Medical Image Segmentation Techniques

Multimodal Image Normalisation Tool (MINT) for the Adolescent Brain and Cognitive Development study: the MINT ABCD Atlas

The paper introduces MINT, a tool for multimodal MRI registration, and presents an ABCD atlas for whole-brain analysis, showing it outperforms existing methods in speed while maintaining accuracy.

MiniVess: A dataset of rodent cerebrovasculature from in vivo multiphoton fluorescence microscopy imaging

The paper introduces MiniVess, a rodent cerebrovasculature dataset for testing machine learning tools in biomedical image analysis.

Automated Analysis of Low-Field Brain MRI in Cerebral Malaria

This paper develops an automated framework for analyzing low-field MRI scans to detect biomarkers of severe brain swelling in cerebral malaria.

Automated 3D reconstruction of the fetal thorax in the standard atlas space from motion-corrupted MRI stacks for 21-36 weeks GA range

This paper proposes an automated pipeline for reconstructing fetal thorax MRI from motion-corrupted stacks across 21-36 weeks GA, using 3D CNNs and landmark-guided pose estimation.

Feasibility of FreeSurfer processing for T1-weighted brain images of 5-year-olds: semiautomated protocol of FinnBrain Neuroimaging Lab

This paper evaluates the feasibility of using FreeSurfer for processing T1-weighted brain images of 5-year-olds, showing minor effects of manual edits and recommending quality assessment for reliable segmentation.

A New Approach to Symmetric Registration of Longitudinal Structural MRI of the Human Brain

This paper introduces ATRA, a symmetric registration algorithm for longitudinal brain MRI scans, achieving precise alignment without interpolation bias or reference volume.

FollicleFinder: automated three-dimensional segmentation of human ovarian follicles

FollicleFinder automates 3D segmentation of human ovarian follicles and measures morphological properties like diameter, surface area, and volume.

Subcortical Segmentation of the Fetal Brain in 3D Ultrasound using Deep Learning

The paper develops a CNN for automated segmentation of fetal brain structures in 3D ultrasound using few-shot learning, achieving performance close to intra-observer variability with minimal manual annotations.

Spatiotemporal 3D image registration for mesoscale studies of brain development.

This paper develops a multi-step algorithm for registering perinatal mouse brain images across developmental stages, achieving accurate alignment and synchronization.

Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation

This paper introduces a Bayesian 3D U-Net for WMH segmentation that provides uncertainty estimates and is robust to imaging variations, achieving high accuracy and efficiency.

Automated Human Habenula Segmentation from T1-weighted Magnetic Resonance Images using V-Net

This paper develops a deep learning model for automated segmentation of the human Habenula from T1-weighted MRI, achieving high accuracy and robustness across different MRI protocols.

3D MRI atlases of congenital aortic arch anomalies and normal fetal heart: application to automated multi-label segmentation

The paper creates 3D MRI atlases of normal and abnormal fetal aortic arch anatomy and evaluates deep learning for automated multi-label segmentation.

Fast Medical Image Auto-segmentation for Bleeding Gastric Tissue Detection based on Deep DuS-KFCM Clustering

This paper presents a hybrid neuro-fuzzy system for automated gastric bleeding lesion segmentation in endoscopy images, achieving high accuracy and robustness against noise.

Deep learning-based auto-segmentation of swallowing and chewing structures

This paper develops deep learning models to automate segmentation of swallowing and chewing structures in H&N CT scans, achieving high accuracy and clinical utility.

Performance of three freely available methods for extracting white matter hyperintensities: FreeSurfer, UBO Detector and BIANCA

The paper compares three WMH segmentation algorithms on MRI data, finding BIANCA and UBO Detector outperform FreeSurfer in accuracy and correlation with Fazekas scores.

Volumetric assessment and longitudinal changes of brain structures in formalinized Beagle brains

The paper assesses MRI-based volumetric changes in formalin-fixed Beagle brains over time and evaluates segmentation reproducibility across operators.

Benchmarking Geometric Deep Learning for Cortical Segmentation and Neurodevelopmental Phenotype Prediction

This paper benchmarks geometric and traditional deep learning models for cortical segmentation and neurodevelopmental phenotype prediction using neonatal cortical surface data.

Charting the human amygdala development across childhood and adolescence: Manual and automatic segmentation

This study compares manual and automatic amygdala segmentation methods in children and adolescents, finding systematic volume differences and method-specific growth patterns.

Application of the Anatomical Fiducials Framework to a Clinical Dataset of Patients with Parkinsons Disease

This study applies the anatomical fiducials framework to Parkinson's Disease clinical MRI data, showing it can measure registration errors and detect morphometric biomarkers of the disease.

Automated skull stripping in mouse fMRI analysis using 3D U-Net

This paper introduces a 3D U-Net method for automated skull stripping in mouse fMRI, achieving high accuracy and matching manual segmentation results.

CEREBRUM-7T: Fast and Fully-volumetric Brain Segmentation of 7 Tesla MR Volumes

CEREBRUM-7T is a fast, fully-volumetric brain segmentation method for 7T MRI that outperforms its training labels and is suitable for neuroimaging studies.

3D U-Net improves automatic brain extraction for isotropic rat brain MRI data

This paper introduces a 3D U-Net framework for automatic brain extraction in rat MRI data, showing improved performance over existing methods.

Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images

This study compares domain adaptation techniques to improve white matter hyperintensity segmentation across different MRI datasets, finding that unsupervised domain adversarial training performs best.

DARQ: Deep learning of quality control for stereotaxic registration of human brain MRI

DARQ is a deep learning method for automated quality control of stereotaxic MRI registration, achieving high accuracy comparable to manual assessment.

Automatic segmentation of the rat brain hippocampus in MRI after traumatic brain injury

The paper develops a CNN-based method for automated hippocampus segmentation in rat MRI images post-TBI, showing superior performance compared to registration-based methods.

Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images

This paper proposes a triplanar ensemble U-Net model for WMH segmentation on MR images, achieving robust performance comparable to existing methods.

3D segmentation of perivascular spaces on T1-weighted 3 Tesla MR images with a convolutional autoencoder and a U-shaped neural network

This paper develops a deep learning algorithm for 3D segmentation of perivascular spaces in MRI data, achieving high accuracy and demonstrating interoperability across scanners.

Radius-Optimized Efficient Template Matching for Lesion Detection from Brain Images

This paper introduces a radius-optimized template matching method for brain lesion detection, reducing computational complexity while maintaining performance comparable to existing techniques.

Mapping fetal brain development based on automated segmentation and 4D brain atlasing

This paper develops an automated pipeline for fetal brain MRI analysis, including extraction, super-resolution, and 4D atlasing, to map fetal brain development in a Chinese population.

Subcortical brain segmentation in 5-year-old children: validation of FSL-FIRST and FreeSurfer against manual segmentation

This study compares FSL-FIRST and FreeSurfer automated subcortical segmentation tools against manual segmentation in 5-year-olds, finding FSL-FIRST more accurate for most structures but with overestimation issues for small ones.