Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features

By Aurélien Lucchi, Kevin Smith, Radhakrishna Achanta, G. Knott, Pascal Fua. IEEE Transactions on Medical Imaging, 2012

295 citations recorded by the source corpus.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Structural Biology

View Paper: https://www.semanticscholar.org/paper/b7b544f1ae34eb28f17413729f25720492794f8c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Albert Cardona
2. Stephan Saalfeld
3. Mark Longair
4. David Stutz
5. J. Urgen Schmidhuber
6. Fred A. Hamprecht
7. Pascal Fua
8. Sabine Süsstrunk

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Andrew Zisserman
3. Ming-Hsuan Yang
4. Xiaogang Wang
5. Dacheng Tao
6. Trevor Darrell
7. Jitendra Malik
8. Alan L. Yuille

In Structural Biology

1. Stephan Saalfeld
2. Albert Cardona
3. Mark Longair

Citations in the corpus

• Quantitative neuroanatomy for connectomics in Drosophila (2015, background): “To express the polyadic nature of insect synapses (Meinertzhagen and O’Neil, 1991), connector nodes can have multiple postsynaptic “targets”, but only one presynaptic “source”.”
• Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images (2012, methodology): “Others have used off-the-shelf random forest classifiers to compute unary terms of neuron membranes [22], or SVMs to compute both unary and binary terms for segmenting mitochondria [28] [27].”
• SLIC Superpixels Compared to State-of-the-Art Superpixel Methods (2012, methodology): “In [19], this approach is extended to 3D image stacks, which can contain billions of voxels.”
• Microscopy Image Browser: A Platform for Segmentation and Analysis of Multidimensional Datasets (2016, methodology): “In some cases, the results can be improved when classification is combined with other methods, for example, the use of supervoxels with watershed [28] or graph-cut-based algorithms [29].”
• Asymmetric Cuts: Joint Image Labeling and Partitioning (2014, methodology): “In the past, automatic methods to segment mitochondria [28], synapses [26,8], as well as the segmentation of the volume into distinct neurons, e.”
• Superpixels: An evaluation of the state-of-the-art (2016, background): “However, we also note that by focusing on natural images some application 255 domains might not be represented well – these include for example specialized research areas such as medical imaging where superpixels are also commonly used [33, 34, 36, 35].”