Deep learning-based classification and segmentation for scalpels

Purpose: Scalpels are typical tools used for cutting in surgery, and the surgical tray is one of the locations where the scalpel is present during surgery. However, there is no known method for the classification and segmentation of multiple types of scalpels. This paper presents a dataset of multiple types of scalpels and a classification and segmentation method that can be applied as a first step for validating segmentation of scalpels and further applications can include identifying scalpels from other tools in different clinical scenarios. Methods: The proposed scalpel dataset contains 6400 images with labeled information of 10 types of scalpels, and a classification and segmentation model for multiple types of scalpels is obtained by training the dataset based on Mask R-CNN. The article concludes with an analysis and evaluation of the network performance, verifying the feasibility of the work. Results: A multi-type scalpel dataset was established, and the classification and segmentation models of multi-type scalpel were obtained by training the Mask R-CNN. The average accuracy and average recall reached 94.19% and 96.61%, respectively, in the classification task and 93.30% and 95.14%, respectively, in the segmentation task. Conclusion: The first scalpel dataset is created covering multiple types of scalpels. And the classification and segmentation of multiple types of scalpels are realized for the first time. This study achieves the classification and segmentation of scalpels in a surgical tray scene, providing a potential solution for scalpel recognition, localization and tracking.