gACSON software for automated segmentation and morphology analyses of
myelinated axons in 3D electron microscopy
- URL: http://arxiv.org/abs/2112.06476v1
- Date: Mon, 13 Dec 2021 08:17:15 GMT
- Title: gACSON software for automated segmentation and morphology analyses of
myelinated axons in 3D electron microscopy
- Authors: Andrea Behanova, Ali Abdollahzadeh, Ilya Belevich, Eija Jokitalo,
Alejandra Sierra, Jussi Tohka
- Abstract summary: We introduce a freely available gACSON software for visualization, segmentation, assessment, and morphology analysis of myelinated axons in 3D-EM volumes.
gACSON automatically segments the intra-axonal space of myelinated axons and their corresponding myelin sheaths.
It analyzes the morphology of myelinated axons, such as axonal diameter, axonal eccentricity, myelin thickness, or g-ratio.
- Score: 55.78588835407174
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Background and Objective: Advances in electron microscopy (EM) now allow
three-dimensional (3D) imaging of hundreds of micrometers of tissue with
nanometer-scale resolution, providing new opportunities to study the
ultrastructure of the brain. In this work, we introduce a freely available
gACSON software for visualization, segmentation, assessment, and morphology
analysis of myelinated axons in 3D-EM volumes of brain tissue samples. Methods:
The gACSON software is equipped with a graphical user interface (GUI). It
automatically segments the intra-axonal space of myelinated axons and their
corresponding myelin sheaths and allows manual segmentation, proofreading, and
interactive correction of the segmented components. gACSON analyzes the
morphology of myelinated axons, such as axonal diameter, axonal eccentricity,
myelin thickness, or g-ratio. Results: We illustrate the use of gACSON by
segmenting and analyzing myelinated axons in six 3D-EM volumes of rat
somatosensory cortex after sham surgery or traumatic brain injury (TBI). Our
results suggest that the equivalent diameter of myelinated axons in
somatisensory cortex was decreased in TBI animals five months after the injury.
Conclusions: Our results indicate that gACSON is a valuable tool for
visualization, segmentation, assessment, and morphology analysis of myelinated
axons in 3D-EM volumes. gACSON is freely available at
https://github.com/AndreaBehan/g-ACSON under the MIT license.
Related papers
- High-resolution segmentations of the hypothalamus and its subregions for training of segmentation models [1.0486773259892048]
HELM, Hypothalamic ex vivo Label Maps is a dataset composed of label maps built from publicly available ultra-high resolution ex vivo MRI from 10 whole hemispheres.
We provide a combination of manual labels for the hypothalamic regions and automated segmentations for the rest of the brain, and mirrored to simulate entire brains.
arXiv Detail & Related papers (2024-06-27T19:16:57Z) - Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing [72.45257414889478]
We aim to reduce human workload by predicting connectivity between over-segmented neuron pieces.
We first construct a dataset, named FlyTracing, that contains millions of pairwise connections of segments expanding the whole fly brain.
We propose a novel connectivity-aware contrastive learning method to generate dense volumetric EM image embedding.
arXiv Detail & Related papers (2024-01-05T19:45:12Z) - AxonCallosumEM Dataset: Axon Semantic Segmentation of Whole Corpus
Callosum cross section from EM Images [1.433758865948252]
AxonCallosumEM dataset comprises a 1.83 times 5.76mm EM image captured from the corpus callosum of the Rett Syndrome (RTT) mouse model.
We meticulously proofread over 600,000 patches at a resolution of 1024 times 1024, thus providing a comprehensive ground truth for myelinated axons and myelin sheaths.
arXiv Detail & Related papers (2023-07-05T17:38:01Z) - CNN-based fully automatic wrist cartilage volume quantification in MR
Image [55.41644538483948]
The U-net convolutional neural network with additional attention layers provides the best wrist cartilage segmentation performance.
The error of cartilage volume measurement should be assessed independently using a non-MRI method.
arXiv Detail & Related papers (2022-06-22T14:19:06Z) - AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions [22.87130178598667]
AxonEM dataset consists of two 30x30x30 um3 EM image volumes from the human and mouse cortex.
We thoroughly proofread over 18,000 axon instances to provide dense 3D axon instance segmentation.
We densely annotate nine ground truth subvolumes for training, per each data volume.
arXiv Detail & Related papers (2021-07-12T14:24:03Z) - Single Neuron Segmentation using Graph-based Global Reasoning with
Auxiliary Skeleton Loss from 3D Optical Microscope Images [30.539098538610013]
We present an end-to-end segmentation network by jointly considering the local appearance and the global geometry traits.
The evaluation results on the Janelia dataset from the BigNeuron project demonstrate that our proposed method exceeds the counterpart algorithms in performance.
arXiv Detail & Related papers (2021-01-22T01:27:14Z) - Weakly-supervised Learning For Catheter Segmentation in 3D Frustum
Ultrasound [74.22397862400177]
We propose a novel Frustum ultrasound based catheter segmentation method.
The proposed method achieved the state-of-the-art performance with an efficiency of 0.25 second per volume.
arXiv Detail & Related papers (2020-10-19T13:56:22Z) - Deep Negative Volume Segmentation [60.44793799306154]
We propose a new angle to the 3D segmentation task: segment empty spaces between all the tissues surrounding the object.
Our approach is an end-to-end pipeline that comprises a V-Net for bone segmentation.
We validate the idea on the CT scans in a 50-patient dataset, annotated by experts in maxillofacial medicine.
arXiv Detail & Related papers (2020-06-22T16:55:23Z) - Interpretation of 3D CNNs for Brain MRI Data Classification [56.895060189929055]
We extend the previous findings in gender differences from diffusion-tensor imaging on T1 brain MRI scans.
We provide the voxel-wise 3D CNN interpretation comparing the results of three interpretation methods.
arXiv Detail & Related papers (2020-06-20T17:56:46Z) - Self-Supervised Feature Extraction for 3D Axon Segmentation [7.181047714452116]
Existing learning-based methods to automatically trace axons in 3D brain imagery often rely on manually annotated segmentation labels.
We propose a self-supervised auxiliary task that utilizes the tube-like structure of axons to build a feature extractor from unlabeled data.
We demonstrate improved segmentation performance over the 3D U-Net model on both the SHIELD PVGPe dataset and the BigNeuron Project, single neuron Janelia dataset.
arXiv Detail & Related papers (2020-04-20T20:46:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.