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/PMC4303738 | PMC |
http://dx.doi.org/10.1007/s12021-014-9242-5 | DOI Listing |
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 PDFBiomedicines
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 PDFBone
January 2025
ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
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 PDFJ Am Chem Soc
January 2025
Center for Electron Microscopy, South China University of Technology, Guangzhou 511436, China.
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 PDFPediatr 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 PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!