Abstract
Minimally invasive surgery (MIS) is among the preferred procedures for treating a number of ailments as patients benefit from fast recovery and reduced blood loss. The trade-off is that surgeons lose direct visual contact with the surgical site and have limited intra-operative imaging techniques for real-time feedback. Computer vision methods as well as segmentation and tracking of the tissues and tools in the video frames, are increasingly being adopted to MIS to alleviate such limitations. So far, most of the advances in MIS have been focused on laparoscopic applications, with scarce literature on knee arthroscopy. Here for the first time, we propose a new method for the automatic segmentation of multiple tissue structures for knee arthroscopy. The training data of 3868 images were collected from 4 cadaver experiments, 5 knees, and manually contoured by two clinicians into four classes: Femur, Anterior Cruciate Ligament (ACL), Tibia, and Meniscus. Our approach adapts the U-net and the U-net++ architectures for this segmentation task. Using the cross-validation experiment, the mean Dice similarity coefficients for Femur, Tibia, ACL, and Meniscus are 0.78, 0.50, 0.41, 0.43 using the U-net and 0.79, 0.50, 0.51, 0.48 using the U-net++. While the reported segmentation method is of great applicability in terms of contextual awareness for the surgical team, it can also be used for medical robotic applications such as SLAM and depth mapping.
Highlights
Unlike open surgery, which involves cutting multiple tissue layers to access the surgical area of interest inside the human body, Minimally invasive surgery (MIS) is conducted via small incisions to reduce surgical trauma and post-operation recovery time
It is expected that the ability to automatically segment and label tissues present in the camera view, similar to what happens in preoperative CT or MRI images [2], can simplify the long learning curve associated with MIS [3]
We report a fully automatic approach for tissue segmentation from knee arthroscopy video
Summary
Unlike open surgery, which involves cutting multiple tissue layers to access the surgical area of interest inside the human body, Minimally invasive surgery (MIS) is conducted via small incisions to reduce surgical trauma and post-operation recovery time. Despite increasing demand for MIS, there are some common drawbacks, namely: limited access to the operating space, reduced field of view (FoV), the lack of haptic feedback, diminished hand-eye coordination, and prolonged. The associate editor coordinating the review of this manuscript and approving it for publication was Hazrat Ali. learning curves and training periods. This leads to extended operation times and increased cost to patients [1]. It is expected that the ability to automatically segment and label tissues present in the camera view, similar to what happens in preoperative CT or MRI images [2], can simplify the long learning curve associated with MIS [3]. For knee arthroscopy, given a video frame, the clinician can only identify Femur with confidence, while other structures, such as Meniscus, Tibia, ACL, and nonstructural tissues (such as fat) remain
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