An Integrated Micro- and Macroarchitectural Analysis of the Drosophila Brain by Computer-Assisted Serial Section Electron Microscopy

By Albert Cardona, Stephan Saalfeld, Stephan Preibisch, Benjamin Schmid, Anchi Cheng, Jim Pulokas, Pavel Tomancak, Volker Hartenstein. 2010

417 citations recorded by the source corpus.

A new software package allows for dense electron microscopy reconstructions of neuronal networks in the fruit fly brain, and reveals specific differences in microcircuits between insects and vertebrates.

Subfields: Cellular and Molecular Neuroscience, Ecology, Genetics, Computational Biology & Bioinformatics, Neuroscience

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Citations in the corpus

U-Net: Convolutional Networks for Biomedical Image Segmentation (2015, methodology): “The data set is provided by the EM segmentation challenge [14,1] that was started at ISBI 2012 and is still open for new contributions.”
Fast image scanning with deep max-pooling convolutional neural networks (2013, methodology): “For huge nets such as those winning the ISBI Electron Microscopy Segmentation Challenge [20, 21] (see Figure 2), our approach is in theory almost three orders of magnitude faster than a straightforward patch-based forward-propagation approach.”
Rotation Equivariant Vector Field Networks (2016, methodology): “This benchmark [1] involves segmentation of neuronal structures in electron microscope (EM) stacks [3].”
Fiji - an Open Source platform for biological image analysis (2012, methodology): “3e ) and quantification of their overlap by colocalization analysis. ssTEM reconstruction. ssTEM is experiencing a renaissance as a tool of choice for mapping brain micro-circuitry 32,33 .”
TI-POOLING: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks (2016, methodology): “The neuronal membrane segmentation dataset was used for ISBI 2012 challenge [4].”
Insect morphology in the age of phylogenomics: innovative techniques and its future role in systematics (2014, background): “Cardona et al. (2010) emphasized using it to visualize the 3D anatomical configuration of the brain of Drosophila on the level of individual neuronal processes and synapses.”
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation (2019, methodology): “1) Electron Microscopy (EM): The dataset is provided by the EM segmentation challenge [30] as a part of ISBI 2012.”
Volume electron microscopy for neuronal circuit reconstruction. (2012, neutral): “,12,13]) are cut in an ultramicrotome with a diamond knife (Figure 1a).”
EMCAD: Efficient Multi-Scale Convolutional Attention Decoding for Medical Image Segmentation (2024, methodology): “We resize images to 352 × 352 and use a multi-scale { 0.75, 1.0, 1.25 } training strategy with a gradient clip limit of 0.5 for ClinicDB [3], Kvasir [29], ColonDB [51], ETIS [51], BKAI [40], ISIC17 [15], and ISIC18 [15], while we resize images to 256 × 256 for BUSI [1], EM [6], and DSB18 [4].”
A fast learning algorithm for image segmentation with max-pooling convolutional networks (2013, methodology): “We use the public dataset of the ISBI 2102 Electron Microscopy Segmentation challenge [12].”
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation (2015, methodology): “EM dataset The EM dataset [12] is provided by the ISBI 2012 workshop on Segmentation of Neuronal Structures in EM Stacks [15].”
Human-Guided Learning for Probabilistic Logic Models (2018, methodology): “We use the Drosophila dataset (Cardona et al., 2010), which has 20 stacks of microscopic images of fruit flies ventral nerve cord.”
End-to-End Learning of Decision Trees and Forests (2019, methodology): “For this purpose, we train an oblique decision tree for a binary image segmentation task on the ISBI challenge dataset (Cardona et al. 2010).”
Democratising deep learning for microscopy with ZeroCostDL4Mic (2021, methodology): “For 2D segmentation, this was a neuronal membrane segmentation dataset from the ISBI challenge 2012 78,79 and for 3D segmentation from the mitochondrial segmentation dataset available from EPFL (https://www.ep ﬂ .ch/labs/cvlab/data/data-em/).”
Sharp U-Net: Depthwise Convolutional Network for Biomedical Image Segmentation (2021, methodology): “We demonstrate and analyze the performance of the proposed image segmentation model on four datasets: Lung Segmentation 2 , Data Science Bowl 2018 segmentation challence 3 , ISIC-2018, COVID-19 CT Segmentation 4 , ISBI-2012 [28], and CVC-ClinicDB [29].”
Semantic Image Segmentation with Contextual Hierarchical Models (2015, methodology): “This dataset contains 30 images from Drosophila ﬁrst instar larva ventral nerve cord (VNC) [21], [22] acquired using serial-section transmission electron microscopy [77], [78].”
Different classes of input and output neurons reveal new features in microglomeruli of the adult Drosophila mushroom body calyx (2012, background): “The Journal of Comparative Neurology | Research in Systems Neuroscience 2191 Nearly all synapses were divergent polyads, having more than a single postsynaptic partner beneath each presynaptic density, retaining a feature also seen in the larval mushroom body calyx (Cardona et al., 2010).”