CellBoost: A pipeline for machine assisted annotation in Neuroanatomy.

Autor:	Qian K; Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA., Friedman B; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA., Takatoh J; Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA., Wang F; Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.; McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA., Kleinfeld D; Department of Physics, University of California, San Diego, La Jolla, CA 92093, USA.; Department of Neurobiology, University of California, San Diego, La Jolla, CA 92093, USA., Freund Y; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA.; Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA 92093, USA.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2024 Jan 21. Date of Electronic Publication: 2024 Jan 21.
DOI:	10.1101/2023.09.13.557658
Abstrakt:	One of the important yet labor intensive tasks in neuroanatomy is the identification of select populations of cells. Current high-throughput techniques enable marking cells with histochemical fluorescent molecules as well as through the genetic expression of fluorescent proteins. Modern scanning microscopes allow high resolution multi-channel imaging of the mechanically or optically sectioned brain with thousands of marked cells per square millimeter. Manual identification of all marked cells is prohibitively time consuming. At the same time, simple segmentation algorithms suffer from high error rates and sensitivity to variation in fluorescent intensity and spatial distribution. We present a methodology that combines human judgement and machine learning that serves to significantly reduce the labor of the anatomist while improving the consistency of the annotation. As a demonstration, we analyzed murine brains with marked premotor neurons in the brainstem. We compared the error rate of our method to the disagreement rate among human anatomists. This comparison shows that our method can reduce the time to annotate by as much as ten-fold without significantly increasing the rate of errors. We show that our method achieves significant reduction in labor while achieving an accuracy that is similar to the level of agreement between different anatomists.
Databáze:	MEDLINE
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