Congratulations to our collaborators at the University of Houston, Cullen College of Engineering. Dr. Nicholas Rey, a member of Dr. Badri Roysam’s lab at the University of Houston, is presenting their FARSIGHT toolkit at the European BioImage Analysis Symposium (EuBIAS), Barcelona, Oct 7-11.
FARSIGHT is a popular toolkit for medical image analysis, particularly for neuronal analysis in microscopy images. It features automated and computer-assisted method for tracing neural structures and for cell analysis. FARSIGHT is written in C++ and python, using Kitware’s Visualization Toolkit (VTK) and the Insight Toolkit (ITK).
Via collaborative grants, we have been assisting Dr. Badri Roysam for many years with the development of FARSIGHT. Most recently, our collaborations with Dr. Roysam were significantly extended by the “VTK Maintenance” R01 funding that we received from the NIH. Dr. Roysam is a subcontractor on that grant, and he will be using the funding to upgrade FARSIGHT to VTK 6 and to take advantage of the improved annotation, informatics, and visualization capabilities available in VTK 6.
We look forward to our continued collaborations with Dr. Roysam, and we are proud of the roles that VTK and ITK are playing in accelerating the pace of microscopy research.
EuBIAS Presentation on FARSIGHT
Title: The FARSIGHT Toolkit (C++) for Large Scale Image Analysis and Understanding
Abstract: The FARSIGHT Toolkit is a flexible open source software package written using a modular architecture with a powerful graphical user interface for image processing. All modules are written in accordance with software practices of the Insight Toolkit Community. The toolkit is mainly developed in C++, a portable, efficient and general purpose programming language. The core high level image processing algorithms implemented in FARSIGHT are build using the filters developed by the Insight Segmentation and Registration Toolkit (ITK) and the visualization is build mainly using The Visualization ToolKit (VTK). Importantly, all modules are accessible through the Python scripting language which allows users to create scripts to accomplish sophisticated associative image analysis tasks over multi-dimensional microscopy image data. In the past years the software has evolved so that it can be used routinely to analyze high throughput high content image data. Core modules like segmentation, tracing, and tracking have been updated to handle images of the order of hundreds of gigabytes efficiently. Also, the graphical user interface has been updated to efficiently visualize and more importantly edit these huge datasets. In particular, three projects will be presented to the audience: the study of immune system response to the implantation of neuroprostetic devices, the study of high-throughput immune cell interaction, and the profiling of cell population changes.