Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review
M Yanzhen, C Song, L Wanping, Y Zufang… - Frontiers in …, 2024 - frontiersin.org
Introduction Brain medical image segmentation is a critical task in medical image
processing, playing a significant role in the prediction and diagnosis of diseases such as …
processing, playing a significant role in the prediction and diagnosis of diseases such as …
Brain atrophy assessment in multiple sclerosis: technical–and subject-related barriers for translation to real-world application in individual subjects
R Zivadinov, A Tranquille, JA Reeves… - Expert Review of …, 2024 - Taylor & Francis
Introduction Brain atrophy is a well-established MRI outcome for predicting clinical
progression and monitoring treatment response in persons with multiple sclerosis (pwMS) at …
progression and monitoring treatment response in persons with multiple sclerosis (pwMS) at …
Cross-site prognosis prediction for nasopharyngeal carcinoma from incomplete multi-modal data
Accurate prognosis prediction for nasopharyngeal carcinoma based on magnetic resonance
(MR) images assists in the guidance of treatment intensity, thus reducing the risk of …
(MR) images assists in the guidance of treatment intensity, thus reducing the risk of …
[HTML][HTML] SiMix: A domain generalization method for cross-site brain MRI harmonization via site mixing
C Xu, J Li, Y Wang, L Wang, Y Wang, X Zhang, W Liu… - NeuroImage, 2024 - Elsevier
Brain magnetic resonance imaging (MRI) is widely used in clinical practice for disease
diagnosis. However, MRI scans acquired at different sites can have different appearances …
diagnosis. However, MRI scans acquired at different sites can have different appearances …
[HTML][HTML] DAW-FA: Domain-aware adaptive weighting with fine-grain attention for unsupervised MRI harmonization
Magnetic resonance (MR) imaging often lacks standardized acquisition protocols across
various sites, leading to contrast variations that reduce image quality and hinder automated …
various sites, leading to contrast variations that reduce image quality and hinder automated …
[HTML][HTML] Deep learning for the harmonization of structural MRI scans: a survey
Medical imaging datasets for research are frequently collected from multiple imaging centers
using different scanners, protocols, and settings. These variations affect data consistency …
using different scanners, protocols, and settings. These variations affect data consistency …
OpenMAP-T1: A Rapid Deep Learning Approach to Parcellate 280 Anatomical Regions to Cover the Whole Brain
This study introduces OpenMAP-T1, a deep-learning-based method for rapid and accurate
whole-brain parcellation in T1-weighted brain MRI, which aims to overcome the limitations of …
whole-brain parcellation in T1-weighted brain MRI, which aims to overcome the limitations of …
Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation
Deep learning algorithms using Magnetic Resonance (MR) images have demonstrated state-
of-the-art performance in the automated segmentation of Multiple Sclerosis (MS) lesions …
of-the-art performance in the automated segmentation of Multiple Sclerosis (MS) lesions …
Harmonizing Flows: Leveraging normalizing flows for unsupervised and source-free MRI harmonization
Lack of standardization and various intrinsic parameters for magnetic resonance (MR)
image acquisition results in heterogeneous images across different sites and devices, which …
image acquisition results in heterogeneous images across different sites and devices, which …
[HTML][HTML] Image harmonization improves consistency of intra-rater delineations of MS lesions in heterogeneous MRI
Clinical magnetic resonance images (MRIs) lack a standard intensity scale due to
differences in scanner hardware and the pulse sequences used to acquire the images …
differences in scanner hardware and the pulse sequences used to acquire the images …