PT  - JOURNAL ARTICLE
AU  - Justin Swaney
AU  - Lee Kamentsky
AU  - Nicholas B Evans
AU  - Katherine Xie
AU  - Young-Gyun Park
AU  - Gabrielle Drummond
AU  - Dae Hee Yun
AU  - Kwanghun Chung
TI  - Scalable image processing techniques for quantitative analysis of volumetric biological images from light-sheet microscopy
AID  - 10.1101/576595
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 576595
4099  - http://biorxiv.org/content/early/2019/03/16/576595.short
4100  - http://biorxiv.org/content/early/2019/03/16/576595.full
AB  - Here we describe an image processing pipeline for quantitative analysis of terabyte-scale volumetric images of SHIELD-processed mouse brains imaged with light-sheet microscopy. The pipeline utilizes open-source packages for destriping, stitching, and atlas alignment that are optimized for parallel processing. The destriping step removes stripe artifacts, corrects uneven illumination, and offers over 100x speed improvements compared to previously reported algorithms. The stitching module builds upon Terastitcher to create a single volumetric image quickly from individual image stacks with parallel processing enabled by default. The atlas alignment module provides an interactive web-based interface that automatically calculates an initial alignment to a reference image which can be manually refined. The atlas alignment module also provides summary statistics of fluorescence for each brain region as well as region segmentations for visualization. The expected runtime of our pipeline on a whole mouse brain hemisphere is 1-2 d depending on the available computational resources and the dataset size.