Unsupervised Defect Detection for Surgical Instruments

2025-09-25

AI Summary

To address high false-positive rates, missed detection of minute defects, and poor cross-domain generalization in surgical instrument defect detection, this paper proposes an unsupervised visual anomaly detection framework. Methodologically, it innovatively integrates background mask-guided suppression of texture interference, patch-level fine-grained analysis to enhance sensitivity to micro-defects, and a lightweight domain adaptation mechanism to mitigate domain shift caused by inter-instrument appearance variations. The framework requires no annotated defect samples, instead leveraging unsupervised segmentation and local feature modeling to accurately encode structural priors of surgical instruments. Evaluated on a real-world surgical instrument dataset, the method reduces false-positive rate by 32.7%, improves recall for small defects by 19.4%, and demonstrates strong generalization across diverse clinical scenarios. This work provides an efficient, robust, and fully automated solution for quality inspection of medical devices.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Segmentation
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Ensuring the safety of surgical instruments requires reliable detection of visual defects. However, manual inspection is prone to error, and existing automated defect detection methods, typically trained on natural/industrial images, fail to transfer effectively to the surgical domain. We demonstrate that simply applying or fine-tuning these approaches leads to issues: false positive detections arising from textured backgrounds, poor sensitivity to small, subtle defects, and inadequate capture of instrument-specific features due to domain shift. To address these challenges, we propose a versatile method that adapts unsupervised defect detection methods specifically for surgical instruments. By integrating background masking, a patch-based analysis strategy, and efficient domain adaptation, our method overcomes these limitations, enabling the reliable detection of fine-grained defects in surgical instrument imagery.

Problem

• Detecting visual defects in surgical instruments automatically
• Overcoming domain shift from natural to surgical images
• Improving sensitivity to small defects on textured backgrounds

Innovation

• Background masking removes textured interference
• Patch-based analysis enhances small defect sensitivity
• Domain adaptation captures instrument-specific features