AIMC Topic: Image Processing, Computer-Assisted

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Automatic skull reconstruction by deep learnable symmetry enforcement.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Every year, thousands of people suffer from skull damage and require personalized implants to fill the cranial cavity. Unfortunately, the waiting time for reconstruction surgery can extend to several weeks or even months, es...

Seafloor debris detection using underwater images and deep learning-driven image restoration: A case study from Koh Tao, Thailand.

Marine pollution bulletin
Traditional detection and monitoring of seafloor debris present considerable challenges due to the high costs associated with underwater imaging devices and the complex environmental conditions in marine ecosystems. In response to these challenges, t...

T2-weighted imaging of rectal cancer using a 3D fast spin echo sequence with and without deep learning reconstruction: A reader study.

Journal of applied clinical medical physics
PURPOSE: To compare image quality and clinical utility of a T2-weighted (T2W) 3-dimensional (3D) fast spin echo (FSE) sequence using deep learning reconstruction (DLR) versus conventional reconstruction for rectal magnetic resonance imaging (MRI).

Leveraging Radiomics and Hybrid Quantum-Classical Convolutional Networks for Non-Invasive Detection of Microsatellite Instability in Colorectal Cancer.

Molecular imaging and biology
PURPOSE: The goal of this study is to create a novel framework for identifying MSI status in colorectal cancer using advanced radiomics and deep learning strategies, aiming to enhance clinical decision-making and improve patient outcomes in oncology.

Robust Myocardial Perfusion MRI Quantification With DeepFermi.

IEEE transactions on bio-medical engineering
Stress perfusion cardiac magnetic resonance is an important technique for examining and assessing the blood supply of the myocardium. Currently, the majority of clinical perfusion scans are evaluated based on visual assessment by experienced clinicia...

Learning-based inference of longitudinal image changes: Applications in embryo development, wound healing, and aging brain.

Proceedings of the National Academy of Sciences of the United States of America
Longitudinal imaging data are routinely acquired for health studies and patient monitoring. A central goal in longitudinal studies is tracking relevant change over time. Traditional methods remove nuisance variation with custom pipelines to focus on ...

A 3D medical image segmentation network based on gated attention blocks and dual-scale cross-attention mechanism.

Scientific reports
In the field of multi-organ 3D medical image segmentation, Convolutional Neural Networks (CNNs) are limited to extracting local feature information, while Transformer-based architectures suffer from high computational complexity and inadequate extrac...

Reinforcement-based leveraging transfer learning for multiclass optical coherence tomography images classification.

Scientific reports
The accurate diagnosis of retinal diseases, such as Diabetic Macular Edema (DME) and Age-related Macular Degeneration (AMD), is essential for preventing vision loss. Optical Coherence Tomography (OCT) imaging plays a crucial role in identifying these...

Application of deep learning for fruit defect recognition in Psidium guajava L.

Scientific reports
Psidium guajava L. is an important tropical and subtropical fruit. Due to its geographical location and suitable climate, Taiwan produces Psidium guajava L. all year round. Quality standardization is therefore a crucial issue. The primary objective w...

IConDiffNet: an unsupervised inverse-consistent diffeomorphic network for medical image registration.

Physics in medicine and biology
Deformable image registration (DIR) is critical in many medical imaging applications. Diffeomorphic transformations, which are smooth invertible mappings with smooth inverses preserve topological properties and are an anatomically plausible means of ...