@article {65832, title = {Automatic Detection of Synaptic Partners in a Whole-Brain Drosophila EM Dataset}, journal = {Nature Methods}, volume = {18}, year = {2021}, month = {2021 Jul 1}, pages = {771-4}, abstract = {

The study of neural circuits requires the reconstruction of neurons and the identification of synaptic connections between them. To scale the reconstruction to the size of whole-brain datasets, semi-automatic methods are needed to solve those tasks. Here, we present an automatic method for synaptic partner identification in insect brains, which uses convolutional neural networks to identify post-synaptic sites and their pre-synaptic partners. The networks can be trained from human generated point annotations alone and requires only simple post-processing to obtain final predictions. We used our method to extract 244 million putative synaptic partners in the fifty-teravoxel full adult fly brain (FAFB) electron microscopy (EM) dataset and evaluated its accuracy on 146,643 synapses from 702 neurons with a total cable length of 312 mm in four different brain regions. The predicted synaptic connections can be used together with a neuron segmentation to infer a connectivity graph with high accuracy: 96\% of edges between connected neurons are correctly classified as weakly connected (less than five synapses) and strongly connected (at least five synapses). Our synaptic partner predictions for the FAFB dataset are publicly available, together with a query library allowing automatic retrieval of up- and downstream neurons.

}, doi = {10.1038/s41592-021-01183-7}, author = {Buhmann, Julia and Sheridan, Arlo and Gerhard, Stephan and Krause, Renate and Nguyen, Tri and Heinrich, Larissa and Schlegel, Philipp and Lee, Wei-Chung Allen and Wilson, Rachel and Saalfeld, Stephan and Jefferis, Gregory and Bock, Davi and Turaga, Srinivas and Cook, Matthew and Funke, Jan} } @article {49292, title = {Synaptic partner prediction from point annotations in insect brains.}, journal = {MICCAI 2018: Medical Image Computing and Computer Assisted Intervention}, year = {2018}, month = {2018 Sep 26}, abstract = {

High-throughput electron microscopy allows recording of lar- ge stacks of neural tissue with sufficient resolution to extract the wiring diagram of the underlying neural network. Current efforts to automate this process focus mainly on the segmentation of neurons. However, in order to recover a wiring diagram, synaptic partners need to be identi- fied as well. This is especially challenging in insect brains like Drosophila melanogaster, where one presynaptic site is associated with multiple post- synaptic elements. Here we propose a 3D U-Net architecture to directly identify pairs of voxels that are pre- and postsynaptic to each other. To that end, we formulate the problem of synaptic partner identification as a classification problem on long-range edges between voxels to encode both the presence of a synaptic pair and its direction. This formulation allows us to directly learn from synaptic point annotations instead of more ex- pensive voxel-based synaptic cleft or vesicle annotations. We evaluate our method on the MICCAI 2016 CREMI challenge and improve over the current state of the art, producing 3\% fewer errors than the next best method.

}, doi = {10.1007/978-3-030-00934-2_35}, author = {Buhmann, Julia and Krause, Renate and Lentini, Rodrigo Ceballos and Eckstein, Nils and Cook, Matthew and Turaga, Srinivas C and Funke, Jan} } @article {48193, title = {TED: A Tolerant Edit Distance for segmentation evaluation.}, journal = {Methods}, volume = {115}, year = {2017}, month = {2017 Jan 17}, pages = {119-27}, abstract = {

In this paper, we present a novel error measure to compare a computer-generated segmentation of images or volumes against ground truth. This measure, which we call Tolerant Edit Distance (TED), is motivated by two observations that we usually encounter in biomedical image processing: (1) Some errors, like small boundary shifts, are tolerable in practice. Which errors are tolerable is application dependent and should be explicitly expressible in the measure. (2) Non-tolerable errors have to be corrected manually. The effort needed to do so should be reflected by the error measure. Our measure is the minimal weighted sum of split and merge operations to apply to one segmentation such that it resembles another segmentation within specified tolerance bounds. This is in contrast to other commonly used measures like Rand index or variation of information, which integrate small, but tolerable, differences. Additionally, the TED provides intuitive numbers and allows the localization and classification of errors in images or volumes. We demonstrate the applicability of the TED on 3D segmentations of neurons in electron microscopy images where topological correctness is arguable more important than exact boundary locations. Furthermore, we show that the TED is not just limited to evaluation tasks. We use it as the loss function in a max-margin learning framework to find parameters of an automatic neuron segmentation algorithm. We show that training to minimize the TED, i.e., to minimize crucial errors, leads to higher segmentation accuracy compared to other learning methods.

}, doi = {10.1016/j.ymeth.2016.12.013}, author = {Funke, Jan and Klein, Jonas and Moreno-Noguer, Francesc and Cardona, Albert and Cook, Matthew} } @article {47560, title = {Structured learning of assignment models for neuron reconstruction to minimize topological errors.}, journal = {IEEE 13th International Symposium on Biomedical Imaging (ISBI)}, year = {2016}, month = {2016 Ap 13}, pages = {607-11}, abstract = {

Structured learning provides a powerful framework for empirical risk minimization on the predictions of structured models. It allows end-to-end learning of model parameters to minimize an application specific loss function. This framework is particularly well suited for discrete optimization models that are used for neuron reconstruction from anisotropic electron microscopy (EM) volumes. However, current methods are still learning unary potentials by training a classifier that is agnostic about the model it is used in. We believe the reason for that lies in the difficulties of (1) finding a representative training sample, and (2) designing an application specific loss function that captures the quality of a proposed solution. In this paper, we show how to find a representative training sample from human generated ground truth, and propose a loss function that is suitable to minimize topological errors in the reconstruction. We compare different training methods on two challenging EM-datasets. Our structured learning approach shows consistently higher reconstruction accuracy than other current learning methods.

}, doi = {10.1109/ ISBI.2016.7493341}, author = {Funke, Jan and Klein, Jonas and Moreno-Noguer, Francesc and Cardona, Albert and Cook, Matthew} } @conference {5685, title = {Efficient automatic 3D-reconstruction of branching neurons from EM data.}, booktitle = {Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on}, year = {2012}, month = {2012}, abstract = {

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.

}, author = {Funke, Jan and Andres, Bjoern and Hamprecht, Fred and Cardona, Albert and Cook, Matthew} }