Fiji is a distribution of the popular Open Source software ImageJ focused on biological image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image processing algorithms. Fiji facilitates the transformation of novel algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

Supplementary data are available at Bioinformatics online.

Bone geometry is commonly measured on computed tomographic (CT) and X-ray microtomographic (μCT) images. We obtained hundreds of CT, μCT and synchrotron μCT images of bones from diverse species that needed to be analysed remote from scanning hardware, but found that available software solutions were expensive, inflexible or methodologically opaque. We implemented standard bone measurements in a novel ImageJ plugin, BoneJ, with which we analysed trabecular bone, whole bones and osteocyte lacunae. BoneJ is open source and free for anyone to download, use, modify and distribute.

MorphoLibJ is freely available at http://imagej.net/MorphoLibJ CONTACT: david.legland@nantes.inra.frSupplementary information: Supplementary data are available at Bioinformatics online.

A key challenge in neuroscience is the expeditious reconstruction of neuronal circuits. For model systems such as Drosophila and C. elegans, the limiting step is no longer the acquisition of imagery but the extraction of the circuit from images. For this purpose, we designed a software application, TrakEM2, that addresses the systematic reconstruction of neuronal circuits from large electron microscopical and optical image volumes. We address the challenges of image volume composition from individual, deformed images; of the reconstruction of neuronal arbors and annotation of synapses with fast manual and semi-automatic methods; and the management of large collections of both images and annotations. The output is a neural circuit of 3d arbors and synapses, encoded in NeuroML and other formats, ready for analysis.

Here we describe an automated method, which we call serial two-photon (STP) tomography, that achieves high-throughput fluorescence imaging of mouse brains by integrating two-photon microscopy and tissue sectioning. STP tomography generates high-resolution datasets that are free of distortions and can be readily warped in 3D, for example, for comparing multiple anatomical tracings. This method opens the door to routine systematic studies of neuroanatomy in mouse models of human brain disorders.

In this article, we present a novel method for the automatic 3D reconstruction of thick tissue blocks from 2D histological sections. The algorithm completes a high-content (multiscale, multifeature) imaging system for simultaneous morphological and molecular analysis of thick tissue samples. This computer-based system integrates image acquisition, annotation, registration, and three-dimensional reconstruction. We present an experimental validation of this tool using both synthetic and real data. In particular, we present the 3D reconstruction of an entire mouse mammary gland and demonstrate the integration of high-resolution molecular data.

Understanding how brain activation mediates behaviors is a central goal of systems neuroscience. Here we apply an automated method for mapping brain activation in the mouse in order to probe how sex-specific social behaviors are represented in the male brain. Our method uses the immediate early gene c-fos, a marker of neuronal activation, visualized by serial two-photon tomography: the c-fos-GFP-positive neurons are computationally detected, their distribution is registered to a reference brain and a brain atlas, and their numbers are analyzed by statistical tests. Our results reveal distinct and shared female and male interaction-evoked patterns of male brain activation representing sex discrimination and social recognition. We also identify brain regions whose degree of activity correlates to specific features of social behaviors and estimate the total numbers and the densities of activated neurons per brain areas. Our study opens the door to automated screening of behavior-evoked brain activation in the mouse.