AIMC Topic: Image Processing, Computer-Assisted

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Iterative reconstruction deep learning image reconstruction: comparison of image quality and diagnostic accuracy of arterial stenosis in low-dose lower extremity CT angiography.

The British journal of radiology
OBJECTIVE: To compare image quality and diagnostic accuracy of arterial stenosis in low-dose lower-extremity CT angiography (CTA) between adaptive statistical iterative reconstruction-V (ASIR-V) and deep learning image reconstruction (DLIR) algorithm...

Use of synthetic images for training a deep learning model for weed detection and biomass estimation in cotton.

Scientific reports
Site-specific treatment of weeds in agricultural landscapes has been gaining importance in recent years due to economic savings and minimal impact on the environment. Different detection methods have been developed and tested for precision weed manag...

Deep learning for computational cytology: A survey.

Medical image analysis
Computational cytology is a critical, rapid-developing, yet challenging topic in medical image computing concerned with analyzing digitized cytology images by computer-aided technologies for cancer screening. Recently, an increasing number of deep le...

Semantic segmentation method for myocardial contrast echocardiogram based on DeepLabV3+ deep learning architecture.

Mathematical biosciences and engineering : MBE
Myocardial contrast echocardiography (MCE) has been proposed as a method to assess myocardial perfusion for the detection of coronary artery diseases in a non-invasive way. As a critical step of automatic MCE perfusion quantification, myocardium segm...

Feasibility of deep learning k-space-to-image reconstruction for diffusion weighted imaging in patients with breast cancers: Focus on image quality and reduced scan time.

European journal of radiology
PURPOSE: This study aimed to evaluate the feasibility of accelerated DLR (deep learning reconstruction) single-shot echo planar imaging (ss-EPI) for diffusion-weighted image (DWI) in patients with breast cancers in comparison to conventional ss-EPI.

The impact of artificial intelligence on radiography as a profession: A narrative review.

Journal of medical imaging and radiation sciences
BACKGROUND AND PURPOSE: Artificial intelligence (AI) algorithms, particularly deep learning, have made significant strides in image recognition and classification, providing remarkable diagnostic accuracy to various diseases. This domain of AI has be...

P-ResUnet: Segmentation of brain tissue with Purified Residual Unet.

Computers in biology and medicine
Brain tissue of Magnetic Resonance Imaging is precisely segmented and quantified, which aids in the diagnosis of neurological diseases such as epilepsy, Alzheimer's, and multiple sclerosis. Recently, UNet-like architectures are widely used for medica...

Patient-specific daily updated deep learning auto-segmentation for MRI-guided adaptive radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Deep Learning (DL) technique has shown great potential but still has limited success in online contouring for MR-guided adaptive radiotherapy (MRgART). This study proposed a patient-specific DL auto-segmentation (DLAS) strateg...

Model-informed unsupervised deep learning approaches to frequency and phase correction of MRS signals.

Magnetic resonance in medicine
PURPOSE: A supervised deep learning (DL) approach for frequency and phase correction (FPC) of MRS data recently showed encouraging results, but obtaining transients with labels for supervised learning is challenging. This work investigates the feasib...

Deep Learning-Based Segmentation of Cryo-Electron Tomograms.

Journal of visualized experiments : JoVE
Cryo-electron tomography (cryo-ET) allows researchers to image cells in their native, hydrated state at the highest resolution currently possible. The technique has several limitations, however, that make analyzing the data it generates time-intensiv...