MoSS: Monocular Shape Sensing for Continuum Robots

Continuum robots are promising candidates for interactive tasks in various applications due to their unique shape, compliance, and miniaturization capability. Accurate and real-time shape sensing is essential for such tasks yet remains a challenge. Embedded shape sensing has high hardware complexity and cost, while vision-based methods require stereo setup and struggle to achieve real-time performance. This paper proposes the first eye-to-hand monocular approach to continuum robot shape sensing. Utilizing a deep encoder-decoder network, our method, MoSSNet, eliminates the computation cost of stereo matching and reduces requirements on sensing hardware. In particular, MoSSNet comprises an encoder and three parallel decoders to uncover spatial, length, and contour information from a single RGB image, and then obtains the 3D shape through curve fitting. A two-segment tendon-driven continuum robot is used for data collection and testing, demonstrating accurate (mean shape error of 0.91 mm, or 0.36% of robot length) and real-time (70 fps) shape sensing on real-world data. Additionally, the method is optimized end-to-end and does not require fiducial markers, manual segmentation, or camera calibration. Code and datasets will be made available at https://github.com/ContinuumRoboticsLab/MoSSNet.

Automating Cobb Angle Measurement for Adolescent Idiopathic Scoliosis using Instance Segmentation

Scoliosis is a three-dimensional deformity of the spine, most often diagnosed in childhood. It affects 2-3% of the population, which is approximately seven million people in North America. Currently, the reference standard for assessing scoliosis is based on the manual assignment of Cobb angles at the site of the curvature center. This manual process is time consuming and unreliable as it is affected by inter- and intra-observer variance. To overcome these inaccuracies, machine learning (ML) methods can be used to automate the Cobb angle measurement process. This paper proposes to address the Cobb angle measurement task using YOLACT, an instance segmentation model. The proposed method first segments the vertebrae in an X-Ray image using YOLACT, then it tracks the important landmarks using the minimum bounding box approach. Lastly, the extracted landmarks are used to calculate the corresponding Cobb angles. The model achieved a Symmetric Mean Absolute Percentage Error (SMAPE) score of 10.76%, demonstrating the reliability of this process in both vertebra localization and Cobb angle measurement.