title: Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python.
abstract: In this article, we describe the use of Python for large-scale automated server-based bio-image analysis in FARSIGHT, a free and open-source toolkit of image analysis methods for quantitative studies of complex and dynamic tissue microenvironments imaged by modern optical microscopes, including confocal, multi-spectral, multi-photon, and time-lapse systems. The core FARSIGHT modules for image segmentation, feature extraction, tracking, and machine learning are written in C++, leveraging widely used libraries including ITK, VTK, Boost, and Qt. For solving complex image analysis tasks, these modules must be combined into scripts using Python. As a concrete example, we consider the problem of analyzing 3-D multi-spectral images of brain tissue surrounding implanted neuroprosthetic devices, acquired using high-throughput multi-spectral spinning disk step-and-repeat confocal microscopy. The resulting images typically contain 5 fluorescent channels. Each channel consists of 6000 × 10,000 × 500 voxels with 16 bits/voxel, implying image sizes exceeding 250 GB. These images must be mosaicked, pre-processed to overcome imaging artifacts, and segmented to enable cellular-scale feature extraction. The features are used to identify cell types, and perform large-scale analysis for identifying spatial distributions of specific cell types relative to the device. Python was used to build a server-based script (Dell 910 PowerEdge servers with 4 sockets/server with 10 cores each, 2 threads per core and 1TB of RAM running on Red Hat Enterprise Linux linked to a RAID 5 SAN) capable of routinely handling image datasets at this scale and performing all these processing steps in a collaborative multi-user multi-platform environment. Our Python script enables efficient data storage and movement between computers and storage servers, logs all the processing steps, and performs full multi-threaded execution of all codes, including open and closed-source third party libraries.
authors.affiliation: BioImage Analytics Laboratory, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Rey-Villamizar
authors.affiliation: BioImage Analytics Laboratory, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA.
authors.forename: Vinay
authors.initials: V
authors.lastname: Somasundar
authors.affiliation: BioImage Analytics Laboratory, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA.
authors.forename: Murad
authors.initials: M
authors.lastname: Megjhani
authors.affiliation: BioImage Analytics Laboratory, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA.
authors.forename: Yan
authors.initials: Y
authors.lastname: Xu
authors.affiliation: BioImage Analytics Laboratory, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA.
authors.forename: Yanbin
authors.initials: Y
authors.lastname: Lu
authors.affiliation: BioImage Analytics Laboratory, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA.
authors.forename: Raghav
authors.initials: R
authors.lastname: Padmanabhan
authors.affiliation: Center for Integrative Brain Research, Seattle Children's Research Institute Seattle, WA, USA.
authors.forename: Kristen
authors.initials: K
authors.lastname: Trett
authors.affiliation: Center for Integrative Brain Research, Seattle Children's Research Institute Seattle, WA, USA.
authors.forename: William
authors.initials: W
authors.lastname: Shain
authors.affiliation: BioImage Analytics Laboratory, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA.
authors.forename: Badri
authors.initials: B
authors.lastname: Roysam
keywords: C++; Python; image processing software; microglia tracing; neuroprostetic device; neuroscience; segmentation