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From Generalization to Precision: Exploring SAM for Tool Segmentation in Surgical Environments
[article]
2024
arXiv
pre-print
Purpose: Accurate tool segmentation is essential in computer-aided procedures. However, this task conveys challenges due to artifacts' presence and the limited training data in medical scenarios. Methods that generalize to unseen data represent an interesting venue, where zero-shot segmentation presents an option to account for data limitation. Initial exploratory works with the Segment Anything Model (SAM) show that bounding-box-based prompting presents notable zero-short generalization.
arXiv:2402.17972v1
fatcat:ztrdlbfrr5cubhfawbkezi3tme