AIMC Topic: Image Processing, Computer-Assisted

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Utilizing deep learning techniques to improve image quality and noise reduction in preclinical low-dose PET images in the sinogram domain.

Medical physics
BACKGROUND: Low-dose positron emission tomography (LD-PET) imaging is commonly employed in preclinical research to minimize radiation exposure to animal subjects. However, LD-PET images often exhibit poor quality and high noise levels due to the low ...

Convolutional neural network-based kidney volume estimation from low-dose unenhanced computed tomography scans.

BMC medical imaging
PURPOSE: Kidney volume is important in the management of renal diseases. Unfortunately, the currently available, semi-automated kidney volume determination is time-consuming and prone to errors. Recent advances in its automation are promising but mos...

Employing Atrous Pyramid Convolutional Deep Learning Approach for Detection to Diagnose Breast Cancer Tumors.

Computational intelligence and neuroscience
Breast cancer is among the most common diseases and one of the most common causes of death in the female population worldwide. Early identification of breast cancer improves survival. Therefore, radiologists will be able to make more accurate diagnos...

Feasibility of Monte Carlo dropout-based uncertainty maps to evaluate deep learning-based synthetic CTs for adaptive proton therapy.

Medical physics
BACKGROUND: Deep learning has shown promising results to generate MRI-based synthetic CTs and to enable accurate proton dose calculations on MRIs. For clinical implementation of synthetic CTs, quality assurance tools that verify their quality and rel...

Study of multistep Dense U-Net-based automatic segmentation for head MRI scans.

Medical physics
BACKGROUND: Despite extensive efforts to obtain accurate segmentation of magnetic resonance imaging (MRI) scans of a head, it remains challenging primarily due to variations in intensity distribution, which depend on the equipment and parameters used...

ST-ITEF: Spatio-Temporal Intraoperative Task Estimating Framework to recognize surgical phase and predict instrument path based on multi-object tracking in keratoplasty.

Medical image analysis
Computer-assisted cognition guidance for surgical robotics by computer vision is a potential future outcome, which could facilitate the surgery for both operation accuracy and autonomy level. In this paper, multiple-object segmentation and feature ex...

Development of a deep learning based image processing tool for enhanced organoid analysis.

Scientific reports
Contrary to 2D cells, 3D organoid structures are composed of diverse cell types and exhibit morphologies of various sizes. Although researchers frequently monitor morphological changes, analyzing every structure with the naked eye is difficult. Given...

Utilizing deep learning via the 3D U-net neural network for the delineation of brain stroke lesions in MRI image.

Scientific reports
The segmentation of acute stroke lesions plays a vital role in healthcare by assisting doctors in making prompt and well-informed treatment choices. Although Magnetic Resonance Imaging (MRI) is a time-intensive procedure, it produces high-fidelity im...

SIMPLEX: Multiple phase-cycled bSSFP quantitative magnetization transfer imaging with physic-guided simulation learning of neural network.

NeuroImage
Most quantitative magnetization transfer (qMT) imaging methods require acquiring additional quantitative maps (such as T) for data fitting. A method based on multiple phase-cycled bSSFP was recently proposed to enable high-resolution 3D qMT imaging b...

MRI-based automatic identification and segmentation of extrahepatic cholangiocarcinoma using deep learning network.

BMC cancer
BACKGROUND: Accurate identification of extrahepatic cholangiocarcinoma (ECC) from an image is challenging because of the small size and complex background structure. Therefore, considering the limitation of manual delineation, it's necessary to devel...