de/publications/Publ-42225
Related Identifier IsPartOf https://doi.org/10.14278/rodare.4123
Related Identifier IsPartOf https://rodare.hzdr.de/communities/rodare
Metadata Access https://rodare.hzdr.de/oai2d?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:rodare.hzdr.de:4124
Provenance
Creator Chekhonin, Paul ORCID logo; Korten, Till ORCID logo; Gerçek, Alinda Ezgi; Hassan, Maleeha ORCID logo; Steinbach, Peter (ORCID: 0000-0002-4974-230X)
Publisher Rodare
Publication Year 2025
Rights Creative Commons Attribution 4.0 International; Open Access; https://creativecommons.org/licenses/by/4.0/legalcode; info:eu-repo/semantics/openAccess
OpenAccess true
Contact https://rodare.hzdr.de/support
Representation
Resource Type Dataset
Discipline Life Sciences; Natural Sciences; Engineering Sciences