Anna Kreshuk

Automated Detection and Segmentation of Synaptic Contacts in Nearly Isotropic Serial Electron Microscopy Images

We describe a protocol for fully automated detection and segmentation of asymmetric, presumed excitatory, synapses in serial electron microscopy images of the adult mammalian cerebral cortex, taken with the focused ion beam, scanning electron microscope (FIB/SEM). The procedure is based on interactive machine learning and only requires a few labeled synapses for training. The statistical learning is performed on geometrical features of 3D neighborhoods of each voxel and can fully exploit the high z-resolution of the data. On a quantitative validation dataset of 111 synapses in 409 images of 1948×1342 pixels with manual annotations by three independent experts the error rate of the algorithm was found to be comparable to that of the experts (0.92 recall at 0.89 precision). Our software offers a convenient interface for labeling the training data and the possibility to visualize and proofread the results in 3D. The source code, the test dataset and the ground truth annotation are freely available on the website http://www.ilastik.org/synapse-detection.

Correlative In Vivo 2 Photon and Focused Ion Beam Scanning Electron Microscopy of Cortical Neurons

Correlating in vivo imaging of neurons and their synaptic connections with electron microscopy combines dynamic and ultrastructural information. Here we describe a semi-automated technique whereby volumes of brain tissue containing axons and dendrites, previously studied in vivo, are subsequently imaged in three dimensions with focused ion beam scanning electron microcopy. These neurites are then identified and reconstructed automatically from the image series using the latest segmentation algorithms. The fast and reliable imaging and reconstruction technique avoids any specific labeling to identify the features of interest in the electron microscope, and optimises their preservation and staining for 3D analysis.

Automated detection of synapses in serial section transmission electron microscopy image stacks.

We describe a method for fully automated detection of chemical synapses in serial electron microscopy images with highly anisotropic axial and lateral resolution, such as images taken on transmission electron microscopes. Our pipeline starts from classification of the pixels based on 3D pixel features, which is followed by segmentation with an Ising model MRF and another classification step, based on object-level features. Classifiers are learned on sparse user labels; a fully annotated data subvolume is not required for training. The algorithm was validated on a set of 238 synapses in 20 serial 7197×7351 pixel images (4.5×4.5×45 nm resolution) of mouse visual cortex, manually labeled by three independent human annotators and additionally re-verified by an expert neuroscientist. The error rate of the algorithm (12% false negative, 7% false positive detections) is better than state-of-the-art, even though, unlike the state-of-the-art method, our algorithm does not require a prior segmentation of the image volume into cells. The software is based on the ilastik learning and segmentation toolkit and the vigra image processing library and is freely available on our website, along with the test data and gold standard annotations (http://www.ilastik.org/synapse-detection/sstem).

Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues

Advances in the application of electron microscopy (EM) to serial imaging are opening doors to new ways of analyzing cellular structure. New and improved algorithms and workflows for manual and semiautomated segmentation allow us to observe the spatial arrangement of the smallest cellular features with unprecedented detail in full three‐dimensions. From larger samples, higher complexity models can be generated; however, they pose new challenges to data management and analysis. Here we review some currently available solutions and present our approach in detail. We use the fully immersive virtual reality (VR) environment CAVE (cave automatic virtual environment), a room in which we are able to project a cellular reconstruction and visualize in 3D, to step into a world created with Blender, a free, fully customizable 3D modeling software with NeuroMorph plug‐ins for visualization and analysis of EM preparations of brain tissue. Our workflow allows for full and fast reconstructions of volumes of brain neuropil using ilastik, a software tool for semiautomated segmentation of EM stacks. With this visualization environment, we can walk into the model containing neuronal and astrocytic processes to study the spatial distribution of glycogen granules, a major energy source that is selectively stored in astrocytes. The use of CAVE was key to the observation of a nonrandom distribution of glycogen, and led us to develop tools to quantitatively analyze glycogen clustering and proximity to other subcellular features. J. Comp. Neurol. 524:23–38, 2016.

Semiautomated correlative 3D electron microscopy of in vivo–imaged axons and dendrites

This protocol describes how in vivo–imaged dendrites and axons in adult mouse brains can subsequently be prepared and imaged with focused ion beam scanning electron microscopy (FIBSEM). The procedure starts after in vivo imaging with chemical fixation, followed by the identification of the fluorescent structures of interest. Their position is then highlighted in the fixed tissue by burning fiducial marks with the two-photon laser. Once the section has been stained and resin-embedded, a small block is trimmed close to these marks. Serially aligned EM images are acquired through this region, using FIBSEM, and the neurites of interest are then reconstructed semiautomatically by using the ilastik software (http://ilastik.org/). This reliable imaging and reconstruction technique avoids the use of specific labels to identify the structures of interest in the electron microscope, enabling optimal chemical fixation techniques to be applied and providing the best possible structural preservation for 3D analysis. The entire protocol takes ∼4 d.

Multicut brings automated neurite segmentation closer to human performance

Reference EPFL-ARTICLE-226946doi:10.1038/nmeth.4151View record in Web of Science Record created on 2017-03-27, modified on 2017-07-13

Automated segmentation of synapses in 3D EM data

This contribution presents a method for automatic detection of excitatory, asymmetric synapses and segmentation of synaptic junctional complexes in stacks of serial electron microscopy images with nearly isotropic resolution. The method uses a Random Forest classifier in the space of generic image features, computed directly in the 3D neighborhoods of each pixel, and an additional step of interactive probability maps thresholding. On the test dataset, the algorithm missed considerably less synapses than the human expert during the ground truth creation, while maintaining an equivalent false positive rate. The algorithm is implemented as an extension to the Interactive Learning and Segmentation Toolkit “ilastik” and is freely available on our website (www.ilastik.org/synapse-detection).

Automated tracing of myelinated axons and detection of the nodes of Ranvier in serial images of peripheral nerves.

The development of realistic neuroanatomical models of peripheral nerves for simulation purposes requires the reconstruction of the morphology of the myelinated fibres in the nerve, including their nodes of Ranvier. Currently, this information has to be extracted by semimanual procedures, which severely limit the scalability of the experiments.

Segmenting and Tracking Multiple Dividing Targets Using ilastik

Tracking crowded cells or other targets in biology is often a challenging task due to poor signal-to-noise ratio, mutual occlusion, large displacements, little discernibility, and the ability of cells to divide. We here present an open source implementation of conservation tracking (Schiegg et al., IEEE international conference on computer vision (ICCV). IEEE, New York, pp 2928-2935, 2013) in the ilastik software framework. This robust tracking-by-assignment algorithm explicitly makes allowance for false positive detections, undersegmentation, and cell division. We give an overview over the underlying algorithm and parameters, and explain the use for a light sheet microscopy sequence of a Drosophila embryo. Equipped with this knowledge, users will be able to track targets of interest in their own data.

Improving 3D EM data segmentation by joint optimization over boundary evidence and biological priors

We present a new automated neuron segmentation algorithm for isotropic 3D electron microscopy data. We cast the problem into the asymmetric multiway cut framework. The latter combines boundary-based segmentation (clustering) with region-based segmentation (semantic labeling) in a single problem and objective function. This joint formulation allows us to augment local boundary evidence with higherlevel biological priors, such as membership to an axonic or dendritic neurite. Joint optimization enforces consistency between evidence and priors, leading to correct resolution of many difficult boundary configurations. We show experimentally on a FIB/SEM dataset of mouse cortex that the new approach outperforms existing hierarchical segmentation and multicut algorithms which only use boundary evidence.