AIMC Topic: Humans

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Multimodal pipe-climbing robot with origami clutches and soft modular legs.

Bioinspiration & biomimetics
In nature, climbing trees and pipes of varying diameters or even navigating inside of hollow pipes and tree holes is easy for some climbing animals and insects. However, today's pipe-climbing robots, which are important for automatically conducting p...

Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy.

Journal of cardiovascular computed tomography
BACKGROUND: Advances in image reconstruction are necessary to decrease radiation exposure from coronary CT angiography (CCTA) further, but iterative reconstruction has been shown to degrade image quality at high levels. Deep-learning image reconstruc...

Haptic Teleoperation of UAVs Through Control Barrier Functions.

IEEE transactions on haptics
This article presents a novel approach to haptic teleoperation. Specifically, we use control barrier functions (CBFs) to generate force feedback to help human operators safely fly quadrotor UAVs. CBFs take a control signal as input and output a contr...

Automatic detection of perforators for microsurgical reconstruction.

Breast (Edinburgh, Scotland)
The deep inferior epigastric perforator (DIEP) is the most commonly used free flap in mastectomy reconstruction. Preoperative imaging techniques are routinely used to detect location, diameter and course of perforators, with direct intervention from ...

SVM-Enabled Intelligent Genetic Algorithmic Model for Realizing Efficient Universal Feature Selection in Breast Cyst Image Acquired via Ultrasound Sensing Systems.

Sensors (Basel, Switzerland)
In recent years, there are several cost-effective intelligent sensing systems such as ultrasound imaging systems for visualizing the internal body structures of the body. Further, such intelligent sensing systems such as ultrasound systems have been ...

Semi-supervised learning for automatic segmentation of the knee from MRI with convolutional neural networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Segmentation is a crucial step in multiple biomechanics and orthopedics applications. The time-intensiveness and expertise requirements of medical image segmentation present a significant bottleneck for corresponding workflo...

Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett's esophagus (with video).

Gastrointestinal endoscopy
BACKGROUND AND AIMS: The visual detection of early esophageal neoplasia (high-grade dysplasia and T1 cancer) in Barrett's esophagus (BE) with white-light and virtual chromoendoscopy still remains challenging. The aim of this study was to assess wheth...

Learning a cortical parcellation of the brain robust to the MRI segmentation with convolutional neural networks.

Medical image analysis
The parcellation of the human cortex into meaningful anatomical units is a common step of various neuroimaging studies. There have been multiple successful efforts to process magnetic resonance (MR) brain images automatically and identify specific an...

Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering.

Medical image analysis
Percutaneous coronary intervention (PCI) is typically performed with image guidance using X-ray angiograms in which coronary arteries are opacified with X-ray opaque contrast agents. Interventional cardiologists typically navigate instruments using n...

Improving cardiac MRI convolutional neural network segmentation on small training datasets and dataset shift: A continuous kernel cut approach.

Medical image analysis
Cardiac magnetic resonance imaging (MRI) provides a wealth of imaging biomarkers for cardiovascular disease care and segmentation of cardiac structures is required as a first step in enumerating these biomarkers. Deep convolutional neural networks (C...