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Abstract #3808

Open-Source Hypothalamic-ForniX (OSHy-X) Atlases and Segmentation Tool for 3T and 7T

Jeryn Chang1, Frederik Steyn1,2,3,4, Shyuan Ngo2,3,4,5, Robert Henderson2,3,4, Christine Guo6, Steffen Bollmann7,8, Jurgen Fripp9, Markus Barth7,8, and Thomas B Shaw2,7,8
1School of Biomedical Sciences, The University of Queensland, St Lucia, Australia, 2Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, Australia, 3Wesley Medical Research, The Wesley Hospital, Brisbane, Australia, 4Centre for Clinical Research, The University of Queensland, Brisbane, Australia, 5Australian Institute of Bioengineering and Nanotechnology, The University of Queensland, St Lucia, Australia, 6QIMR Berghofer Medical Research Institute, Brisbane, Australia, 7School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Australia, 8Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia, 9Health and Biosecurity, The Commonwealth Scientific and Industrial Research Organisation (CSIRO), Brisbane, Australia


Segmentation and volumetric analysis of the hypothalamus and fornix plays a critical role in improving the understanding of degenerative processes that might impact the function of these structures. We present Open-Source Hypothalamic-ForniX (OSHy-X) atlases and tool for multi-atlas fusion segmentation for 3T and 7T. The atlases are based on 20 manual segmentations, which we demonstrate have high interrater agreement. The versatility of the OSHy-X tool allows segmentation and volumetric analysis for different field strengths and contrasts. We also demonstrate that OSHy-X segmentation has higher Dice overlaps (3T and 7T inputs: p<0005, p<0.005) than a deep-learning segmentation method for the hypothalamus.

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