The objective of this article is to define the different stages involved in the 3D reconstruction of arteries and to review, from our experience and from the literature, the solutions already proposed. A full reconstruction framework includes the characterization of the imaging device (in terms of distortion and calibration), the specificity of the image acquisition process, the preprocessing that can be applied, the detection of the vascular structures, the 2D feature formation, the reconstruction itself, and the visualization aspects. They are examined according to a computer vision approach where two or three views are assumed to be available. Their generalization to temporal image sequences are also considered. Some of the material reported here is unpublished. The article allows the reader to identify the true critical issues that are not often clearly mentioned in the literature and the challenges that they convey. A final discussion presents a few perspectives in this area of research.
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Radiology
January 2025
From the Institute of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, Munich 81675, Germany.
Background Studies have explored the application of multimodal large language models (LLMs) in radiologic differential diagnosis. Yet, how different multimodal input combinations affect diagnostic performance is not well understood. Purpose To evaluate the impact of varying multimodal input elements on the accuracy of OpenAI's GPT-4 with vision (GPT-4V)-based brain MRI differential diagnosis.
View Article and Find Full Text PDFFront Public Health
January 2025
Department of Ophthalmology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Introduction: Diabetic retinopathy grading plays a vital role in the diagnosis and treatment of patients. In practice, this task mainly relies on manual inspection using human visual system. However, the human visual system-based screening process is labor-intensive, time-consuming, and error-prone.
View Article and Find Full Text PDFFront Comput Neurosci
January 2025
Data Science and Analytics Innovation Center, University of Missouri-Kansas City, Kansas City, MO, United States.
Sci Rep
January 2025
Department of Robotics, Hanyang University, Ansan, 15588, Republic of Korea.
Diabetic retinopathy (DR) presents a significant concern among diabetic patients, often leading to vision impairment or blindness if left untreated. Traditional diagnosis methods are prone to human error, necessitating accurate alternatives. While various computer-aided systems have been developed to assist in DR detection, there remains a need for accurate and efficient methods to classify its stages.
View Article and Find Full Text PDFInj Prev
January 2025
Carnegie Applied Rugby Research (CARR) centre, Carnegie School of Sport, Leeds Beckett University, Leeds, UK.
Background: Head-on-head impacts are a risk factor for concussion, which is a concern for sports. Computer vision frameworks may provide an automated process to identify head-on-head impacts, although this has not been applied or evaluated in rugby.
Methods: This study developed and evaluated a novel computer vision framework to automatically classify head-on-head and non-head-on-head impacts.
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