AIMC Topic: Multimodal Imaging

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seg2med: a bridge from artificial anatomy to multimodal medical images.

Physics in medicine and biology
. We present seg2med (segmentation-to-medical images), a modular framework for anatomy-driven multimodal medical image synthesis. The system integrates three components to enable high-fidelity, cross-modality generation of computed tomography (CT) an...

Unsupervised discovery of ischemic stroke phenotypes from multimodal MRI radiomics.

Biomedical physics & engineering express
This study presents a fully unsupervised and label-independent radiomic pipeline designed to group different types of ischemic stroke lesions using multimodal Magnetic Resonance Imaging (MRI) . The aim is to address lesion heterogeneity and the absen...

Brain tumour segmentation in fused MRI-PET images with permutate U-Net framework.

PloS one
Brain tumor segmentation from MRI's and PET has always been a challenging and time-consuming phase for radiologists, due to low sensitivity boundary region pixels in this image modality. Deep learning-based image segmentation is the hot research topi...

Interpretable machine learning model based on multimodal ultrasound for bedside diagnosis of acute exacerbations in COPD.

Respiratory research
BACKGROUND: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with accelerated lung function decline and increased mortality. However, early and accurate diagnosis remains clinically challenging due to nonspecific s...

Multimodal fusion of ultrasound images using HXM net for breast cancer diagnosis.

Scientific reports
Breast cancer is a major global health issue in women, where diagnosis at an early stage is decisive for enhancing the effectiveness of treatment and survival. Despite the advances in imaging using medical technologies, maintaining uniformly good dia...

Long-range correlation-guided dual-encoder fusion network for medical images.

Scientific reports
Multimodal medical image fusion plays an important role in clinical applications. However, multimodal medical image fusion methods ignore the feature dependence among modals, and the feature fusion ability with different granularity is not strong. A ...

Enhanced brain tumor segmentation in medical imaging using multi-modal multi-scale contextual aggregation and attention fusion.

Scientific reports
Accurate segmentation of brain tumors from multi-modal MRI scans is critical for diagnosis, treatment planning, and disease monitoring. Tumor heterogeneity and inter-image variability across MRI sequences pose challenging problems to state-of-the-art...

Automated Multimodal Image Registration for Prostate Cancer Using Squeeze-and-Excitation ResNet with Thin Plate Spline Transformation: A Deep Learning Approach.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Accurate spatial correlation between preoperative prostate MRI and post-prostatectomy histopathology is critical for improving prostate cancer diagnosis, treatment planning, and MRI interpretation. Current manual registration methods are t...

An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease.

Journal of neural engineering
Alzheimer's disease (AD) is the most prevalent form of dementia worldwide, encompassing a prodromal stage known as mild cognitive impairment (MCI), where patients may either progress to AD or remain stable. The objective of the work was to capture st...

Multimodal MRI analysis selecting key brain features for machine learning based classification of diabetic neuropathic pain and phenotypes.

Journal of the neurological sciences
Cerebral alterations are associated with diabetic peripheral neuropathy (DPN) and neuropathic pain, including reductions in brain volumes, cortical thickness, sulcus depth, and alterations in metabolites and functional connectivity. This study combin...