Autonomous Robot for Removing Superficial Traumatic Blood

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.

The Advances in Computer Vision That Are Enabling More Autonomous Actions in Surgery: A Systematic Review of the Literature

This is a review focused on advances and current limitations of computer vision (CV) and how CV can help us obtain to more autonomous actions in surgery. It is a follow-up article to one that we previously published in Sensors entitled, “Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?” As opposed to that article that also discussed issues of machine learning, deep learning and natural language processing, this review will delve deeper into the field of CV. Additionally, non-visual forms of data that can aid computerized robots in the performance of more autonomous actions, such as instrument priors and audio haptics, will also be highlighted. Furthermore, the current existential crisis for surgeons, endoscopists and interventional radiologists regarding more autonomy during procedures will be discussed. In summary, this paper will discuss how to harness the power of CV to keep doctors who do interventions in the loop.

Object Detection and Distance Measurement in Teleoperation

In recent years, teleoperation has experienced rapid development. Numerous teleoperation applications in diverse areas have been reported. Among all teleoperation-related components, computer vision (CV) is treated as one of the must-have technologies, because it allows users to observe remote scenarios. In addition, CV can further help the user to identify and track the desired targets from complex scenes. It has been proven that efficient CV methods can significantly improve the operation accuracy and relieve user’s physical and mental fatigue. Therefore, furthering understanding about CV techniques and reviewing the latest research outcomes is necessary for teleoperation designers. In this context, this review article was composed.