Antico, Vukovic, Camps, Sasazawa, Takeda, Le, Jaiprakash, Roberts, Crawford, Fontanarosa, Carneiro (2020) Deep learning for US image quality assessment based on femoral cartilage boundaries detection in autonomous knee arthroscopy IEEE transactions on ultrasonics, ferroelectrics, and frequency control ()
Knee arthroscopy is a complex minimally invasive surgery that can cause unintended injuries to femoral cartilage and/or post-operative complications. Autonomous robotic systems using real time volumetric ultrasound (US) imaging guidance hold potential for reducing significantly these issues and for improving patient outcomes. To enable the robotic system to navigate autonomously in the knee joint, the imaging system should provide the robot with a real-time comprehensive map of the surgical site. To this end, the first step is automatic image quality assessment, to ensure that the boundaries of the relevant knee structures are defined well enough to be detected, outlined and then tracked. In this paper, a recently developed one-class classifier deep learning algorithm was used to discriminate among US images acquired in a simulated surgical scenario on which the femoral cartilage either could or could not be outlined. 38,656 2D US images were extracted from 151 3D US volumes, collected from 6 volunteers, and were labelled as '1' or as '0' when an expert was or was not able to outline the cartilage on the image, respectively. The algorithm was evaluated using the expert labels as ground-truth with a 5-fold cross validation, where each fold was trained and tested on average with 15,640 and 6,246 labelled images, respectively. The algorithm reached a mean accuracy of 78.4 % ± 5.0, mean specificity of 72.5 % ± 9.4, mean sensitivity of 82.8 % ± 5.8 and mean area under the curve of 85 % ± 4.4. In addition, inter and intra observer tests involving two experts were performed on an image subset of 1536 2D US images. Percent agreement values of 0.89 and 0.93 were achieved between two experts (i.e., inter-observer) and by each expert (i.e., intra-observer), respectively. These results show the feasibility of the first essential step in the development of automatic US image acquisition and interpretation systems for autonomous robotic knee arthroscopy.