Neuronal Subcompartment Classification and Merge Error Correction

Li, Hanyu; Jain, Viren; Li, Peter Hawley

doi:10.1007/978-3-030-59722-1_9

Cited by 14 publications

(13 citation statements)

References 23 publications

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“…2E). A classification model predicted axon, dendrite, astrocyte, soma, cilium, and axon initial segment classes at skeleton node locations distributed throughout each cell (Li et al, 2020). Occasional agglomeration errors could produce merges between nearby objects, such as a passing axon and dendrite (Fig.…”

Section: Figure 1 Image Acquisition For the Human Brain Samplementioning

confidence: 99%

“…We trained deep networks to classify the cellular subcompartment or cell type for a subset of skeleton nodes (20% uniformly sampled) following the approach of Li et al (Li et al, 2020). The model architecture was a ResNet-18 (He et al 2015) with all convolutions extended to 3D and inputs of 129 x 129 x 129 voxels at 32 x 32 x 33 nm resolution centered on each skeleton node.…”

Section: Cellular Subcompartment Classification and Merge Error Correctionmentioning

confidence: 99%

“…The model outputs were probabilities for four classes: axon, dendrite, astrocyte, or soma, and models were trained via stochastic gradient descent with batch size 64 for up to 1.5 M steps. We built on the approach of Li et al (Li et al, 2020) to use subcompartment predictions to fix segmentation errors based on the observation that while FFN base segments are largely free of merge errors, occasionally in FFN agglomeration two base segments with inconsistent classes (e.g. axon versus dendrite) are erroneously merged.…”

Section: Cellular Subcompartment Classification and Merge Error Correctionmentioning

confidence: 99%

“…Prior to processing, the agglomeration graph for each object was modified to remove cycles by breaking an arbitrary edge of each cycle. We also simplified on Li et al (Li et al, 2020) by considering only the highest probability predicted class label for each node, rather than working with predicted class probabilities per node.…”

Section: Cellular Subcompartment Classification and Merge Error Correctionmentioning

confidence: 99%

“…Applying the same set of cuts to 20201916 resulted in segmentation 20201916c3. Because we removed somas first to avoid spurious suggested cuts at the soma / branch interface, we did not fix any agglomeration errors involving the soma cluster of base segments, although methods to address this were proposed for songbird data (Li et al, 2020).…”

Section: Cellular Subcompartment Classification and Merge Error Correctionmentioning

confidence: 99%

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A connectomic study of a petascale fragment of human cerebral cortex

Shapson-Coe

Januszewski

Berger

et al. 2021

Preprint

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We acquired a rapidly preserved human surgical sample from the temporal lobe of the cerebral cortex. We stained a 1 mm3 volume with heavy metals, embedded it in resin, cut more than 5000 slices at ~30 nm and imaged these sections using a high-speed multibeam scanning electron microscope. We used computational methods to render the three-dimensional structure of 50,000 cells, hundreds of millions of neurites and 130 million synaptic connections. The 1.3 petabyte electron microscopy volume, the segmented cells, cell parts, blood vessels, myelin, inhibitory and excitatory synapses, and 100 manually proofread cells are available to peruse online. Despite the incompleteness of the automated segmentation caused by split and merge errors, many interesting features were evident. Glia outnumbered neurons 2:1 and oligodendrocytes were the most common cell type in the volume. The E:I balance of neurons was 69:31%, as was the ratio of excitatory versus inhibitory synapses in the volume. The E:I ratio of synapses was significantly higher on pyramidal neurons than inhibitory interneurons. We found that deep layer excitatory cell types can be classified into subsets based on structural and connectivity differences, that chandelier interneurons not only innervate excitatory neuron initial segments as previously described, but also each others initial segments, and that among the thousands of weak connections established on each neuron, there exist rarer highly powerful axonal inputs that establish multi-synaptic contacts (up to ~20 synapses) with target neurons. Our analysis indicates that these strong inputs are specific, and allow small numbers of axons to have an outsized role in the activity of some of their postsynaptic partners.

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Section: Figure 1 Image Acquisition For the Human Brain Samplementioning

confidence: 99%

Section: Cellular Subcompartment Classification and Merge Error Correctionmentioning

confidence: 99%

Section: Cellular Subcompartment Classification and Merge Error Correctionmentioning

confidence: 99%

Section: Cellular Subcompartment Classification and Merge Error Correctionmentioning

confidence: 99%

Section: Cellular Subcompartment Classification and Merge Error Correctionmentioning

confidence: 99%

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