Medical Image Segmentation Techniques

On the accuracy of image registration in portable low-field 3D brain MRI

This study evaluates image registration accuracy in portable low-field 3D brain MRI, finding that synthesis-based methods perform best despite acquisition artifacts.

Refined Myocardium Segmentation from CT Using a Hybrid-Fusion Transformer

This paper presents a Hybrid-Fusion Transformer model that refines myocardium segmentation in cardiac CT by combining CT images with semi-automatic labels to remove papillary muscles.

Multimodal Connectivity-based Cortical Segmentation with Graph Neural Networks

This paper evaluates Graph Neural Networks for brain cortex segmentation using structural MRI and diffusion MRI data, finding GAT models perform competitively with non-graph methods.

In Vivo Surface Reconstruction of Three Signal-defined Intracortical Layers Using 5T 3D FLAIR MR Imaging

This paper develops BrainMLSR, an automated framework to reconstruct three intracortical signal-defined layers from 5T 3D FLAIR MRI, showing high agreement with manual annotations and revealing regional morphometric differences in epilepsy.

Harmonized Protocol for Subfield Segmentation in the Hippocampal Body on High-Resolution in vivo MRI from the Hippocampal Subfields Group (HSG)

The paper develops a harmonized segmentation protocol for hippocampal subfields in MRI, improving cross-study comparisons and understanding of subfield structure and function.

Multiscale Segmentation using Hierarchical Phase-contrast Tomography and Deep Learning

The paper develops a multiscale segmentation pipeline using HiP-CT and deep learning to identify glomeruli in human kidneys across different resolutions.

FibroTrack: A Standalone Deep Learning Platform for Automated Fibrosis Quantification in Muscle and Cardiac Histology

FibroTrack automates fibrosis quantification in muscle and cardiac histology using deep learning, achieving high precision and agreement with pathologists.

Automated 3D Segmentation of Human Vagus Nerve Fascicles and Epineurium from Micro-Computed Tomography Images Using Anatomy-Aware Neural Networks

This paper introduces a 3D U-Net with an anatomy-aware loss function for automated segmentation of human vagus nerve fascicles and epineurium from microCT images, achieving high accuracy and reducing anatomical errors.

Fast segmentation with the NextBrain histological atlas

The paper introduces a fast segmentation tool for brain MRI scans using a hybrid method, achieving comparable accuracy to a Bayesian approach but with significantly reduced runtime.

SUITPy: A Python-based toolbox for the analysis of cerebellar functional and anatomical imaging data across the human lifespan

SUITPy improves cerebellar imaging analysis by isolating the cerebellum with a U-Net model, enhancing alignment and reducing contamination from surrounding cortical structures.

Deep learning to overcome human error and bias in electrode position extraction in tDCS-fMRI studies

The paper develops a deep learning model to automate electrode detection in tDCS-fMRI studies, outperforming manual and semi-automated methods in precision and reducing human bias.

Morphometry-based detection of deep learning faults in glomerular segmentation

This study uses shape analysis to detect and prioritize errors in deep learning-based glomerular segmentation, enabling more efficient correction by pathologists.

The MTL200: a surface-based, probabilistic atlas of the medial temporal lobe

The paper introduces the MTL200, a surface-based probabilistic atlas of the medial temporal lobe regions, derived from anatomical MRI data of 200 participants.

Automated Joint Space Detection Improves Bone Segmentation Accuracy

This paper introduces a deep learning model for automated joint space detection in micro-CT images of mouse paws, significantly improving segmentation accuracy compared to semi-automated methods.

Characterization of hippocampal subfields using histology-based annotated postmortem MRI: Lessons for in vivo segmentation II

The paper characterizes hippocampal subfield border variability using postmortem MRI and histology, finding consistent subfield appearance/disappearance orders and no clear diagnosis-related differences.

Harmonized Protocol for Segmentation of the Hippocampal Tail on High-Resolution in vivo MRI from the Hippocampal Subfields Group (HSG)

The paper presents a harmonized segmentation protocol for the hippocampal tail in high-resolution MRI, improving cross-study comparability through standardized definitions and consensus-based guidelines.

MORPH2DIAG: Automated Structural MRI Preprocessing and Tissue Segmentation for Interpretable Machine and Deep Learning-Based Neuroanatomical Classification

MORPH2DIAG automates MRI preprocessing and segmentation, using machine and deep learning to classify neuroanatomical subtypes with high accuracy.

Deep leaning auto-segmentation of tomographic datasets, repeatability and biomechanical outcomes

This paper develops a deep learning framework for automated segmentation of 3D CT data and validates its biomechanical accuracy using zebrafish mandibles.

Detailed delineation of the fetal brain in diffusion MRI via multi-task learning

This paper develops a multi-task deep learning method to segment and parcellate fetal brain structures from diffusion MRI, achieving high accuracy in tissue segmentation, tract segmentation, and parcellation.

Automatic segmentation of medial temporal lobe subregions in multi-scanner, multi-modality MRI of variable quality

This paper develops a multi-modality MRI segmentation model to improve robustness of medial temporal lobe subregion segmentation in poor-quality images.

PELICAN: a Longitudinal Image Processing Pipeline for Analyzing Structural Magnetic Resonance Images in Aging and Neurodegenerative Disease Populations

PELICAN is a longitudinal MRI processing pipeline that improves accuracy and reliability in analyzing brain atrophy and abnormalities in aging and neurodegenerative disease populations.

Spatial Prior-Guided Boundary and Region-Aware 2D Lesion Segmentation in Neonatal Hypoxic Ischemic Encephalopathy

This paper introduces a 2D segmentation framework for neonatal HIE lesions using DWI data, achieving competitive performance against 3D methods with a Dice Similarity Coefficient of 0.6060.

Automated Locus Coeruleus segmentation and age-related signal changes across Neuromelanin-sensitive MRI and T1/T2-weighted images

This paper develops an automated method for segmenting the locus coeruleus in MRI scans and examines age-related signal changes across different imaging modalities.

Brain virtual histology of a lizard species (Podarcis bocagei) using X-ray micro-tomography and deep-learning segmentation

The paper uses X-ray micro-tomography and deep-learning segmentation to estimate brain region volumes in wild lizards, showing that a deep-learning model can be trained with few datasets.

Developing an anatomically valid segmentation protocol for anterior regions of the medial temporal lobe for neurodegenerative diseases

The paper develops a segmentation protocol for anterior medial temporal lobe regions and integrates it into an automated approach to improve imaging biomarkers for neurodegenerative diseases.

Fully Automated and Scalable Pipeline for Macaque Brain Registration

The paper develops Macaca-Star, a pipeline for automated macaque brain registration across modalities, addressing deformation and variability challenges in ex vivo samples.

Predicting semantic segmentation quality in laryngeal endoscopy images

This paper presents an automatic system to evaluate semantic segmentation quality in laryngeal endoscopy images, showing its performance matches human raters using intersection over union metric.

SurfNet: Reconstruction of Cortical Surfaces via Coupled Diffeomorphic Deformations

SurfNet jointly reconstructs cortical surfaces from MRIs using diffeomorphic deformations, achieving state-of-the-art accuracy and topological correctness.

User-Assisted Approach for Accurate Nonrigid Registration of Images and Traces

This paper introduces a user-assisted nonrigid registration method that improves accuracy by incorporating fiducial points, achieving sufficient biomedical application accuracy within minutes.

A comparative study of rigid-body registration algorithms for the alignment of longitudinal structural MRI of the brain

This study compares ATRA to FSL, FreeSurfer, and ANTS for longitudinal brain MRI alignment, finding ATRA more accurate in most cases.