AIMC Topic: Image Processing, Computer-Assisted

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Stereology neuron counts correlate with deep learning estimates in the human hippocampal subregions.

Scientific reports
Hippocampal subregions differ in specialization and vulnerability to cell death. Neuron death and hippocampal atrophy have been a marker for the progression of Alzheimer's disease. Relatively few studies have examined neuronal loss in the human brain...

Convolutional neural network-based model observer for signal known statistically task in breast tomosynthesis images.

Medical physics
BACKGROUND: Since human observer studies are resource-intensive, mathematical model observers are frequently used to assess task-based image quality. The most common implementation of these model observers assume that the signal information is exactl...

Live 4D-OCT denoising with self-supervised deep learning.

Scientific reports
By providing three-dimensional visualization of tissues and instruments at high resolution, live volumetric optical coherence tomography (4D-OCT) has the potential to revolutionize ophthalmic surgery. However, the necessary imaging speed is accompani...

Rethinking causality-driven robot tool segmentation with temporal constraints.

International journal of computer assisted radiology and surgery
PURPOSE: Vision-based robot tool segmentation plays a fundamental role in surgical robots perception and downstream tasks. CaRTS, based on a complementary causal model, has shown promising performance in unseen counterfactual surgical environments in...

The Impact of Fatty Infiltration on MRI Segmentation of Lower Limb Muscles in Neuromuscular Diseases: A Comparative Study of Deep Learning Approaches.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Deep learning methods have been shown to be useful for segmentation of lower limb muscle MRIs of healthy subjects but, have not been sufficiently evaluated on neuromuscular disease (NDM) patients.

All answers are in the images: A review of deep learning for cerebrovascular segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cerebrovascular imaging is a common examination. Its accurate cerebrovascular segmentation become an important auxiliary method for the diagnosis and treatment of cerebrovascular diseases, which has received extensive attention from researchers. Deep...

Dose reduction and image enhancement in micro-CT using deep learning.

Medical physics
BACKGROUND: In preclinical settings, micro-computed tomography (CT) provides a powerful tool to acquire high resolution anatomical images of rodents and offers the advantage to in vivo non-invasively assess disease progression and therapy efficacy. M...

Glomerulus Detection Using Segmentation Neural Networks.

Journal of digital imaging
Digital pathology is vital for the correct diagnosis of kidney before transplantation or kidney disease identification. One of the key challenges in kidney diagnosis is glomerulus detection in kidney tissue segments. In this study, we propose a deep ...

DBCU-Net: deep learning approach for segmentation of coronary angiography images.

The international journal of cardiovascular imaging
Coronary angiography (CAG) is the "gold standard" for diagnosing coronary artery disease (CAD). However, due to the limitation of current imaging methods, the CAG image has low resolution and poor contrast with a lot of artifacts and noise, which mak...

Comparison of deep learning models for digital H&E staining from unpaired label-free multispectral microscopy images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: This paper presents the quantitative comparison of three generative models of digital staining, also known as virtual staining, in H&E modality (i.e., Hematoxylin and Eosin) that are applied to 5 types of breast tissue. More...