Journal
DEVELOPMENTS IN X-RAY TOMOGRAPHY XIV
Volume 12242, Issue -, Pages -Publisher
SPIE-INT SOC OPTICAL ENGINEERING
DOI: 10.1117/12.2637973
Keywords
tomography; synchrotron radiation; deep learning; active learning; segmentation
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Funding
- Helmholtz Imaging Platform HIP (a platform of the Helmholtz Incubator on Information and Data Science)
- BMBF project Multi-task Deep Learning for Large-scale Multimodal Biomedical Image Analysis (MDLMA) (BMBF) [031L0202A, 031L0202C]
- Helmholtz AI project Universal Segmentation Framework (UniSeF)
- Project Holistic Data Analysis (HoliDAy) of the Innovation-, Information-& Biologisation-Fonds (I2B) of Hereon
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In this study, an active learning approach for semantic segmentation of tomography data using a guided and interactive framework is proposed, and different acquisition functions for selecting images to be annotated in the iterative process are evaluated.
The Helmholtz-Zentrum Hereon is operating several tomography end stations at the beamlines P05 and P07 of the synchrotron radiation facility PETRA III at DESY in Hamburg, Germany. Attenuation and phase contrast imaging techniques are provided as well as sample environments for in situ/operando/vivo experiments for applications in biology, medicine, materials science, etc. Very large and diverse data sets with varying spatiotemporal resolution, noise levels and artifacts are acquired which are challenging to process and analyze. Here we report on an active learning approach for the semantic segmentation of tomography data using a guided and interactive framework, and evaluate different acquistion functions for the selection of images to be annotated in the iterative process.
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