Machine learning of hierarchical clustering to segment 2D and 3D images

We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.Comment: 15 pages, 8 figure

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

A circle-based method for detection of neural fibre cross-sections in classically stained 2D electron micrographs

Recent developments in electron microscopy now permit the unambiguous reconstruction of even the smallest neural fibres by human experts. However, manual reconstruction of an interesting volume of neural tissue would take thousands of person-years. Techniques to automate such reconstruction are therefore highly desirable and currently under active development. Here we present a novel circle-based technique and assess its performance on classically stained electron micrographs of the molecular layer of mouse cerebellar cortex. We compare its performance to a recently published pixel-based classifier (ilastik), selected because a similar random forest classifier from the same group has shown promising results on images of neural tissue. The performance of our algorithm and that of ilastik are similar, achieving approximately 50% on an overlap-based f-measure