AIMC Topic: Tomography, X-Ray Computed

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Cross-dimensional transfer learning in medical image segmentation with deep learning.

Medical image analysis
Over the last decade, convolutional neural networks have emerged and advanced the state-of-the-art in various image analysis and computer vision applications. The performance of 2D image classification networks is constantly improving and being train...

An investigation into the risk of population bias in deep learning autocontouring.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: To date, data used in the development of Deep Learning-based automatic contouring (DLC) algorithms have been largely sourced from single geographic populations. This study aimed to evaluate the risk of population-based bias by...

Evaluating the Accuracy and Reliability of Blowout Fracture Area Measurement Methods: A Review and the Potential Role of Artificial Intelligence.

The Journal of craniofacial surgery
Blowout fractures are a common type of facial injury that requires accurate measurement of the fracture area for proper treatment planning. This systematic review aimed to summarize and evaluate the current methods for measuring blowout fracture area...

Infection diagnosis in hydrocephalus CT images: a domain enriched attention learning approach.

Journal of neural engineering
. Hydrocephalus is the leading indication for pediatric neurosurgical care worldwide. Identification of postinfectious hydrocephalus (PIH) verses non-postinfectious hydrocephalus, as well as the pathogen involved in PIH is crucial for developing an a...

Two-stage multi-task deep learning framework for simultaneous pelvic bone segmentation and landmark detection from CT images.

International journal of computer assisted radiology and surgery
PURPOSE: Pelvic bone segmentation and landmark definition from computed tomography (CT) images are prerequisite steps for the preoperative planning of total hip arthroplasty. In clinical applications, the diseased pelvic anatomy usually degrades the ...

Diagnostic ability of deep learning in detection of pancreatic tumour.

Scientific reports
Pancreatic cancer is associated with higher mortality rates due to insufficient diagnosis techniques, often diagnosed at an advanced stage when effective treatment is no longer possible. Therefore, automated systems that can detect cancer early are c...

Automated deep learning auto-segmentation of air volumes for MRI-guided online adaptive radiation therapy of abdominal tumors.

Physics in medicine and biology
. In the current MR-Linac online adaptive workflow, air regions on the MR images need to be manually delineated for abdominal targets, and then overridden by air density for dose calculation. Auto-delineation of these regions is desirable for speed p...

Prediction of osteoporosis using MRI and CT scans with unimodal and multimodal deep-learning models.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: Osteoporosis is the systematic degeneration of the human skeleton, with consequences ranging from a reduced quality of life to mortality. Therefore, the prediction of osteoporosis reduces risks and supports patients in taking precautions. De...

Revolution of echocardiographic reporting: the new era of artificial intelligence and natural language processing.

Journal of echocardiography
Artificial intelligence (AI) has been making a significant impact on cardiovascular imaging, transforming everything from data capture to report generation. In the field of echocardiography, AI offers the potential to enhance accuracy, speed up repor...

Glenoid segmentation from computed tomography scans based on a 2-stage deep learning model for glenoid bone loss evaluation.

Journal of shoulder and elbow surgery
BACKGROUND: The best-fitting circle drawn by computed tomography (CT) reconstruction of the en face view of the glenoid bone to measure the bone defect is widely used in clinical application. However, there are still some limitations in practical app...