AIMC Topic: Tomography, X-Ray Computed

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The Trials and Tribulations of Assembling Large Medical Imaging Datasets for Machine Learning Applications.

Journal of digital imaging
With vast interest in machine learning applications, more investigators are proposing to assemble large datasets for machine learning applications. We aim to delineate multiple possible roadblocks to exam retrieval that may present themselves and lea...

Detection and analysis of COVID-19 in medical images using deep learning techniques.

Scientific reports
The main purpose of this work is to investigate and compare several deep learning enhanced techniques applied to X-ray and CT-scan medical images for the detection of COVID-19. In this paper, we used four powerful pre-trained CNN models, VGG16, Dense...

Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach.

European journal of nuclear medicine and molecular imaging
PURPOSE: Post-stroke cognitive impairment can affect up to one third of stroke survivors. Since cognitive function greatly contributes to patients' quality of life, an objective quantitative biomarker for early prediction of dementia after stroke is ...

COVID-19 detection from lung CT-Scans using a fuzzy integral-based CNN ensemble.

Computers in biology and medicine
The COVID-19 pandemic has collapsed the public healthcare systems, along with severely damaging the economy of the world. The SARS-CoV-2 virus also known as the coronavirus, led to community spread, causing the death of more than a million people wor...

Deep Learning-based Reconstruction for Lower-Dose Pediatric CT: Technical Principles, Image Characteristics, and Clinical Implementations.

Radiographics : a review publication of the Radiological Society of North America, Inc
Optimizing the CT acquisition parameters to obtain diagnostic image quality at the lowest possible radiation dose is crucial in the radiosensitive pediatric population. The image quality of low-dose CT can be severely degraded by increased image nois...

Retrospective study of deep learning to reduce noise in non-contrast head CT images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
PURPOSE: Presented herein is a novel CT denoising method uses a skip residual encoder-decoder framework with group convolutions and a novel loss function to improve the subjective and objective image quality for improved disease detection in patients...

Robustness of deep learning segmentation of cardiac substructures in noncontrast computed tomography for breast cancer radiotherapy.

Medical physics
PURPOSE: To develop and evaluate deep learning-based autosegmentation of cardiac substructures from noncontrast planning computed tomography (CT) images in patients undergoing breast cancer radiotherapy and to investigate the algorithm sensitivity to...

3D multi-scale, multi-task, and multi-label deep learning for prediction of lymph node metastasis in T1 lung adenocarcinoma patients' CT images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The diagnosis of preoperative lymph node (LN) metastasis is crucial to evaluate possible therapy options for T1 lung adenocarcinoma patients. Radiologists preoperatively diagnose LN metastasis by evaluating signs related to LN metastasis, like spicul...

IFSS-Net: Interactive Few-Shot Siamese Network for Faster Muscle Segmentation and Propagation in Volumetric Ultrasound.

IEEE transactions on medical imaging
We present an accurate, fast and efficient method for segmentation and muscle mask propagation in 3D freehand ultrasound data, towards accurate volume quantification. A deep Siamese 3D Encoder-Decoder network that captures the evolution of the muscle...

Comparison of visibility of in-stent restenosis between conventional- and ultra-high spatial resolution computed tomography: coronary arterial phantom study.

Japanese journal of radiology
PURPOSE: The purposes of this experimental study were to compare the quantitative and qualitative visibility of in-stent restenosis between conventional-resolution CT (CRCT) and ultra-high-resolution CT (U-HRCT) and to investigate the effects of the ...