AIMC Topic: Image Processing, Computer-Assisted

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Development of a deep learning method for phase retrieval image enhancement in phase contrast microcomputed tomography.

Journal of microscopy
Propagation-based imaging (one method of X-ray phase contrast imaging) with microcomputed tomography (PBI-µCT) offers the potential to visualise low-density materials, such as soft tissues and hydrogel constructs, which are difficult to be identified...

AI-driven glomerular morphology quantification: a novel pipeline for assessing basement membrane thickness and podocyte foot process effacement in kidney diseases.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Measuring the thickness of the glomerular basement membrane (GBM) and assessing the percentage of podocyte foot process effacement (%PFPE) are important for diagnosing non-neoplastic kidney diseases. However, when performed ...

Application of a pulmonary nodule detection program using AI technology to ultra-low-dose CT: differences in detection ability among various image reconstruction methods.

Japanese journal of radiology
PURPOSE: This study aimed to investigate the performance of an artificial intelligence (AI)-based lung nodule detection program in ultra-low-dose CT (ULDCT) imaging, with a focus on the influence of various image reconstruction methods on detection a...

Enhanced reconstruction of atomic force microscopy cell images to super-resolution.

Journal of microscopy
Atomic force microscopy (AFM) plays a pivotal role in cell biology research. It enables scientists to observe the morphology of cell surfaces at the nanoscale, providing essential data for understanding cellular functions, including cell-cell interac...

Integrating weight and imaging features: A machine learning framework for larval instar identification in (Walker).

Bulletin of entomological research
The oriental armyworm, (Walker), is a highly migratory pest known for its sudden larval outbreaks, which result in severe crop losses. These unpredictable surges pose significant challenges for timely and accurate monitoring, as conventional methods...

Lung cancer detection and classification using optimized CNN features and Squeeze-Inception-ResNeXt model.

Computational biology and chemistry
Lung cancer, with its high mortality rate, is one of the deadliest diseases globally. The alarming increase in lung cancer deaths and its widespread prevalence have led to the development of various cancer control research and early detection methods...

Fast and Stable Neonatal Brain MR Imaging Using Integrated Learned Subspace Model and Deep Learning.

IEEE transactions on bio-medical engineering
OBJECTIVE: To enable fast and stable neonatal brain MR imaging by integrating learned neonate-specific subspace model and model-driven deep learning.

DEISM: Deep Reconstruction Framework With Self-Calibration Mechanisms for Accelerated Chemical Exchange Saturation Transfer Imaging.

IEEE transactions on bio-medical engineering
The prolonged scan time of chemical exchange saturation transfer (CEST) imaging, caused by multiple data acquisitions over the varying saturation offset frequencies, necessitates accelerated imaging techniques. In this work, the artifact information ...

Brain tumor segmentation and classification using MRI: Modified segnet model and hybrid deep learning architecture with improved texture features.

Computational biology and chemistry
Brain tumors are quickly overtaking all other causes of death worldwide. The failure to perform a timely diagnosis is the main cause of increasing the death rate. Traditional methods of brain tumor diagnosis heavily rely on the expertise of radiologi...

Large models in medical imaging: Advances and prospects.

Chinese medical journal
Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in proc...