Gossamer: Scaling Image Processing and Reconstruction to Whole Brains

Marrett, K.; Moradi, K.; Park, C. S.; Yan, M.; Choi, C.; Zhu, M.; Akram, M.; Nanda, S.; Xue, Q.; Mun, H.-S.; Gutierez, A.; Rudd, M.; Zingg, B.; Magat, G.; Wijaya, K.; Dong, H.; Yang, X. W.; Cong, J.

doi:10.1101/2024.04.07.588466

bioRxiv · 10.1101/2024.04.07.588466

Gossamer: Scaling Image Processing and Reconstruction to Whole Brains

Marrett, K.·Moradi, K.·Park, C. S.·Yan, M.·Choi, C.·Zhu, M.·Akram, M.·Nanda, S.·Xue, Q.·Mun, H.-S.·Gutierez, A.·Rudd, M.·Zingg, B.·Magat, G.·Wijaya, K.·Dong, H.·Yang, X. W.·Cong, J.

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Abstract

Neuronal reconstruction-a process that transforms image volumes into 3D geometries and skeletons of cells- bottlenecks the study of brain function, connectomics and pathology. Domain scientists need exact and complete segmentations to study subtle topological differences. Existing methods are diskbound, dense-access, coupled, single-threaded, algorithmically unscalable and require manual cropping of small windows and proofreading of skeletons due to low topological accuracy. Designing a data-intensive parallel solution suited to a neurons shape, topology and far-ranging connectivity is particularly challenging due to I/O and load-balance, yet by abstracting these vision tasks into strategically ordered specializations of search, we progressively lower memory by 4 orders of magnitude. This enables 1 mouse brain to be fully processed in-memory on a single server, at 67x the scale with 870x less memory while having 78% higher automated yield than APP2, the previous state of the art in performant reconstruction.

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Marrett, K., Moradi, K., Park, C. S., Yan, M., Choi, C., Zhu, M., Akram, M., Nanda, S., Xue, Q., Mun, H.-S., Gutierez, A., Rudd, M., Zingg, B., Magat, G., Wijaya, K., Dong, H., Yang, X. W., Cong, J.. 2024-04-09. Gossamer: Scaling Image Processing and Reconstruction to Whole Brains. https://doi.org/10.1101/2024.04.07.588466

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