AIMC Topic: Image Processing, Computer-Assisted

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Causal recurrent intervention for cross-modal cardiac image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cross-modal cardiac image segmentation is essential for cardiac disease analysis. In diagnosis, it enables clinicians to obtain more precise information about cardiac structure or function for potential signs by leveraging specific imaging modalities...

OrgaMeas: A pipeline that integrates all the processes of organelle image analysis.

Biochimica et biophysica acta. Molecular cell research
Although image analysis has emerged as a key technology in the study of organelle dynamics, the commonly used image-processing methods, such as threshold-based segmentation and manual setting of regions of interests (ROIs), are error-prone and labori...

Towards real-time conformal palliative treatment of spine metastases: A deep learning approach for Hounsfield Unit recovery of cone beam CT images.

Medical physics
BACKGROUND: The extension of onboard cone-beam CT (CBCT) imaging for real-time treatment planning is constrained by limitations in image quality. Synthetic CT (sCT) generation using deep learning provides a potential solution to these limitations.

A CVAE-based generative model for generalized B inhomogeneity corrected chemical exchange saturation transfer MRI at 5 T.

NeuroImage
Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) has emerged as a powerful tool to image endogenous or exogenous macromolecules. CEST contrast highly depends on radiofrequency irradiation B level. Spatial inhomogeneity of...

Artificial intelligence-powered four-fold upscaling of human brain synthetic metabolite maps.

The Journal of international medical research
ObjectiveCompared with anatomical magnetic resonance imaging modalities, metabolite images from magnetic resonance spectroscopic imaging often suffer from low quality and detail due to their larger voxel sizes. Conventional interpolation techniques a...

One for multiple: Physics-informed synthetic data boosts generalizable deep learning for fast MRI reconstruction.

Medical image analysis
Magnetic resonance imaging (MRI) is a widely used radiological modality renowned for its radiation-free, comprehensive insights into the human body, facilitating medical diagnoses. However, the drawback of prolonged scan times hinders its accessibili...

Sharper insights: Adaptive ellipse-template for robust fovea localization in challenging retinal landscapes.

Computers in biology and medicine
Automated identification of retinal landmarks, particularly the fovea is crucial for diagnosing diabetic retinopathy and other ocular diseases. But accurate identification is challenging due to varying contrast, color irregularities, anatomical struc...

Deep-learning-enabled spatial frequency domain imaging of the spatiotemporal dynamics of skin physiology.

Journal of biomedical optics
SIGNIFICANCE: Spatial frequency domain imaging (SFDI) is an emerging optical imaging modality for visualizing tissue absorption and scattering properties. This approach is promising for noninvasive wide field-of-view (FOV) monitoring of biophysiologi...

SagMSI: A graph convolutional network framework for precise spatial segmentation in mass spectrometry imaging.

Analytica chimica acta
BACKGROUND: Mass Spectrometry Imaging (MSI) is a label-free imaging technique used in spatial metabolomics to explore the distribution of various metabolites within biological tissues. Spatial segmentation plays a crucial role in the biochemical inte...

Two-stage color fundus image registration via Keypoint Refinement and Confidence-Guided Estimation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Color fundus images are widely used for diagnosing diseases such as Glaucoma, Cataracts, and Diabetic Retinopathy. The registration of color fundus images is crucial for assessing changes in fundus appearance to determine disease progression. In this...