134 citations · 370 across the 21 of their papers we have counts for
24 papers
A Fully Automatic Pipeline for 3D Dendrite Instance Segmentation in SBF-SEM
Zewen Zhuo, Ilya Belevich, Eija Jokitalo +2
Accurate three-dimensional (3D) reconstruction of individual dendrites in serial block-face scanning electron microscopy (SBF-SEM) is essential for quantifying structural plasticit…
SomaNet: Weakly Supervised Learning for Instance Soma Segmentation in 3D Electron Microscopy with Partial Annotations
Mohammad Khateri, Morteza Ghahremani, Jussi Tohka +1
Soma instance segmentation, i.e., identifying and delineating individual cell somas as distinct instances, is crucial for cellular analysis and connectomic reconstruction. Three-di…
Cross-Dataset Linkage of Brain MRI using Image Similarity Measures
Gaurang Sharma, Harri Polonen, Juha Pajula +2
Head magnetic resonance imaging (MRI) data are routinely collected and shared for research under strict regulatory frameworks that require the removal of direct identifiers prior t…
USFetal: Tools for Fetal Brain Ultrasound Compounding
Mohammad Khateri, Morteza Ghahremani, Sergio Valencia +5
Ultrasound offers a safe, cost-effective, and widely accessible technology for fetal brain imaging, making it especially suitable for routine clinical use. However, it suffers from…
MRI Super-Resolution with Deep Learning: A Comprehensive Survey
Mohammad Khateri, Serge Vasylechko, Morteza Ghahremani +9
High-resolution (HR) magnetic resonance imaging (MRI) is crucial for many clinical and research applications. However, achieving it remains costly and constrained by technical trad…
Federated Learning for Predicting Mild Cognitive Impairment to Dementia Conversion
Gaurang Sharma, Elaheh Moradi, Juha Pajula +2
Dementia is a progressive condition that impairs an individual's cognitive health and daily functioning, with mild cognitive impairment (MCI) often serving as its precursor. The pr…
Unified Bayesian representation for high-dimensional multi-modal biomedical data for small-sample classification
Albert Belenguer-Llorens, Carlos Sevilla-Salcedo, Jussi Tohka +1
We present BALDUR, a novel Bayesian algorithm designed to deal with multi-modal datasets and small sample sizes in high-dimensional settings while providing explainable solutions.…
Segment Anything for Dendrites from Electron Microscopy
Zewen Zhuo, Ilya Belevich, Ville Leinonen +4
Segmentation of cellular structures in electron microscopy (EM) images is fundamental to analyzing the morphology of neurons and glial cells in the healthy and diseased brain tissu…
No-Clean-Reference Image Super-Resolution: Application to Electron Microscopy
Mohammad Khateri, Morteza Ghahremani, Alejandra Sierra +1
The inability to acquire clean high-resolution (HR) electron microscopy (EM) images over a large brain tissue volume hampers many neuroscience studies. To address this challenge, w…
Optimizing Feature Selection for Binary Classification with Noisy Labels: A Genetic Algorithm Approach
Vandad Imani, Elaheh Moradi, Carlos Sevilla-Salcedo +2
Feature selection in noisy label scenarios remains an understudied topic. We propose a novel genetic algorithm-based approach, the Noise-Aware Multi-Objective Feature Selection Gen…
Convolutional Neural Networks for Automatic Detection of Intact Adenovirus from TEM Imaging with Debris, Broken and Artefacts Particles
Olivier Rukundo, Andrea Behanova, Riccardo De Feo +3
Regular monitoring of the primary particles and purity profiles of a drug product during development and manufacturing processes is essential for manufacturers to avoid product var…
Volume electron microscopy in injured rat brain validates white matter microstructure metrics from diffusion MRI
Ricardo Coronado-Leija, Ali Abdollahzadeh, Hong-Hsi Lee +8
Biophysical modeling of diffusion MRI (dMRI) offers the exciting potential of bridging the gap between the macroscopic MRI resolution and microscopic cellular features, effectively…
Self-Supervised Super-Resolution Approach for Isotropic Reconstruction of 3D Electron Microscopy Images from Anisotropic Acquisition
Mohammad Khateri, Morteza Ghahremani, Alejandra Sierra +1
Three-dimensional electron microscopy (3DEM) is an essential technique to investigate volumetric tissue ultra-structure. Due to technical limitations and high imaging costs, sample…
Multi-Objective Genetic Algorithm for Multi-View Feature Selection
Vandad Imani, Carlos Sevilla-Salcedo, Elaheh Moradi +2
Multi-view datasets offer diverse forms of data that can enhance prediction models by providing complementary information. However, the use of multi-view data leads to an increase…
Sauron U-Net: Simple automated redundancy elimination in medical image segmentation via filter pruning
Juan Miguel Valverde, Artem Shatillo, Jussi Tohka
We introduce Sauron, a filter pruning method that eliminates redundant feature maps of convolutional neural networks (CNNs). Sauron optimizes, jointly with the loss function, a reg…
Adversarial Distortion Learning for Medical Image Denoising
Morteza Ghahremani, Mohammad Khateri, Alejandra Sierra +1
We present a novel adversarial distortion learning (ADL) for denoising two- and three-dimensional (2D/3D) biomedical image data. The proposed ADL consists of two auto-encoders: a d…
What Does FEXI Measure?
Mohammad Khateri, Marco Reisert, Alejandra Sierra +2
Filter-exchange imaging (FEXI) has already been utilized in several biomedical studies for evaluating the permeability of cell membranes. The method relies on suppressing the extra…
gACSON software for automated segmentation and morphology analyses of myelinated axons in 3D electron microscopy
Andrea Behanova, Ali Abdollahzadeh, Ilya Belevich +3
Background and Objective: Advances in electron microscopy (EM) now allow three-dimensional (3D) imaging of hundreds of micrometers of tissue with nanometer-scale resolution, provid…
Automatic cerebral hemisphere segmentation in rat MRI with lesions via attention-based convolutional neural networks
Juan Miguel Valverde, Artem Shatillo, Riccardo de Feo +1
We present MedicDeepLabv3+, a convolutional neural network that is the first completely automatic method to segment cerebral hemispheres in magnetic resonance (MR) volumes of rats…
Region-wise Loss for Biomedical Image Segmentation
Juan Miguel Valverde, Jussi Tohka
We propose Region-wise (RW) loss for biomedical image segmentation. Region-wise loss is versatile, can simultaneously account for class imbalance and pixel importance, and it can b…
Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review
Juan Miguel Valverde, Vandad Imani, Ali Abdollahzadeh +4
Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks to improve generalization in the tasks of interest. In MRI, transfer le…
Evaluation of machine learning algorithms for Health and Wellness applications: a tutorial
Jussi Tohka, Mark van Gils
Research on decision support applications in healthcare, such as those related to diagnosis, prediction, treatment planning, etc., have seen enormously increased interest recently.…
RatLesNetv2: A Fully Convolutional Network for Rodent Brain Lesion Segmentation
Juan Miguel Valverde, Artem Shatillo, Riccardo de Feo +3
We present a fully convolutional neural network (ConvNet), named RatLesNetv2, for segmenting lesions in rodent magnetic resonance (MR) brain images. RatLesNetv2 architecture resemb…
Automatic Rodent Brain MRI Lesion Segmentation with Fully Convolutional Networks
Juan Miguel Valverde, Artem Shatillo, Riccardo de Feo +3
Manual segmentation of rodent brain lesions from magnetic resonance images (MRIs) is an arduous, time-consuming and subjective task that is highly important in pre-clinical researc…