TY - GEN
T1 - Learning Probabilistic Piecewise Rigid Atlases of Model Organisms via Generative Deep Networks
AU - Nejatbakhsh, Amin
AU - Dey, Neel
AU - Venkatachalam, Vivek
AU - Yemini, Eviatar
AU - Paninski, Liam
AU - Varol, Erdem
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Atlases are crucial to imaging statistics as they enable the standardization of inter-subject and inter-population analyses. While existing atlas estimation methods based on fluid/elastic/diffusion registration yield high-quality results for the human brain, these deformation models do not extend to a variety of other challenging areas of neuroscience such as the anatomy of C. elegans worms and fruit flies. To this end, this work presents a general probabilistic deep network-based framework for atlas estimation and registration which can flexibly incorporate various deformation models and levels of keypoint supervision that can be applied to a wide class of model organisms. Of particular relevance, it also develops a deformable piecewise rigid atlas model which is regularized to preserve inter-observation distances between neighbors. These modeling considerations are shown to improve atlas construction and key-point alignment across a diversity of datasets with small sample sizes including neuron positions in C. elegans hermaphrodites, fluorescence microscopy of male C. elegans, and images of fruit fly wings. Code is accessible at https://github.com/amin-nejat/Deformable-Atlas.
AB - Atlases are crucial to imaging statistics as they enable the standardization of inter-subject and inter-population analyses. While existing atlas estimation methods based on fluid/elastic/diffusion registration yield high-quality results for the human brain, these deformation models do not extend to a variety of other challenging areas of neuroscience such as the anatomy of C. elegans worms and fruit flies. To this end, this work presents a general probabilistic deep network-based framework for atlas estimation and registration which can flexibly incorporate various deformation models and levels of keypoint supervision that can be applied to a wide class of model organisms. Of particular relevance, it also develops a deformable piecewise rigid atlas model which is regularized to preserve inter-observation distances between neighbors. These modeling considerations are shown to improve atlas construction and key-point alignment across a diversity of datasets with small sample sizes including neuron positions in C. elegans hermaphrodites, fluorescence microscopy of male C. elegans, and images of fruit fly wings. Code is accessible at https://github.com/amin-nejat/Deformable-Atlas.
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U2 - 10.1007/978-3-031-34048-2_26
DO - 10.1007/978-3-031-34048-2_26
M3 - Conference contribution
AN - SCOPUS:85163994243
SN - 9783031340475
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 332
EP - 343
BT - Information Processing in Medical Imaging - 28th International Conference, IPMI 2023, Proceedings
A2 - Frangi, Alejandro
A2 - de Bruijne, Marleen
A2 - Wassermann, Demian
A2 - Navab, Nassir
PB - Springer Science and Business Media Deutschland GmbH
T2 - 28th International Conference on Information Processing in Medical Imaging, IPMI 2023
Y2 - 18 June 2023 through 23 June 2023
ER -