In recent years, deep learning (DL) has grown popular for medical image segmentation. Despite these advancements, DL-based segmentation still fails to solve some challenges. Some deep learning algorithms have recently made strides by including anatomical information, which is a vital cue for manual segmentation. Unlike standard medical imaging, the unstructured open surgery scenario, combined with our unconstrained configuration with accessible handheld digital cameras, makes this endeavour particularly difficult. Deep learning implementation, on the other hand, is behind in surgery. Despite the importance of visual discrimination during surgery, standardised imaging technologies are not frequently integrated into surgical processes, particularly for open surgery. Computer vision, on the other hand, presents a unique chance to aid and augment surgeons during surgery. As a result of this research, it appears that deep learning's use in medical science is now quite effective in the present period.
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