AIMC Topic: Tomography, X-Ray Computed

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Automated system for classification of COVID-19 infection from lung CT images based on machine learning and deep learning techniques.

Scientific reports
The objectives of our proposed study were as follows: First objective is to segment the CT images using a k-means clustering algorithm for extracting the region of interest and to extract textural features using gray level co-occurrence matrix (GLCM)...

Deep Transfer Learning for COVID-19 Detection and Lesion Recognition Using Chest CT Images.

Computational and mathematical methods in medicine
Starting from December 2019, the global pandemic of coronavirus disease 2019 (COVID-19) is continuously expanding and has caused several millions of deaths worldwide. Fast and accurate diagnostic methods for COVID-19 detection play a vital role in co...

Differentiation of eosinophilic and non-eosinophilic chronic rhinosinusitis on preoperative computed tomography using deep learning.

Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery
OBJECTIVES: This study aimed to develop deep learning (DL) models for differentiating between eosinophilic chronic rhinosinusitis (ECRS) and non-ECRS (NECRS) on preoperative CT.

Development of Deep Learning Technique of Features for the Analysis of Clinical Images Integrated with CANN.

BioMed research international
Computer tomography is an extensively used method for the detection of the disease in the subjects. Basically, computer-aided tomography depending on the artificial intelligence reveals its significance in smart health care monitoring system. Owing t...

Automatic Detection and Segmentation of Ovarian Cancer Using a Multitask Model in Pelvic CT Images.

Oxidative medicine and cellular longevity
Ovarian cancer is one of the most common malignant tumours of female reproductive organs in the world. The pelvic CT scan is a common examination method used for the screening of ovarian cancer, which shows the advantages in safety, efficiency, and p...

Assessment of automatic rib fracture detection on chest CT using a deep learning algorithm.

European radiology
OBJECTIVES: To evaluate deep neural networks for automatic rib fracture detection on thoracic CT scans and to compare its performance with that of attending-level radiologists using a large amount of datasets from multiple medical institutions.

A novel 3D lumbar vertebrae location and segmentation method based on the fusion envelope of 2D hybrid visual projection images.

Computers in biology and medicine
In recent years, fast and precise lumbar vertebrae segmentation technology have been one of the important topics in practical medical diagnosis and assisted medical surgery scenarios. However, most of the existing vertebral segmentation methods are b...

Automated Diagnosis of COVID-19 Using Deep Supervised Autoencoder With Multi-View Features From CT Images.

IEEE/ACM transactions on computational biology and bioinformatics
Accurate and rapid diagnosis of coronavirus disease 2019 (COVID-19) from chest CT scans is of great importance and urgency during the worldwide outbreak. However, radiologists have to distinguish COVID-19 pneumonia from other pneumonia in a large num...

Image quality improvement in low-dose chest CT with deep learning image reconstruction.

Journal of applied clinical medical physics
OBJECTIVES: To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low-dose chest CT in comparison with 40% adaptive statistical iterative reconstruction-Veo (ASiR-V40%) algorithm.

Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Supervised deep learning has become a standard approach to solving medical image segmentation tasks. However, serious difficulties in attaining pixel-level annotations for sufficiently large volumetric datasets in real-life applications have highligh...