November 14, 2025 Dataset Open Access

Chekhonin, Paul; Korten, Till; Gerçek, Alinda Ezgi; Hassan, Maleeha; Steinbach, Peter

Dublin Core Export

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Chekhonin, Paul</dc:creator>
  <dc:creator>Korten, Till</dc:creator>
  <dc:creator>Gerçek, Alinda Ezgi</dc:creator>
  <dc:creator>Hassan, Maleeha</dc:creator>
  <dc:creator>Steinbach, Peter</dc:creator>
  <dc:date>2025-11-14</dc:date>
  <dc:description>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</dc:description>
  <dc:identifier>https://rodare.hzdr.de/record/4124</dc:identifier>
  <dc:identifier>10.14278/rodare.4124</dc:identifier>
  <dc:identifier>oai:rodare.hzdr.de:4124</dc:identifier>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-42225</dc:relation>
  <dc:relation>doi:10.14278/rodare.4123</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/rodare</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>machine learning</dc:subject>
  <dc:subject>SEM</dc:subject>
  <dc:subject>steel</dc:subject>
  <dc:subject>carbide</dc:subject>
  <dc:subject>segmentation</dc:subject>
  <dc:subject>image processing</dc:subject>
  <dc:title>Training Data and Models for the paper: Data-efficient U-Net for Segmentation of Carbide Microstructures in SEM Images of Steel Alloys</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>