Motivation: Automatic recognition of cell identities is critical for quantitative measurement, targeting and manipulation of cells of model animals at single-cell resolution. It has been shown to be a powerful tool for studying gene expression and regulation, cell lineages and cell fates. Existing methods first segment cells, before applying a recognition algorithm in the second step. As a result, the segmentation errors in the first step directly affect and complicate the subsequent cell recognition step. Moreover, in new experimental settings, some of the image features that have been previously relied upon to recognize cells may not be easy to reproduce, due to limitations on the number of color channels available for fluorescent imaging or to the cost of building transgenic animals. An approach that is more accurate and relies on only a single signal channel is clearly desirable.

Results: We have developed a new method, called simultaneous recognition and segmentation (SRS) of cells, and applied it to 3D image stacks of the model organism Caenorhabditis elegans. Given a 3D image stack of the animal and a 3D atlas of target cells, SRS is effectively an atlas-guided voxel classification process: cell recognition is realized by smoothly deforming the atlas to best fit the image, where the segmentation is obtained naturally via classification of all image voxels. The method achieved a 97.7% overall recognition accuracy in recognizing a key class of marker cells, the body wall muscle (BWM) cells, on a dataset of 175 C.elegans image stacks containing 14 118 manually curated BWM cells providing the ‘ground-truth’ for accuracy. This result was achieved without any additional fiducial image features. SRS also automatically identified 14 of the image stacks as involving ±90○ rotations. With these stacks excluded from the dataset, the recognition accuracy rose to 99.1%. We also show SRS is generally applicable to other cell types, e.g. intestinal cells.

Availability: The supplementary movies can be downloaded from our web site http://penglab.janelia.org/proj/celegans_seganno. The method has been implemented as a plug-in program within the V3D system (http://penglab.janelia.org/proj/v3d), and will be released in the V3D plugin source code repository.

Contact: pengh@janelia.hhmi.org

Motivation: Digital reconstruction, or tracing, of 3D neuron structures is critical toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low signal-to-noise ratio (SNR) and fragmented neuron segments. Published work can handle these hard situations only by introducing global prior information, such as where a neurite segment starts and terminates. However, manual incorporation of such global information can be very time consuming. Thus, a completely automatic approach for these hard situations is highly desirable.

Results: We have developed an automatic graph algorithm, called the all-path pruning (APP), to trace the 3D structure of a neuron. To avoid potential mis-tracing of some parts of a neuron, an APP first produces an initial over-reconstruction, by tracing the optimal geodesic shortest path from the seed location to every possible destination voxel/pixel location in the image. Since the initial reconstruction contains all the possible paths and thus could contain redundant structural components (SC), we simplify the entire reconstruction without compromising its connectedness by pruning the redundant structural elements, using a new maximal-covering minimal-redundant (MCMR) subgraph algorithm. We show that MCMR has a linear computational complexity and will converge. We examined the performance of our method using challenging 3D neuronal image datasets of model organisms (e.g. fruit fly).

Availability: The software is available upon request. We plan to eventually release the software as a plugin of the V3D-Neuron package at http://penglab.janelia.org/proj/v3d.

Contact: pengh@janelia.hhmi.org

Motivation: Digital reconstruction of 3D neuron structures is an important step toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low single-to-noise ratio and discontinued segments of neurite patterns.

Results: We developed a graph-augmented deformable model (GD) to reconstruct (trace) the 3D structure of a neuron when it has a broken structure and/or fuzzy boundary. We formulated a variational problem using the geodesic shortest path, which is defined as a combination of Euclidean distance, exponent of inverse intensity of pixels along the path and closeness to local centers of image intensity distribution. We solved it in two steps. We first used a shortest path graph algorithm to guarantee that we find the global optimal solution of this step. Then we optimized a discrete deformable curve model to achieve visually more satisfactory reconstructions. Within our framework, it is also easy to define an optional prior curve that reflects the domain knowledge of a user. We investigated the performance of our method using a number of challenging 3D neuronal image datasets of different model organisms including fruit fly, Caenorhabditis elegans, and mouse. In our experiments, the GD method outperformed several comparison methods in reconstruction accuracy, consistency, robustness and speed. We further used GD in two real applications, namely cataloging neurite morphology of fruit fly to build a 3D ‘standard’ digital neurite atlas, and estimating the synaptic bouton density along the axons for a mouse brain.

