AIMC Topic: Magnetic Resonance Imaging

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3-D Quantum-Inspired Self-Supervised Tensor Network for Volumetric Segmentation of Medical Images.

IEEE transactions on neural networks and learning systems
This article introduces a novel shallow 3-D self-supervised tensor neural network in quantum formalism for volumetric segmentation of medical images with merits of obviating training and supervision. The proposed network is referred to as the 3-D qua...

Automatic pipeline for segmentation of LV myocardium on quantitative MR T1 maps using deep learning model and computation of radial T1 and ECV values.

NMR in biomedicine
Native T1 mapping is a non-invasive technique used for early detection of diffused myocardial abnormalities, and it provides baseline tissue characterization. Post-contrast T1 mapping enhances tissue differentiation, enables extracellular volume (ECV...

Fusing multi-scale functional connectivity patterns via Multi-Branch Vision Transformer (MB-ViT) for macaque brain age prediction.

Neural networks : the official journal of the International Neural Network Society
Brain age (BA) is defined as a measure of brain maturity and could help characterize both the typical brain development and neuropsychiatric disorders in mammals. Various biological phenotypes have been successfully applied to predict BA of human usi...

Fully Automated Hippocampus Segmentation using T2-informed Deep Convolutional Neural Networks.

NeuroImage
Hippocampal atrophy (tissue loss) has become a fundamental outcome parameter in clinical trials on Alzheimer's disease. To accurately estimate hippocampus volume and track its volume loss, a robust and reliable segmentation is essential. Manual hippo...

Recent trends in AI applications for pelvic MRI: a comprehensive review.

La Radiologia medica
Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum. Since the diagnostic pathway of pelvic MRI can involve various complex procedures depending on the ...

The stroke outcome optimization project: Acute ischemic strokes from a comprehensive stroke center.

Scientific data
Stroke is a leading cause of disability, and Magnetic Resonance Imaging (MRI) is routinely acquired for acute stroke management. Publicly sharing these datasets can aid in the development of machine learning algorithms, particularly for lesion identi...

BrainNPT: Pre-Training Transformer Networks for Brain Network Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Deep learning methods have advanced quickly in brain imaging analysis over the past few years, but they are usually restricted by the limited labeled data. Pre-trained model on unlabeled data has presented promising improvement in feature learning in...

Integrating MRI-based radiomics and clinicopathological features for preoperative prognostication of early-stage cervical adenocarcinoma patients: in comparison to deep learning approach.

Cancer imaging : the official publication of the International Cancer Imaging Society
OBJECTIVES: The roles of magnetic resonance imaging (MRI) -based radiomics approach and deep learning approach in cervical adenocarcinoma (AC) have not been explored. Herein, we aim to develop prognosis-predictive models based on MRI-radiomics and cl...