AIMC Topic: Image Processing, Computer-Assisted

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Deep Learning-Based Saturation Compensation for High Dynamic Range Multispectral Fluorescence Lifetime Imaging.

IEEE transactions on bio-medical engineering
In multispectral fluorescence lifetime imaging (FLIm), achieving consistent imaging quality across all spectral channels is crucial for accurately identifying a wide range of fluorophores. However, these essential measurements are frequently compromi...

Automated detection of anterior crossbite on intraoral images and videos utilizing deep learning.

International journal of computerized dentistry
AIM: Malocclusion has emerged as a burgeoning global public health concern. Individuals with an anterior crossbite face an elevated risk of exhibiting characteristics such as a concave facial profile, negative overjet, and poor masticatory efficiency...

A preliminary attempt to harmonize using physics-constrained deep neural networks for multisite and multiscanner MRI datasets (PhyCHarm).

NeuroImage
In magnetic resonance imaging (MRI), variations in scan parameters and scanner specifications can result in differences in image appearance. To minimize these differences, harmonization in MRI has been suggested as a crucial image processing techniqu...

Deep learning-based diffusion MRI tractography: Integrating spatial and anatomical information.

NeuroImage
Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain. It plays a crucial role in neuroscience and clinical fields by facilitating the study of brain connectivity and neurological disorders....

A Meta-Learning Approach for Multicenter and Small-Data Single-Cell Image Analysis.

Analytical chemistry
The application of algorithm-based single-cell imaging techniques can visualize and analyze cellular heterogeneity. However, algorithm-based single-cell imaging techniques are severely limited by the high workload required to label single-cell images...

Reconstructing Super-Resolution Raman Spectral Image Using a Generative Adversarial Network-Based Algorithm.

Analytical chemistry
Raman imaging utilizes molecular fingerprint information to visualize the spatial distribution of a substance within the scanned area. Subject to its scanning mechanism, it usually costs a prolonged data acquisition duration for achieving high-resolu...

Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens.

Microbiology spectrum
Mycobacteria, including (MTB) and non-tuberculosis mycobacteria (NTM), are important causes of infectious disease and cause significant mortality and morbidity globally. Fast detection is extremely important to reduce transmission and mortality asso...

LGF-Net: A multi-scale feature fusion network for thyroid nodule ultrasound image classification.

Journal of applied clinical medical physics
BACKGROUND: Thyroid cancer is one of the most common cancers in clinical practice, and accurate classification of thyroid nodule ultrasound images is crucial for computer-aided diagnosis. Models based on a convolutional neural network (CNN) or a tran...

Enhancing Brain Metastases Detection and Segmentation in Black-Blood MRI Using Deep Learning and Segment Anything Model (SAM).

Yonsei medical journal
PURPOSE: Black-blood (BB) magnetic resonance images (MRI) offer superior image contrast for the detection and segmentation of brain metastases (BMs). This study investigated the efficacy and accuracy of deep learning (DL) architectures and post-proce...

A multi-stage 3D convolutional neural network algorithm for CT-based lung segment parcellation.

Journal of applied clinical medical physics
BACKGROUND: Current approaches to lung parcellation utilize established fissures between lobes to provide estimates of lobar volume. However, deep learning segment parcellation provides the ability to better assess regional heterogeneity in ventilati...