Large-scale automated identification of mouse brain cells in confocal light sheet microscopy images
Autori: Frasconi P., Silvestri L., Soda P., Cortini R., Pavone F. S., Iannello G.
Affiliazione autori: Univ Florence, Dept Informat Engn DINFO, I-50139 Florence, Italy; Univ Florence, LENS, I-50019 Sesto Fiorentino, Italy; Univ Campus Biomed Roma, Integrated Res Ctr, I-00128 Rome, Italy
Abstract: Motivation: Recently, confocal light sheet microscopy has enabled high-throughput acquisition of whole mouse brain 3D images at the micron scale resolution. This poses the unprecedented challenge of creating accurate digital maps of the whole set of cells in a brain.
Results: We introduce a fast and scalable algorithm for fully automated cell identification. We obtained the whole digital map of Purkinje cells in mouse cerebellum consisting of a set of 3D cell center coordinates. The method is accurate and we estimated an F1 measure of 0.96 using 56 representative volumes, totaling 1.09 GVoxel and containing 4138 manually annotated soma centers.
Availability and implementation: Source code and its documentation are available at http://bcfind.dinfo.unifi.it/. The whole pipeline of methods is implemented in Python and makes use of Pylearn2 and modified parts of Scikit-learn. Brain images are available on request.
Volume: 30 (17) Da Pagina: i587 A: i593
Maggiori informazioni: The work of L.S. and F.P. was supported by E.U. grants FP7 228334, FP7 284464 and FET flagship HBP (604102).DOI: 10.1093/bioinformatics/btu469Citazioni: 32dati da “WEB OF SCIENCE” (of Thomson Reuters) aggiornati al: 2021-09-19Riferimenti tratti da Isi Web of Knowledge: (solo abbonati) Link per visualizzare la scheda su IsiWeb: Clicca quiLink per visualizzare la citazioni su IsiWeb: Clicca qui