AIMC Topic: Image Processing, Computer-Assisted

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Knowledge-based and deep learning-based automated chest wall segmentation in magnetic resonance images of extremely dense breasts.

Medical physics
PURPOSE: Segmentation of the chest wall, is an important component of methods for automated analysis of breast magnetic resonance imaging (MRI). Methods reported to date show promising results but have difficulties delineating the muscle border corre...

Intelligent Imaging: Artificial Intelligence Augmented Nuclear Medicine.

Journal of nuclear medicine technology
Artificial intelligence (AI) in nuclear medicine and radiology represents a significant disruptive technology. Although there has been much debate about the impact of AI on the careers of radiologists, the opportunities in nuclear medicine enhance th...

Automatic diagnosis of fungal keratitis using data augmentation and image fusion with deep convolutional neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Fungal keratitis is caused by inflammation of the cornea that results from infection by fungal organisms. The lack of an early effective diagnosis often results in serious complications even blindness. Confocal microscopy i...

Automation of the Timed-Up-and-Go Test Using a Conventional Video Camera.

IEEE journal of biomedical and health informatics
The Timed-Up-and-Go (TUG) test is a simple clinical tool commonly used to quickly assess the mobility of patients. Researchers have endeavored to automate the test using sensors or motion tracking systems to improve its accuracy and to extract more r...

Recognition of peripheral blood cell images using convolutional neural networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Morphological analysis is the starting point for the diagnostic approach of more than 80% of hematological diseases. However, the morphological differentiation among different types of normal and abnormal peripheral blood c...

Improving realism in patient-specific abdominal ultrasound simulation using CycleGANs.

International journal of computer assisted radiology and surgery
PURPOSE: In this paper, we propose to apply generative adversarial neural networks trained with a cycle consistency loss, or CycleGANs, to improve realism in ultrasound (US) simulation from computed tomography (CT) scans.

Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI.

Magnetic resonance in medicine
PURPOSE: This prospective clinical study assesses the feasibility of training a deep neural network (DNN) for intravoxel incoherent motion (IVIM) model fitting to diffusion-weighted MRI (DW-MRI) data and evaluates its performance.

Synthetic CT reconstruction using a deep spatial pyramid convolutional framework for MR-only breast radiotherapy.

Medical physics
PURPOSE: The superior soft-tissue contrast achieved using magnetic resonance imaging (MRI) compared to x-ray computed tomography (CT) has led to the popularization of MRI-guided radiation therapy (MR-IGRT), especially in recent years with the advent ...

MRI super-resolution reconstruction for MRI-guided adaptive radiotherapy using cascaded deep learning: In the presence of limited training data and unknown translation model.

Medical physics
PURPOSE: Deep learning (DL)-based super-resolution (SR) reconstruction for magnetic resonance imaging (MRI) has recently been receiving attention due to the significant improvement in spatial resolution compared to conventional SR techniques. Challen...

Fully automated identification of skin morphology in raster-scan optoacoustic mesoscopy using artificial intelligence.

Medical physics
PURPOSE: Identification of morphological characteristics of skin lesions is of vital importance in diagnosing diseases with dermatological manifestations. This task is often performed manually or in an automated way based on intensity level. Recently...