Robot-assisted surgery is an emerging technology which has undergone rapid growth with the development of robotics and imaging systems. Innovations in vision, haptics and accurate movements of robot arms have enabled surgeons to perform precise minimally invasive surgeries. Real-time semantic segmentation of the robotic instruments and tissues is a crucial step in robot-assisted surgery. Accurate and efficient segmentation of the surgical scene not only aids in the identification and tracking of instruments but also provided contextual information about the different tissues and instruments being operated with. For this purpose, we have developed a lightweight cascaded convolutional neural network (CNN) to segment the surgical instruments from high-resolution videos obtained from a commercial robotic system. We propose a multi-resolution feature fusion module (MFF) to fuse the feature maps of different dimensions and channels from the auxiliary and main branch. We also introduce a novel way of combining auxiliary loss and adversarial loss to regularize the segmentation model. Auxiliary loss helps the model to learn low-resolution features, and adversarial loss improves the segmentation prediction by learning higher order structural information. The model also consists of a lightweight spatial pyramid pooling (SPP) unit to aggregate rich contextual information in the intermediate stage. We show that our model surpasses existing algorithms for pixelwise segmentation of surgical instruments in both prediction accuracy and segmentation time of high-resolution videos.
The inverse problem of estimating the open-channel flow roughness is solved using an embedded optimization model. Measurement data for flow depths and discharges at several locations and times are used as inputs to the optimization model. The nonlinear optimization model embeds the finite-difference approximations of the governing equations for unsteady flow in an open channel as equality constraints. The Sequential Quadratic Programming Algorithm is used to solve the optimization model. The performance of the proposed parameter estimation model is evaluated for different scenarios of data availability and noise in flow measurement data. Solution results for illustrative problems indicate the potential applicability of the proposed model.
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