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| Item Type: | Preprint |
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| Title: | PatchPerPixMatch for automated 3d search of neuronal morphologies in light microscopy |
| Creators Name: | Mais, L., Hirsch, P., Managan, C., Wang, K., Rokicki, K., Svirskas, R.R., Dickson, B.J., Korff, W., Rubin, G.M., Ihrke, G., Meissner, G.W. and Kainmueller, D. |
| Abstract: | Studies of individual neurons in the Drosophila nervous system are facilitated by transgenic lines that sparsely and repeatably label respective neurons of interest. Sparsity can be enhanced by means of intersectional approaches like the split-GAL4 system, which labels the positive intersection of the expression patterns of two (denser) GAL4 lines. To this end, two GAL4 lines have to be identified as labelling a neuron of interest. Current approaches to tackling this task include visual inspection, as well as automated search in 2d projection images, of single cell multi-color flip-out (MCFO) acquisitions of GAL4 expression patterns. There is to date no automated method available that performs full 3d search in MCFO imagery of GAL4 lines, nor one that leverages automated reconstructions of the labelled neuron morphologies. To close this gap, we propose PatchPerPixMatch, a fully automated approach for finding a given neuron morphology in MCFO acquisitions of Gen1 GAL4 lines. PatchPerPixMatch performs automated instance segmentation of MCFO acquisitions, and subsequently searches for a target neuron morphology by minimizing an objective that aims at covering the target with a set of well-fitting segmentation fragments. PatchPerPixMatch is computationally efficient albeit being full 3d, while also highly robust to inaccuracies in the automated neuron instance segmentation. We are releasing PatchPerPixMatch search results for ~30,000 neuron morphologies from the Drosophila hemibrain in ~20,000 MCFO acquisitions of ~3,500 Gen1 GAL4 lines. |
| Keywords: | Animals, Drosophila melanogaster |
| Source: | bioRxiv |
| Publisher: | Cold Spring Harbor Laboratory Press |
| Article Number: | 2021.07.23.453511 |
| Date: | 26 July 2021 |
| Official Publication: | https://doi.org/10.1101/2021.07.23.453511 |
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