Medical Image Segmentation Techniques

The paper develops a multi-model consensus deep learning approach for automated gold fiducial marker segmentation in prostate MRI, achieving high sensitivity and precision.

MacaSurfer provides a unified framework for automated surface-volume mapping of the macaque brain across age and sites, showing high consistency and robustness.

This paper introduces a self-supervised deep learning framework for automated fossil CT segmentation, achieving high accuracy and significantly reducing processing time.

LVentiView automates 3D left ventricular mesh reconstruction and analysis from cardiac MRI, achieving accuracy comparable to expert manual segmentation.

This paper develops a registration pipeline to align multimodal 3D imaging data from colon cancer tissue and uses unsupervised clustering to identify spatially coherent tissue regions.

This paper calibrates penalty parameters in vascular deformation mapping using synthetic data to improve 3D aortic deformation analysis in health and disease.

This paper introduces Bone-Segnet, a dual-view guided network for automated bone lesion segmentation in SPECT images, achieving improved Dice scores and enabling quantitative analysis of lesion characteristics.

This paper develops an automated framework for morphometric analysis of stented arterial cross-sections, achieving high accuracy and significantly reducing analysis time.

This paper reviews the evolution of deep learning in biomedical image segmentation, highlighting the shift to foundation models and introducing a taxonomy for promptable segmentation methods.

DeepBranchAI reduces annotation time for 3D branching network segmentation by using a cascade workflow that transforms sparse labels into comprehensive training data.

QNPtoVox maps 2D quantitative neuropathology data to 3D MNI voxel space for spatial standardization and integration with neuroimaging tools.

The paper develops an automated method combining MRI contrasts to improve tissue segmentation and perivascular space detection using data from aging and Alzheimer's studies.

This paper introduces VesSynth, a vessel segmentation framework trained on synthetic data that achieves state-of-the-art accuracy across multiple imaging modalities and spatial resolutions.

This paper compares automated and prompt-based models for trachea segmentation in CT scans, showing that dataset structure affects reliability and interpretability.

ROIMAPer is an open-source FIJI plugin for registering brain images to standard atlases, demonstrating accuracy in quantifying marker genes across brain regions and subregions.

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

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.

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.

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

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

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

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

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

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

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

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

The paper develops a deep learning framework for 3D segmentation of brain white matter microstructures from X-ray nano holography data, achieving high accuracy in complex anatomical regions.

The paper trains a deep learning model to quantify brain vascular morphology from standard MR images, showing it captures aging and health-related features not found in traditional metrics.