Medical Image Segmentation Techniques

SAM-based Automatic Workflow for Histology Cyst Segmentation in Autosomal Dominant Polycystic Kidney Disease

This paper introduces an automated workflow using the SAM model to segment cysts in histological images of ADPKD, showing increased diseased tissue proportion over time and outperforming existing tools.

AmygdalaGo-BOLT: an open and reliable AI tool to trace boundaries of human amygdala

AmygdalaGo-BOLT improves amygdala segmentation accuracy and efficiency in pediatric MRI scans across multiple centers and ages.

Segmentation and Feature Extraction of Fingernail Plate and Lunula Based on Deep Learning

This paper introduces a deep learning model called Nailnet for segmenting fingernail plate and lunula from hand images, achieving high IoU and accuracy scores.

Confidence-Enhanced Semi-supervised Learning for Mediastinal Neoplasm Segmentation

This paper introduces a confidence-enhanced semi-supervised learning framework for mediastinal neoplasm segmentation, improving performance by leveraging unlabeled data and refining prediction reliability.

Morphological simulation tests the limits on phenotype discovery in 3D image analysis

This paper introduces a 3D morphological simulation approach to test phenotype detection methods, showing that detectability depends on effect size, sample size, and ROI selection.

Automated segmentation of epilepsy surgical resection cavities: comparison of four methods to manual segmentation

The paper compares four automated MRI segmentation methods for epilepsy resection cavities to manual segmentation, finding SPM-based methods outperform deep learning approaches in most cases.

Automatic Geometry-based Estimation of the Locus Coeruleus Region on T1-Weighted Magnetic Resonance Images

This paper evaluates two methods for automatically estimating the locus coeruleus region from standard T1-weighted MRI scans using non-contrast neuroanatomical data.

Automatization and validation of the hippocampal-to-ventricle ratio in a clinical sample

This paper evaluates automated methods for hippocampal segmentation and HVR calculation, finding that a CNN-based method outperforms others in accuracy and reliability.

SAMson: an automated brain extraction tool for rodents using SAM

SAMson is an automated tool for rodent brain extraction from MRI data, outperforming existing methods in segmentation accuracy across diverse datasets.

Brain Templates for Chinese Babies from Newborn to Three Months of Age

This study constructs age-specific brain templates for Chinese infants aged 6 days to 3 months using MRI, showing significant volumetric growth and sex differences.

A next-generation, histological atlas of the human brain and its application to automated brain MRI segmentation

The paper introduces NextBrain, a probabilistic histological atlas of the human brain, and presents a tool for automated MRI segmentation of anatomical regions.

UniFed: A unified deep learning framework for segmentation of partially labelled, distributed neuroimaging data

UniFed addresses harmonisation and privacy in neuroimaging by enabling federated learning with partially labelled distributed data, producing high-quality segmentations.

The FinnBrain Multimodal Neonatal Template and Atlas Collection: T1, T2, and DTI brain templates, and accompanying cortical and subcortical atlases

The paper introduces a multi-contrast neonatal brain template and atlases to improve image processing and spatial normalization in neonatal neuroimaging.

FFCM-MRF: An accurate and generalizable cerebrovascular segmentation pipeline for humans and rhesus monkeys based on TOF-MRA

This paper introduces FFCM-MRF, a cerebrovascular segmentation method for humans and rhesus monkeys, showing improved accuracy and generalizability across datasets and scanners.

Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods

This paper introduces two algorithms, GridFit and CEPA, for accurate and automatic localization of intracranial electrodes and brain-shift compensation, validated on real patient data.

Internally-consistent and fully-unbiased multimodal MRI brain template construction from UK Biobank: Oxford-MM

This paper constructs internally-consistent, fully-unbiased multimodal MRI brain templates from UK Biobank data, showing improved registration accuracy compared to existing templates.

MEBRAINS 1.0: a new population-based macaque atlas

MEBRAINS 1.0 creates a multi-subject, multi-modal macaque brain atlas using T1 and T2 images, surface models, and curated segmentations to improve data integration.

MMORF - FSL's MultiMOdal Registration Framework

MMORF is a nonlinear image registration tool for MRI brain images that outperforms existing methods in accuracy and distortion control across multiple domains.

Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology

The paper develops open-source tools for 3D analysis of brain dissection photographs and surface scans, achieving accurate reconstructions and segmentations comparable to MRI.

Segmentation of supragranular and infragranular layers in ultra-high resolution 7T ex vivo MRI of the human cerebral cortex

This paper introduces a semi-supervised segmentation model for identifying supragranular and infragranular layers in human cerebral cortex MRI scans with high precision.

SEGMENTATION OF DYNAMIC TOTAL-BODY -FDG PET IMAGES USING UNSUPERVISED CLUSTERING

This study evaluates unsupervised clustering methods for segmenting dynamic total-body PET images at the organ level, finding that k-means with PCA performs best but still has limited accuracy.

Early detection of white matter hyperintensities using SHIVA-WMH detector

The paper introduces a deep learning tool, SHIVA-WMH, for detecting white matter hyperintensities across different WMH severities and demonstrates its superior performance compared to existing methods.

An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids

This paper introduces an AI-based pipeline for analyzing cerebral organoids using MRI, achieving high segmentation accuracy and quality classification.

Domain-Invariant Brainstem Nuclei Segmentation and Signal Quantification

StARQ automates high-granularity segmentation and quantification of brainstem nuclei using deep learning, enhancing reliable computational analysis.

Sequential Modelling for Medical Image Segmentation

This paper proposes a sequential modelling approach for medical image segmentation using a modified MegaByte model, achieving comparable performance on intracranial haemorrhage segmentation and outperforming existing methods on tiny structures.

DeepSlice: rapid fully automatic registration of mouse brain imaging to a volumetric atlas

DeepSlice automatically registers mouse brain histology to the Allen Brain Atlas, achieving high accuracy and speed improvements of over 1000 times.

Comparison of image registration techniques in functional ultrasound imaging

This study compares image registration techniques for functional ultrasound imaging, finding that Imregdeform provides the best alignment of 2D fUSI brain data.

Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images

This paper develops an automated deep learning method for ischemic stroke lesion segmentation in mouse MRI, achieving high agreement with manual segmentation.

Deep learning segmentation of the nucleus basalis of Meynert on 3T MRI

This paper develops a deep learning model to accurately segment the nucleus basalis of Meynert on 3T MRI, showing improved performance over standard atlases in both healthy subjects and TLE patients.

Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration

The paper releases curated anatomical fiducial placements for MRI datasets to improve image registration quality control and registration accuracy.