Availability: The software is provided as part of the V3D-Neuron 1.0 package freely available at http://penglab.janelia.org/proj/v3d

Contact: pengh@janelia.hhmi.org

Motivation: The fruit fly (Drosophila melanogaster) is a commonly used model organism in biology. We are currently building a 3D digital atlas of the fruit fly larval nervous system (LNS) based on a large collection of fly larva GAL4 lines, each of which targets a subset of neurons. To achieve such a goal, we need to automatically align a number of high-resolution confocal image stacks of these GAL4 lines. One commonly employed strategy in image pattern registration is to first globally align images using an affine transform, followed by local non-linear warping. Unfortunately, the spatially articulated and often twisted LNS makes it difficult to globally align the images directly using the affine method. In a parallel project to build a 3D digital map of the adult fly ventral nerve cord (VNC), we are confronted with a similar problem.

Results: We proposed to standardize a larval image by best aligning its principal skeleton (PS), and thus used this method as an alternative of the usually considered affine alignment. The PS of a shape was defined as a series of connected polylines that spans the entire shape as broadly as possible, but with the shortest overall length. We developed an automatic PS detection algorithm to robustly detect the PS from an image. Then for a pair of larval images, we designed an automatic image registration method to align their PSs and the entire images simultaneously. Our experimental results on both simulated images and real datasets showed that our method does not only produce satisfactory results for real confocal larval images, but also perform robustly and consistently when there is a lot of noise in the data. We also applied this method successfully to confocal images of some other patterns such as the adult fruit fly VNC and center brain, which have more complicated PS. This demonstrates the flexibility and extensibility of our method.

Availability: The supplementary movies, full size figures, test data, software, and tutorial on the software can be downloaded freely from our website http://penglab.janelia.org/proj/principal_skeleton

Contact: pengh@janelia.hhmi.org

Supplementary information: Supplementary data are available at Bioinformatics online.

Volume-object annotation system (VANO) is a cross-platform image annotation system that enables one to conveniently visualize and annotate 3D volume objects including nuclei and cells. An application of VANO typically starts with an initial collection of objects produced by a segmentation computation. The objects can then be labeled, categorized, deleted, added, split, merged and redefined. VANO has been used to build high-resolution digital atlases of the nuclei of Caenorhabditis elegans at the L1 stage and the nuclei of Drosophila melanogaster's ventral nerve cord at the late embryonic stage.

Availability: Platform independent executables of VANO, a sample dataset, and a detailed description of both its design and usage are available at research.janelia.org/peng/proj/vano. VANO is open-source for co-development.

Contact: pengh@janelia.hhmi.org

Supplementary information: Supplementary data are available at Bioinformatics online.

In recent years, the deluge of complicated molecular and cellular microscopic images creates compelling challenges for the image computing community. There has been an increasing focus on developing novel image processing, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems. This emerging new area of bioinformatics can be called ‘bioimage informatics’. This article reviews the advances of this field from several aspects, including applications, key techniques, available tools and resources. Application examples such as high-throughput/high-content phenotyping and atlas building for model organisms demonstrate the importance of bioimage informatics. The essential techniques to the success of these applications, such as bioimage feature identification, segmentation and tracking, registration, annotation, mining, image data management and visualization, are further summarized, along with a brief overview of the available bioimage databases, analysis tools and other resources.

Contact: pengh@janelia.hhmi.org

Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Caenorhabditis elegans, a roundworm found in soil, is a widely studied model organism with about 1000 cells in the adult. Producing high-resolution fluorescence images of C.elegans to reveal biological insights is becoming routine, motivating the development of advanced computational tools for analyzing the resulting image stacks. For example, worm bodies usually curve significantly in images. Thus one must ‘straighten’ the worms if they are to be compared under a canonical coordinate system.

Results: We develop a worm straightening algorithm (WSA) that restacks cutting planes orthogonal to a ‘backbone’ that models the anterior–posterior axis of the worm. We formulate the backbone as a parametric cubic spline defined by a series of control points. We develop two methods for automatically determining the locations of the control points. Our experimental methods show that our approaches effectively straighten both 2D and 3D worm images.

Contact: pengh@janelia.hhmi.org

Supplementary information: The example data sets and programs are available upon request.