Medical Image Segmentation Techniques

Evaluation of surface-based hippocampal registration using ground-truth subfield definitions

This paper introduces a surface-based hippocampal registration method that improves subfield alignment across different morphologies.

BOston Neonatal Brain Injury Dataset for HypoxicIschemic Encephalopathy (BONBID-HIE): Part I. MRI and Manual Lesion Annotation

The paper introduces a public dataset of neonatal brain MRIs with expert annotations for HIE-related lesions to improve segmentation methods for small, diffuse lesions.

AutoMacq: an automatic pipeline to analyse macaque structural MRI data

AutoMacq is an automatic pipeline for processing rhesus macaque MRI data, producing reliable voxel-based and surface-based metrics with minimal manual intervention.

HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations

The paper introduces HumanBrainAtlas, a high-resolution in vivo MRI dataset with detailed segmentations for research, clinical, and educational use.

Automated identification of piglet brain tissue from MRI images using Region-based Convolutional Neural Networks

This paper develops an automated brain extraction method for piglet MRI images using Mask R-CNN, achieving high accuracy and consistency.

Accurate Bayesian segmentation of thalamic nuclei using diffusion MRI and an improved histological atlas

This paper introduces a Bayesian segmentation algorithm for thalamic nuclei using diffusion MRI and an improved histological atlas, showing improved accuracy in identifying nuclei and detecting Alzheimer's disease involvement.

Topology-Aware Focal Loss for 3D Image Segmentation

This paper introduces a topology-aware focal loss to improve 3D image segmentation by addressing topological errors through a Wasserstein distance-based constraint.

BOUNTI: Brain vOlumetry and aUtomated parcellatioN for 3D feTal MRI

The paper introduces a deep learning pipeline for automated fetal brain segmentation and volumetry, showing reduced need for manual refinement and agreement with manual segmentations in clinical cases.

Quantifying Numerical and Spatial Reliability of Amygdala and Hippocampal Subdivisions in FreeSurfer

The paper assesses the numerical and spatial reliability of hippocampal subfields and amygdala nuclei segmentation in FreeSurfer 7 using repeated scans from 923 participants.

An axis-referenced morphometric model reshaping the hippocampus following its longitudinal organization

This paper introduces an axis-referenced morphometric model (ARMM) to reshape the hippocampus based on its anatomical structure, showing improved shape similarity and ability to capture neurodegenerative changes.

DEEP LEARNING ENABLED MULTI-ORGAN SEGMENTATION OF MOUSE EMBRYOS

This paper introduces MEMOS, a deep learning tool for segmenting 50 anatomical structures in mouse embryos, validated against atlas-based methods and knockout strain data.

Proposed Methodology for Reducing Bias in Structural MRI Analysis in the Presence of Lesions: Data from a Pediatric Traumatic Brain Injury Cohort

The paper presents a two-step methodology to reduce bias in structural MRI analysis of pediatric traumatic brain injury cases with lesions using Freesurfer, retaining 8 out of 14 cases.

Digital 3D Brain MRI Arterial Territories Atlas

This paper creates a digital 3D arterial territory atlas from stroke patient MRIs to aid in automated analysis of brain damage.

Automated and manual segmentation of the hippocampus in human infants

The paper compares manual and automated methods for hippocampus segmentation in infants, finding that ASHS provides more reliable results than manual or other automated approaches.

Restoring morphology of light sheet microscopy data based on magnetic resonance histology

This paper presents a method to register light sheet microscopy data with magnetic resonance histology to restore in-skull morphology, achieving accurate registration with low displacement error.

Multifaceted Atlases of the Human Brain in its Infancy

This paper introduces month-specific brain atlases for infants aged two weeks to two years, integrating surface and volumetric data to capture early brain development in a unified coordinate system.

A generalizable brain extraction net (BEN) for multimodal MRI data from rodents, nonhuman primates, and humans

The paper introduces BEN, a deep learning method for brain tissue extraction across species and MRI modalities, showing improved accuracy and generalizability over existing tools.

Automated segmentation of fetal intracranial volume in 3D ultrasound using deep learning: identifying sex differences in prenatal brain development

The paper develops a deep learning model to automate fetal intracranial volume segmentation from 3D ultrasound and finds sex differences in ICV and growth.

Applying Unet for extraction of vascular metrics from T1-weighted and T2-weighted MRI

This paper trains a Unet model to segment arteries and estimate diameters from T1w and T2w MRI images, showing T2w images can provide comparable results to TOF.

Tumor Segmentation using temporal Independent Component Analysis for DCE-MRI

This study compares temporal ICA (tICA) and spatial ICA (sICA) for canine brain tumor segmentation, finding tICA achieves higher overlap with manual delineation than sICA.

Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold

HippUnfold is an automated BIDS-App for hippocampal unfolding and subfield segmentation using deep learning and topological constraints, enabling individual-specific morphometric analysis.

3D-MASNet: 3D Mixed-scale Asymmetric Convolutional Segmentation Network for 6-month-old Infant Brain MR Images

This paper introduces 3D-MASNet, a 3D mixed-scale asymmetric convolutional network for segmenting infant brain MR images, achieving high Dice coefficients in GM, WM, and CSF.

Tensor Image Registration Library: Automated Deformable Registration of Stand-Alone Histology Images to Whole-Brain Post-Mortem MRI Data

The paper introduces an automated pipeline for registering stand-alone histology sections to whole-brain MRI data with sub-voxel accuracy.

DeepParcellation: a novel deep learning method for robust brain magnetic resonance imaging parcellation in older East Asians

DeepParcellation provides accurate 3D MRI parcellation for older East Asians, outperforming existing methods in this demographic.

Improving 3D Edge Detection for Visual Inspection of MRI Coregistration and Alignment

The paper proposes using the second derivative (DoG) for edge detection in MRI coregistration, improving alignment quality by generating closed surfaces and reducing intensity dependency.

An automated BIDS-App for brain segmentation of human fetal functional MRI data

The paper introduces funcmasker-flex, a BIDS App using a 3D U-net to automate fetal fMRI brain segmentation, achieving high Dice scores and reducing manual effort.

DeepImageTranslator V2: analysis of multimodal medical images using semantic segmentation maps generated through deep learning

The paper updates DeepImageTranslator with a tool for multimodal medical image segmentation analysis, showing it produces accurate statistics comparable to manual ROI selection.

Using Transfer Learning for Automated Microbleed Segmentation

This paper develops an automated microbleed segmentation tool using transfer learning on MRI data, achieving high sensitivity and specificity.

ABLE: Automated Brain Lines Extraction Based on Laplacian Surface Collapse

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: Simultaneous Training and Model Pruning for Low Data Regimes in Medical Image Segmentation

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.