Medical Image Segmentation Techniques

Quality control strategies for brain MRI segmentation and parcellation: practical approaches and recommendations - insights from The Maastricht Study

This study compares manual and automated quality control strategies for brain MRI segmentation, finding that Euler numbers and MRIQC provide reliable and efficient methods to reduce measurement error.

Reliability and sensitivity of two whole-brain segmentation approaches included in FreeSurfer - ASEG and SAMSEG

This paper compares two whole-brain segmentation methods, ASEG and SAMSEG, in FreeSurfer, finding SAMSEG provides more reliable and sensitive measurements for detecting longitudinal changes and Alzheimer's disease-related atrophy.

MACHINE LEARNING FRAMEWORK FOR FULLY AUTOMATIC QUALITY CHECKING OF RIGID AND AFFINE REGISTRATIONS IN BIG DATA BRAIN MRI

The paper proposes a machine learning framework for automatically checking the quality of rigid and affine brain MRI registrations using similarity functions as features, achieving high F1-scores on a dataset of 215 autism subjects.

MRI Brain Templates of the Male Yucatan Minipig

This paper creates an average brain template for male Yucatan minipigs using non-linear registration and evaluates its performance compared to linear methods and another minipig species template.

Automated analysis of rabbit knee calcified cartilage morphology using micro-computed tomography and deep learning segmentation

The paper develops a deep learning segmentation method for analyzing calcified cartilage thickness using micro-CT and compares it to histology across rabbit knee regions.

Deep-learning based fully automatic segmentation of the globus pallidus interna and externa using ultra-high 7 Tesla MRI

This paper introduces GP-net, a deep-learning model for automatic segmentation of the globus pallidus interna and externa using 7 Tesla MRI, showing improved results over atlas-based methods.

Robust T1 MRI cortical surface pipeline for neonatal brain and systematic evaluation using multi-site MRI datasets

This paper introduces a robust T1 MRI cortical surface pipeline for neonatal brains and evaluates it across multiple datasets, showing high accuracy and reproducibility.

Portable framework to deploy deep learning segmentation models for medical images

This paper presents a framework for deploying deep learning segmentation models for medical images across operating systems and languages, enabling clinical use and multi-institutional studies.

NiftyTorch: A Deep Learning framework for NeuroImaging

NiftyTorch provides a deep learning framework for neuroimaging tasks, aiming to simplify 3D deep learning for non-experts.

Harmonised segmentation of neonatal brain MRI

This paper applies unsupervised domain adaptation techniques to segment neonatal brain MRI data from a preterm population, improving segmentation accuracy and enabling analysis of cortical thickness and language outcomes.

Constructing and Optimizing 3D Atlases From 2D Data With Application to the Developing Mouse Brain

The paper develops a computational method to construct 3D atlases from 2D data, improving accuracy and reducing artifacts in mouse brain development studies.

Improved segmentation of the intracranial and ventricular volumes in populations with cerebrovascular lesions and atrophy using 3D CNNs

The paper introduces CNN-based models for segmenting intracranial and ventricular volumes in neuroimaging data, showing improved accuracy and efficiency compared to existing methods.

Fast Variational Alignment of non-flat 1D Displacements for Applications in Neuroimaging

The paper introduces a variational method for aligning 1D signals with non-flat displacements, achieving state-of-the-art alignment quality and speed in neuroimaging applications.

Anatomical modeling of brain vasculature in two-photon microscopy by generalizable deep learning

This paper develops a deep learning method for segmenting brain vasculature from two-photon microscopy images, achieving faster computation and better generalization across imaging systems.

Automated joint skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases

The paper introduces MU-Net, a convolutional neural network for automated skull-stripping and segmentation in mouse brain MRI, achieving high accuracy and speed.

Replicability, repeatability, and long-term reproducibility of cerebellar morphometry

This study evaluates the replicability, repeatability, and long-term reproducibility of three cerebellar morphometry tools using MRI data from different studies.

Fully Automated Detection of Paramagnetic Rims in Multiple Sclerosis Lesions on 3T Susceptibility-Based MR Imaging

This paper develops an automated method to detect paramagnetic rim lesions in MS using MRI data and radiomic features with a random forest classifier.

Approach for Semi-Automated Measurement of Fiber Diameter in Murine and Canine Skeletal Muscle

MyoSAT is an image-processing pipeline that improves automated measurement of muscle fiber diameter in murine and canine tissue under non-optimal staining conditions.

Multi-contrast Anatomical Subcortical Structures Parcellation

This paper introduces a new open-source algorithm for automatically parcellating subcortical structures in MRI data, validated against experts and tested on 17 structures.

Cortical thickness estimation in individuals with cerebral small vessel disease, focal atrophy, and chronic stroke lesions.

This study improves FreeSurfer's cortical thickness estimation in patients with cerebral small vessel disease and stroke by applying correction procedures, leading to better segmentation and cognitive association results.

Automatic Reconstruction of Cerebellar Cortex from Standard MRI Using Diffeomorphic Registration of a High-Resolution Template (ARCUS)

This paper develops a method to reconstruct the cerebellar cortex from standard MRI using diffeomorphic registration of a high-resolution template, enabling accurate topographic reconstructions for neuroimaging applications.

A pipeline to characterize local cortical folds by mapping them to human-interpretable shapes

The paper introduces a pipeline to automate manual mapping of cortical folds to human-interpretable shapes, demonstrating it on detecting discontinuities in the orbitofrontal cortex with high accuracy.

U-Net Model for Brain Extraction: Trained on Humans for Transfer to Non-human Primates

The paper develops a transfer-learning U-Net model for brain extraction in non-human primates using human data, achieving better performance than traditional methods and popular MRI packages.

Perivascular Space Semi-Automatic Segmentation (PVSSAS): A Tool for Segmenting, Viewing and Editing Perivascular Spaces

The paper introduces PVSSAS, a tool for semi-automated segmentation of perivascular spaces, showing high agreement with manual methods.

Automated Segmentation of Amyloid-β Stained Whole Slide Images of Brain Tissue

This paper develops an automated segmentation pipeline for GM/WM in brain WSIs using CNNs and post-processing, achieving high IoU scores and reducing manual effort.

Testing a convolutional neural network-based hippocampal segmentation method in a stroke population

This study compares a new convolutional neural network-based hippocampal segmentation method (Hippodeep) with existing methods on stroke MRIs, finding Hippodeep more accurate in quality control and spatial accuracy.

BISON: Brain tISue segmentatiON pipeline using T1-weighted magnetic resonance images and a random forests classifier

BISON improves brain tissue segmentation using T1-weighted MRIs and random forests, outperforming existing methods in accuracy and reliability across age and scanner variations.

Depth relationships and measures of tissue thickness in dorsal midbrain

This study develops depth metrics to characterize tissue thickness and depth relationships in the human dorsal midbrain using functional MRI data.

A series of five population-specific Indian brain templates and atlases spanning ages 6 to 60 years

The paper develops and validates age-specific Indian brain templates and atlases for spatial normalization of MRI data across ages 6-60 years.

A Large-scale Comparison of Cortical and Subcortical Structural Segmentation Methods in Alzheimer's Disease: a Statistical Approach

This study compares cortical and subcortical segmentation methods in Alzheimer's disease using MRI data, finding that HIPS, volBrain, and CAT perform well while BrainSuite shows misclassification issues.