@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 {48367, title = {Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.}, journal = {Bioinformatics (Oxford, England)}, volume = {33}, year = {2017}, month = {2017 Mar 29}, pages = {2424-6}, abstract = {

Summary: State-of-the-art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This processis time-consuming and often a major bottleneck in the evaluation pipeline. To overcome this problem, we have introduced the Trainable Weka Segmentation (TWS), a machine learning tool that leveragesa limited number of manual annotations in order to train a classifier and segment the remaining dataautomatically. In addition, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to employ user-designed image features or classifiers.

Availability and Implementation: TWS is distributed as open-source software as part of the Fiji image processing distribution of ImageJ at http://imagej.net/Trainable_Weka_Segmentation.

Contact: ignacio.arganda@ehu.eus.

Supplementary information: Supplementary data are available at Bioinformatics online.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btx180}, author = {Arganda-Carreras, Ignacio and Kaynig, Verena and Rueden, Curtis and Eliceiri, Kevin W and Schindelin, Johannes and Cardona, Albert and Seung, H Sebastian} } @article {5684, title = {TrakEM2 software for neural circuit reconstruction.}, journal = {PLoS One}, volume = {7}, year = {2012}, month = {2012}, pages = {e38011}, abstract = {

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.

}, keywords = {Animals, Models, Nerve Net, Neuroimaging, Neurological, Neurons, Software, Synapses, Synaptic Transmission}, issn = {1932-6203}, doi = {10.1371/journal.pone.0038011}, author = {Cardona, Albert and Saalfeld, Stephan and Schindelin, Johannes and Arganda-Carreras, Ignacio and Preibisch, Stephan and Longair, Mark and Tomancak, Pavel and Hartenstein, Volker and Douglas, Rodney J} } @article {46785, title = {TrakEM2 software for neural circuit reconstruction.}, journal = {PLoS One}, volume = {7}, year = {2012}, month = {2012}, pages = {e38011}, abstract = {

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.

}, keywords = {Animals, Models, Neurological, Nerve Net, Neuroimaging, Neurons, Software, Synapses, Synaptic Transmission}, issn = {1932-6203}, doi = {10.1371/journal.pone.0038011}, author = {Cardona, Albert and Saalfeld, Stephan and Schindelin, Johannes and Arganda-Carreras, Ignacio and Preibisch, Stephan and Longair, Mark and Tomancak, Pavel and Hartenstein, Volker and Douglas, Rodney J} }