AIMC Topic: Image Processing, Computer-Assisted

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Efficient application of deep learning-based elective lymph node regions delineation for pelvic malignancies.

Medical physics
BACKGROUND: While there are established international consensuses on the delineation of pelvic lymph node regions (LNRs), significant inter- and intra-observer variabilities persist. Contouring these clinical target volumes for irradiation in pelvic ...

FPWT: Filter pruning via wavelet transform for CNNs.

Neural networks : the official journal of the International Neural Network Society
The enormous data and computational resources required by Convolutional Neural Networks (CNNs) hinder the practical application on mobile devices. To solve this restrictive problem, filter pruning has become one of the practical approaches. At presen...

An unrolled neural network for accelerated dynamic MRI based on second-order half-quadratic splitting model.

Magnetic resonance imaging
The reconstruction of dynamic magnetic resonance images from incomplete k-space data has sparked significant research interest due to its potential to reduce scan time. However, traditional iterative optimization algorithms fail to faithfully reconst...

Multi-modality deep learning-based [Ga]Ga-DOTA-FAPI-04 PET polar map generation: potential value in detecting reactive fibrosis after myocardial infarction.

European journal of nuclear medicine and molecular imaging
PURPOSE: Generating polar map (PM) from [Ga]Ga-DOTA-FAPI-04 PET images is challenging and inaccurate using existing automatic methods that rely on the myocardial anatomical integrity in PET images. This study aims to enhance the accuracy of PM genera...

Model-based federated learning for accurate MR image reconstruction from undersampled k-space data.

Computers in biology and medicine
Deep learning-based methods have achieved encouraging performances in the field of Magnetic Resonance (MR) image reconstruction. Nevertheless, building powerful and robust deep learning models requires collecting large and diverse datasets from multi...

Holistic evaluation of a machine learning-based timing calibration for PET detectors under varying data sparsity.

Physics in medicine and biology
Modern PET scanners offer precise TOF information, improving the SNR of the reconstructed images. Timing calibrations are performed to reduce the worsening effects of the system components and provide valuable TOF information. Traditional calibration...

Deep learning for intracranial aneurysm segmentation using CT angiography.

Physics in medicine and biology
This study aimed to employ a two-stage deep learning method to accurately detect small aneurysms (4-10 mm in size) in computed tomography angiography images.This study included 956 patients from 6 hospitals and a public dataset obtained with 6 CT sca...

A deep learning-based method for the detection and segmentation of breast masses in ultrasound images.

Physics in medicine and biology
Automated detection and segmentation of breast masses in ultrasound images are critical for breast cancer diagnosis, but remain challenging due to limited image quality and complex breast tissues. This study aims to develop a deep learning-based meth...

Endoscopic Artificial Intelligence for Image Analysis in Gastrointestinal Neoplasms.

Digestion
BACKGROUND: Artificial intelligence (AI) using deep learning systems has recently been utilized in various medical fields. In the field of gastroenterology, AI is primarily implemented in image recognition and utilized in the realm of gastrointestina...

Deep learning-based material decomposition of iodine and calcium in mobile photon counting detector CT.

PloS one
Photon-counting detector (PCD)-based computed tomography (CT) offers several advantages over conventional energy-integrating detector-based CT. Among them, the ability to discriminate energy exhibits significant potential for clinical applications be...