Segment SEM images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F24%3A10256099" target="_blank" >RIV/61989100:27740/24:10256099 - isvavai.cz</a>

Výsledek na webu

<a href="https://code.it4i.cz/SEM-Image/segment_sem_images_hctpm.git" target="_blank" >https://code.it4i.cz/SEM-Image/segment_sem_images_hctpm.git</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Segment SEM images

Popis výsledku v původním jazyce

The study that is supported by the provided software demonstrated that the deep learning model can accurately segment SEM (scanning electron microscope) images, achieving high dice scores for coating layers, pores, and precipitates. The result highlights the significance of the Blender-generated synthetic data in training the deep learning model. The use of blend files for creating realistic synthetic SEM images was pivotal in enhancing segmentation accuracy, particularly for limited real data, showcasing Blender&apos;s value in data augmentation for scientific imaging tasks. This approach uniquely combines synthetic data augmentation and HPC resources to address challenges in SEM image segmentation with limited real-world data.

Název v anglickém jazyce

Segment SEM images

Popis výsledku anglicky

The study that is supported by the provided software demonstrated that the deep learning model can accurately segment SEM (scanning electron microscope) images, achieving high dice scores for coating layers, pores, and precipitates. The result highlights the significance of the Blender-generated synthetic data in training the deep learning model. The use of blend files for creating realistic synthetic SEM images was pivotal in enhancing segmentation accuracy, particularly for limited real data, showcasing Blender&apos;s value in data augmentation for scientific imaging tasks. This approach uniquely combines synthetic data augmentation and HPC resources to address challenges in SEM image segmentation with limited real-world data.

Druh

R - Software

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

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Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Interní identifikační kód produktu

023/13-12-2024_SW

Technické parametry

The software performs segmentation of SEM images, identifying coating layers (e.g., Fe2Al5, FeAl), pores, and chromium precipitates using a U-Net-based deep learning model. The Blender component facilitates synthetic data augmentation via blend files, crucial for enhancing model training. The synthetic data was rendered on Karolina cluster. Large amount of synthetic data can be generated by rendering the blend files using the computational resources on HPC systems.

Ekonomické parametry

Neaplikovatelné

IČO vlastníka výsledku

61989100

Název vlastníka

VŠB - Technická univerzita Ostrava