AIMC Topic: Image Processing, Computer-Assisted

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Deep Learning Hybrid Techniques for Brain Tumor Segmentation.

Sensors (Basel, Switzerland)
Medical images play an important role in medical diagnosis and treatment. Oncologists analyze images to determine the different characteristics of deadly diseases, plan the therapy, and observe the evolution of the disease. The objective of this pape...

Segmentation of trabecular bone microdamage in Xray microCT images using a two-step deep learning method.

Journal of the mechanical behavior of biomedical materials
INTRODUCTION: One of the current approaches to improve our understanding of osteoporosis is to study the development of bone microdamage under mechanical loading. The current practice for evaluating bone microdamage is to quantify damage volume from ...

Explainable multi-module semantic guided attention based network for medical image segmentation.

Computers in biology and medicine
Automated segmentation of medical images is crucial for disease diagnosis and treatment planning. Medical image segmentation has been improved based on the convolutional neural networks (CNNs) models. Unfortunately, they are still limited by scenario...

Strong semantic segmentation for Covid-19 detection: Evaluating the use of deep learning models as a performant tool in radiography.

Radiography (London, England : 1995)
INTRODUCTION: With the increasing number of Covid-19 cases as well as care costs, chest diseases have gained increasing interest in several communities, particularly in medical and computer vision. Clinical and analytical exams are widely recognized ...

Real-Time Ship Segmentation in Maritime Surveillance Videos Using Automatically Annotated Synthetic Datasets.

Sensors (Basel, Switzerland)
This work proposes a new system capable of real-time ship instance segmentation during maritime surveillance missions by unmanned aerial vehicles using an onboard standard RGB camera. The implementation requires two stages: an instance segmentation n...

Segmentation for Multi-Rock Types on Digital Outcrop Photographs Using Deep Learning Techniques.

Sensors (Basel, Switzerland)
The basic identification and classification of sedimentary rocks into sandstone and mudstone are important in the study of sedimentology and they are executed by a sedimentologist. However, such manual activity involves countless hours of observation...

An artificial-intelligence-based age-specific template construction framework for brain structural analysis using magnetic resonance images.

Human brain mapping
It is an essential task to construct brain templates and analyze their anatomical structures in neurological and cognitive science. Generally, templates constructed from magnetic resonance imaging (MRI) of a group of subjects can provide a standard r...

SFA-Net: Scale and Feature Aggregate Network for Retinal Vessel Segmentation.

Journal of healthcare engineering
A U-Net-based network has achieved competitive performance in retinal vessel segmentation. Previous work has focused on using multilevel high-level features to improve segmentation accuracy but has ignored the importance of shallow-level features. In...

Improving myocardial pathology segmentation with U-Net++ and EfficientSeg from multi-sequence cardiac magnetic resonance images.

Computers in biology and medicine
BACKGROUND: Myocardial pathology segmentation plays an utmost role in the diagnosis and treatment of myocardial infarction (MI). However, manual segmentation is time-consuming and labor-intensive, and requires a lot of professional knowledge and clin...