In this review, the author discusses the significance of camera-based tracking and mapping in medical computer vision. The content covers various medical specialties such as orthopaedics, obstetrics, neurosurgery, gastroenterology, cardiology, pulmonology, urology, otorhinolaryngology (ENT), plastic surgery, and general surgery. Different datasets are explored for quantifying tracking and mapping methods in medical computer vision.
The review delves into the challenges faced in medical computer vision applications due to specific visual nature requirements of medical scenes. It highlights the need for accurate tracking and mapping algorithms to improve patient outcomes, ease clinical tasks, and reduce healthcare costs. Various datasets are discussed that provide ground truth data for training and evaluating algorithms used in camera-based tracking and mapping.
Key metrics such as Intersection over Union (IOU), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), among others are used to evaluate performance. The review also covers feature detection and description methods essential for creating correspondences between images.
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by Adam Schmidt... at arxiv.org 03-04-2024
https://arxiv.org/pdf/2310.11475.pdfDeeper Inquiries