Medical Image Segmentation Techniques

ABLE extracts sulcal fundi and gyral crown lines from brain surfaces using Laplacian surface collapse, validated on HCP data to show reliable and accurate sulcal topology representation.

STAMP trains and prunes a UNet for medical image segmentation with online pruning, achieving higher performance and reducing parameters by over 85% with fewer labels.

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.

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

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

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.

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.

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

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

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.

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

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.

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.

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

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

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.

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

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

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

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

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.

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

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

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

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.

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

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

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