2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDBIO-3.6
Paper Title MULTI-SCALE MODELING OF NEURAL STRUCTURE IN X-RAY IMAGERY
Authors Aishwarya Balwani, Joseph Miano, Ran Liu, Georgia Institute of Technology, United States; Lindsey Kitchell, Johns Hopkins University Applied Physics Laboratory, United States; Judy A. Prasad, University of North Carolina at Chapel Hill, United States; Erik C. Johnson, William Gray-Roncal, Johns Hopkins University Applied Physics Laboratory, United States; Eva L. Dyer, Georgia Institute of Technology / Emory University, United States
SessionBIO-3: Biomedical Signal Processing 3
LocationArea C
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Biomedical Signal Processing: Biological image analysis
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Abstract Methods for resolving the brain’s microstructure are rapidly improving, allowing us to image large brain volumes at high resolutions. As a result, the interrogation of samples spanning multiple diversified brain regions is becoming increasingly common. Understanding these samples often requires multi-scale processing: segmentation of the detailed microstructure and large-scale modelling of the macrostructure. Current brain mapping algorithms often analyze data only at a single scale, and optimization for each scale occurs independently, potentially limiting the consistency, performance, and interpretability. In this work we introduce a deep learning framework for segmentation of brain structure at multiple scales. We leverage a modified U-Net architecture with a multi-task learning objective and unsupervised pre-training to simultaneously model both the micro and macro architecture of the brain. We successfully apply our methods to a heterogeneous, three-dimensional, X-ray micro-CT dataset spanning multiple regions in the mouse brain, and show that our approach consistently outperforms another multi-task architecture, and is competitive with strong single-task baselines at both scales.