We discuss the advantages and challenges of the open-source strategy in biological image analysis and argue that its full impact will not be realized without better support and recognition of software engineers’ contributions to the biological sciences and more support of this development model from funders and institutions.

Anatomy of large biological specimens is often reconstructed from serially sectioned volumes imaged by high-resolution microscopy. We developed a method to reassemble a continuous volume from such large section series that explicitly minimizes artificial deformation by applying a global elastic constraint. We demonstrate our method on a series of transmission electron microscopy sections covering the entire 558-cell Caenorhabditis elegans embryo and a segment of the Drosophila melanogaster larval ventral nerve cord.

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 new 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.

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.

We present an approach for the automatic reconstruction of neurons from 3D stacks of electron microscopy sections. The core of our system is a set of possible assignments, each of which proposes with some cost a link between neuron regions in consecutive sections. These can model the continuation, branching, and end of neurons. The costs are trainable on positive assignment samples. An optimal and consistent set of assignments is found for the whole volume at once by solving an integer linear program. This set of assignments determines both the segmentation into neuron regions and the correspondence between such regions in neighboring slices. For each picked assignment, a confidence value helps to prioritize decisions to be reviewed by a human expert. We evaluate the performance of our method on an annotated volume of neural tissue and compare to the current state of the art [26]. Our method is superior in accuracy and can be trained using a small number of samples. The observed inference times are linear with about 2 milliseconds per neuron and section.

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.

The task of the visual system is to translate light into neuronal encoded information. This translation of photons into neuronal signals is achieved by photoreceptor neurons (PRs), specialized sensory neurons, located in the eye. Upon perception of light the PRs will send a signal to target neurons, which represent a first station of visual processing. Increasing complexity of visual processing stems from the number of distinct PR subtypes and their various types of target neurons that are contacted. The visual system of the fruit fly larva represents a simple visual system (larval optic neuropil, LON) that consists of 12 PRs falling into two classes: blue-senstive PRs expressing Rhodopsin 5 (Rh5) and green-sensitive PRs expressing Rhodopsin 6 (Rh6). These afferents contact a small number of target neurons, including optic lobe pioneers (OLPs) and lateral clock neurons (LNs). We combine the use of genetic markers to label both PR subtypes and the distinct, identifiable sets of target neurons with a serial EM reconstruction to generate a high-resolution map of the larval optic neuropil. We find that the larval optic neuropil shows a clear bipartite organization consisting of one domain innervated by PRs and one devoid of PR axons. The topology of PR projections, in particular the relationship between Rh5 and Rh6 afferents, is maintained from the nerve entering the brain to the axon terminals. The target neurons can be subdivided according to neurotransmitter or neuropeptide they use as well as the location within the brain. We further track the larval optic neuropil through development from first larval instar to its location in the adult brain as the accessory medulla.

The neuroarchitecture of Acoela has been at the center of morphological debates. Some authors, using immunochemical tools, suggest that the nervous system in Acoela is organized as a commissural brain that bears little resemblance to the central, ganglionic type brain of other flatworms, and bilaterians in general. Others, who used histological staining on paraffin sections, conclude that it is a compact structure (an endonal brain; e.g., Raikova 2004; von Graff 1891; Delage Arch Zool Exp Gén 4:109-144, 1886). To address this question with modern tools, we have obtained images from serial transmission electron microscopic sections of the entire hatchling of Symsagittifera roscoffensis. In addition, we obtained data from wholemounts of hatchlings labeled with markers for serotonin and tyrosinated tubulin. Our data show that the central nervous system of a juvenile S. roscoffensis consists of an anterior compact brain, formed by a dense, bilobed mass of neuronal cell bodies surrounding a central neuropile. The neuropile flanks the median statocyst and contains several types of neurites, classified according to their types of synaptic vesicles. The neuropile issues three pairs of nerve cords that run at different dorso-ventral positions along the whole length of the body. Neuronal cell bodies flank the cords, and neuromuscular synapses are abundant. The TEM analysis also reveals different classes of peripheral sensory neurons and provides valuable information about the spatial relationships between neurites and other cell types within the brain and nerve cords. We conclude that the acoel S. roscoffensis has a central brain that is comparable in size and architecture to the brain of other (rhabditophoran) flatworms.

Tiled serial section Transmission Electron Microscopy (ssTEM) is increasingly used to describe high-resolution anatomy of large biological specimens. In particular in neurobiology, TEM is indispensable for analysis of synaptic connectivity in the brain. Registration of ssTEM image mosaics has to recover the 3D continuity and geometrical properties of the specimen in presence of various distortions that are applied to the tissue during sectioning, staining and imaging. These include staining artifacts, mechanical deformation, missing sections and the fact that structures may appear dissimilar in consecutive sections.