Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python

• Published in: Frontiers in neuroinformatics Vol. 8; p. 39
• Main Authors: Rey-Villamizar, Nicolas, Somasundar, Vinay, Megjhani, Murad, Xu, Yan, Lu, Yanbin, Padmanabhan, Raghav, Trett, Kristen, Shain, William, Roysam, Badri
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 29.04.2014; Frontiers Media S.A
• Subjects: Algorithms; Automation; Brain research; C++; Computational neuroscience; Confocal microscopy; Datasets; Human error; Image processing; Image Processing Software; Itk protein; Learning algorithms; Microenvironments; Microscopes; Microscopy; Morphology; Neuroimaging; neuroprostetic device; Neuroscience; Prostheses; Prosthetics; python; Registration; Segmentation; Software; Spatial distribution; United States > US; San Francisco California; Fiji; C++; neuroscience; segmentation; image processing software; microglia tracing; Python; neuroprostetic device
• ISSN: 1662-5196; 1662-5196
• DOI: 10.3389/fninf.2014.00039

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.