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.

Autonomous aspirating robot for removing saliva blood mixed liquid in oral surgery.

Saliva blood mixed liquid (SBML) appears in oral surgery, such as scaling and root planning, and it affects surgical vision and causes discomfort to the patient. However, removing SBML, i.e. frequent aspiration of the mixed liquid, is a routine task involving heavy workload and interruption of oral surgery. Therefore, it is valuable to alternate the manual mode by autonomous robotic technique. The robotic system is designed consisting of an RGB-D camera, a manipulator, a disposable oral aspirator. An algorithm is developed for detection of SBML. Path planning method is also addressed for the distal end of the aspirator. A workflow for removing SBML is presented. 95% of the area of the SBML in the oral cavity was removed after liquid aspiration among a group of ten SBML aspiration experiments. This study provides the first result of the autonomous aspirating robot (AAR) for removing SBML in oral surgery, demonstrating that SBML can be removed by the autonomous robot, freeing stomatology surgeon from tedious work.

Biopsy Needle System With a Steerable Concentric Tube and Online Monitoring of Electrical Resistivity and Insertion Forces.

OBJECTIVE: Biopsies are the gold standard for clinical diagnosis. However, a discrepancy between the biopsy sample and target tissue because of misplacement of the biopsy spoon can lead to errors in the diagnosis and subsequent treatment. Thus, correctly determining whether the needle tip is in the tumor is crucial for accurate biopsy results. METHODS: A biopsy needle system was designed with a steerable, flexible, and superelastic concentric tube; electrodes to monitor the electrical resistivity; and load cells to monitor the insertion force. The degrees of freedom were analyzed for two working modes: straight-line and deflection. RESULTS: Experimental results showed that the system could perceive the tissue type in online based on the electrical resistivity. In addition, changes in the insertion force indicated transitions between the interfaces of adjacent tissue layers. CONCLUSION: The two monitoring methods guarantee that the biopsy spoon is at the desired position inside the tumor during an operation. SIGNIFICANCE: The proposed biopsy needle system can be integrated into an autonomous robotic biopsy system.