AIMC Topic: Humans

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Automatic needle tracking using Mask R-CNN for MRI-guided percutaneous interventions.

International journal of computer assisted radiology and surgery
PURPOSE: Accurate needle tracking provides essential information for MRI-guided percutaneous interventions. Passive needle tracking using MR images is challenged by variations of the needle-induced signal void feature in different situations. This wo...

Radiomics and Artificial Intelligence for Renal Mass Characterization.

Radiologic clinics of North America
Radiomics allows for high throughput extraction of quantitative data from images. This is an area of active research as groups try to capture and quantify imaging parameters and convert these into descriptive phenotypes of organs or tumors. Texture a...

Efficient mapping of crash risk at intersections with connected vehicle data and deep learning models.

Accident; analysis and prevention
Traditional methods for identifying crash-prone roadways are mainly based on historical crash data. It usually requires more than three years to collect a sufficient amount of dataset for road safety assessment. However, the emerging connected vehicl...

Automatic CT image segmentation of maxillary sinus based on VGG network and improved V-Net.

International journal of computer assisted radiology and surgery
PURPOSE: The analysis of the maxillary sinus (MS) can provide an assessment for many clinical diagnoses, so accurate CT image segmentation of the MS is essential. However, common segmentation methods are mainly done by experienced doctors manually, a...

Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI.

International journal of computer assisted radiology and surgery
PURPOSE: Management of vestibular schwannoma (VS) is based on tumour size as observed on T1 MRI scans with contrast agent injection. The current clinical practice is to measure the diameter of the tumour in its largest dimension. It has been shown th...

Multi-class motor imagery EEG classification using collaborative representation-based semi-supervised extreme learning machine.

Medical & biological engineering & computing
Both labeled and unlabeled data have been widely used in electroencephalographic (EEG)-based brain-computer interface (BCI). However, labeled EEG samples are generally scarce and expensive to collect, while unlabeled samples are considered to be abun...

Deep learning applications in automatic needle segmentation in ultrasound-guided prostate brachytherapy.

Medical physics
PURPOSE: High-Dose-Rate (HDR) brachytherapy is one of the most effective ways to treat the prostate cancer, which is the second most common cancer in men worldwide. This treatment delivers highly conformal dose through the transperineal needle implan...

Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea.

Frontiers of medicine
Dyspnea is one of the most common manifestations of patients with pulmonary disease, myocardial dysfunction, and neuromuscular disorder, among other conditions. Identifying the causes of dyspnea in clinical practice, especially for the general practi...

Machine learning methods to support personalized neuromusculoskeletal modelling.

Biomechanics and modeling in mechanobiology
Many biomedical, orthopaedic, and industrial applications are emerging that will benefit from personalized neuromusculoskeletal models. Applications include refined diagnostics, prediction of treatment trajectories for neuromusculoskeletal diseases, ...