X-ray computed tomography is lately becoming an increasingly popular method in developmental biology. A key step in analysis of image data is image segmentation. This segmentation is in many cases done manually, because automatic segmentation algorithms are not able to achieve the accuracy necessary for further analysis. The state-of-the-art in image segmentation is achived by deep learning algorithms. In this project, a deep learning based segmentation solution will be developed for segmentation of soft tissues in x-ray computed tomography images and implemented in Avizo software.

CEITEC VUT-J-20-6477

Czech

Matula Jan, Ing. - principal person responsible
Kaiser Jozef, prof. Ing., Ph.D. - fellow researcher

Advanced instrumentation and methods for material characterization
- responsible department (5.3.2020 - not assigned)
Advanced instrumentation and methods for material characterization
- responsible department (1.1.2020 - 31.12.2020)
Central European Institute of Technology BUT
- beneficiary (1.1.2020 - 31.12.2020)

MATULA, J.; POLÁKOVÁ, V.; ŠALPLACHTA, J.; TESAŘOVÁ, M.; ZIKMUND, T.; KAUCKÁ, M.; ADAMEYKO, I.; KAISER, J. Resolving complex cartilage structures in developmental biology via deep learning-based automatic segmentation of X-ray computed microtomography images. Scientific Reports, 2022, vol. 12, no. 1, p. 1-13. ISSN: 2045-2322.
Detail

MATULA, J.; TESAŘOVÁ, M.; ZIKMUND, T.; KAUCKÁ, M.; ADAMEYKO, I.; KAISER, J. X-ray microtomography–based atlas of mouse cranial development. GigaScience, 2021, vol. 10, no. 3, p. 1-6. ISSN: 2047-217X.
Detail

Responsibility: Matula Jan, Ing.