NeuroMorph: a toolset for the morphometric analysis and visualization of 3D models derived from electron microscopy image stacks.

Neuroinformatics

Computer Vision Lab, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland,

Published: January 2015

Serialelectron microscopy imaging is crucial for exploring the structure of cells and tissues. The development of block face scanning electron microscopy methods and their ability to capture large image stacks, some with near isotropic voxels, is proving particularly useful for the exploration of brain tissue. This has led to the creation of numerous algorithms and software for segmenting out different features from the image stacks. However, there are few tools available to view these results and make detailed morphometric analyses on all, or part, of these 3D models. We have addressed this issue by constructing a collection of software tools, called NeuroMorph, with which users can view the segmentation results, in conjunction with the original image stack, manipulate these objects in 3D, and make measurements of any region. This approach to collecting morphometric data provides a faster means of analysing the geometry of structures, such as dendritic spines and axonal boutons. This bridges the gap that currently exists between rapid reconstruction techniques, offered by computer vision research, and the need to collect measurements of shape and form from segmented structures that is currently done using manual segmentation methods.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4303738PMC
http://dx.doi.org/10.1007/s12021-014-9242-5DOI Listing

Publication Analysis

Top Keywords

image stacks
12
electron microscopy
8
neuromorph toolset
4
toolset morphometric
4
morphometric analysis
4
analysis visualization
4
visualization models
4
models derived
4
derived electron
4
image
4

Similar Publications

Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning.

Comput Biol Med

January 2025

Emerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia; Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia. Electronic address:

- Brain tumors (BT), both benign and malignant, pose a substantial impact on human health and need precise and early detection for successful treatment. Analysing magnetic resonance imaging (MRI) image is a common method for BT diagnosis and segmentation, yet misdiagnoses yield effective medical responses, impacting patient survival rates. Recent technological advancements have popularized deep learning-based medical image analysis, leveraging transfer learning to reuse pre-trained models for various applications.

View Article and Find Full Text PDF

A Novel Ensemble Meta-Model for Enhanced Retinal Blood Vessel Segmentation Using Deep Learning Architectures.

Biomedicines

January 2025

Perception, Robotics, and Intelligent Machines Lab (PRIME), Department of Computer Science, Université de Moncton, Moncton, NB E1A 3E9, Canada.

Retinal blood vessel segmentation plays an important role in diagnosing retinal diseases such as diabetic retinopathy, glaucoma, and hypertensive retinopathy. Accurate segmentation of blood vessels in retinal images presents a challenging task due to noise, low contrast, and the complex morphology of blood vessel structures. In this study, we propose a novel ensemble learning framework combining four deep learning architectures: U-Net, ResNet50, U-Net with a ResNet50 backbone, and U-Net with a transformer block.

View Article and Find Full Text PDF

Nonlinear homogenised finite element (hFE) models can accurately predict stiffness and strength of ultra-distal sections of the radius and tibia using in vivo HR-pQCT images. Recent findings showed good stiffness prediction at these distal sections but a limited ability to reproduce experimental strain localisation. The coarseness of voxel-based meshes reduces the computational effort at the cost of heavily simplifying the underlying geometry of the cortex, the gradient of material properties, and the resulting strain distribution.

View Article and Find Full Text PDF

Adsorption behaviors are typically examined through adsorption isotherms, which measure the average adsorption amount as a function of partial pressure or time. However, this method is incapable of identifying inhomogeneities across the adsorbent, which may occur in the presence of strong intermolecular interactions of the adsorbate. In this study, we visualize the adsorption of molecular iodine (I) in the metal-organic framework material MFM-300(Sc) using high-resolution scanning transmission electron microscopy (STEM).

View Article and Find Full Text PDF

Scanner-based real-time three-dimensional brain + body slice-to-volume reconstruction for T2-weighted 0.55-T low-field fetal magnetic resonance imaging.

Pediatr Radiol

January 2025

Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, SE1 7EH, UK.

Background: Motion correction methods based on slice-to-volume registration (SVR) for fetal magnetic resonance imaging (MRI) allow reconstruction of three-dimensional (3-D) isotropic images of the fetal brain and body. However, all existing SVR methods are confined to research settings, which limits clinical integration. Furthermore, there have been no reported SVR solutions for low-field 0.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!