AIMC Topic: Imaging, Three-Dimensional

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Toward automated detection of microbleeds with anatomical scale localization using deep learning.

Medical image analysis
Cerebral Microbleeds (CMBs) are chronic deposits of small blood products in the brain tissues, which have explicit relation to various cerebrovascular diseases depending on their anatomical location, including cognitive decline, intracerebral hemorrh...

Three-Dimensional Thermal Tomography with Physics-Informed Neural Networks.

Tomography (Ann Arbor, Mich.)
: Accurate reconstruction of internal temperature fields from surface temperature data is critical for applications such as non-invasive thermal imaging, particularly in scenarios involving small temperature gradients, like those in the human body. :...

MediLite3DNet: A lightweight network for segmentation of nasopharyngeal airways.

Medical & biological engineering & computing
The precise segmentation and three-dimensional reconstruction of the nasopharyngeal airway are crucial for the diagnosis and treatment of adenoid hypertrophy in children. However, traditional methods face challenges such as information loss and low c...

3D full-dose brain-PET volume recovery from low-dose data through deep learning: quantitative assessment and clinical evaluation.

European radiology
OBJECTIVES: Low-dose (LD) PET imaging would lead to reduced image quality and diagnostic efficacy. We propose a deep learning (DL) method to reduce radiotracer dosage for PET studies while maintaining diagnostic quality.

Enhancement and evaluation for deep learning-based classification of volumetric neuroimaging with 3D-to-2D knowledge distillation.

Scientific reports
The application of deep learning techniques for the analysis of neuroimaging has been increasing recently. The 3D Convolutional Neural Network (CNN) technology, which is commonly adopted to encode volumetric information, requires a large number of da...

Cell quantification at the osteochondral interface from synchrotron radiation phase contrast micro-computed tomography images using a deep learning approach.

Scientific reports
Osteochondral interface consists of two tissues: the calcified cartilage (CC) containing chondrocytes, and subchondral bone (SCB) containing osteocytes that interact with each other. In this study, we propose a new method for the three-dimensional (3...

Deep learning methods for 3D magnetic resonance image denoising, bias field and motion artifact correction: a comprehensive review.

Physics in medicine and biology
Magnetic resonance imaging (MRI) provides detailed structural information of the internal body organs and soft tissue regions of a patient in clinical diagnosis for disease detection, localization, and progress monitoring. MRI scanner hardware manufa...

Artificial intelligence measured 3D lumbosacral body composition and clinical outcomes in rectal cancer patients.

ANZ journal of surgery
INTRODUCTION: Patient body composition (BC) has been shown to help predict clinical outcomes in rectal cancer patients. Artificial intelligence algorithms have allowed for easier acquisition of BC measurements, creating a comprehensive BC profile in ...

Automatic Segmentation of Abdominal Aortic Aneurysms From Time-Resolved 3-D Ultrasound Images Using Deep Learning.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Abdominal aortic aneurysms (AAAs) are rupture-prone dilatations of the aorta. In current clinical practice, the maximal diameter of AAAs is monitored with 2-D ultrasound to estimate their rupture risk. Recent studies have shown that 3-D and mechanica...