Robotic-Assisted Surgery approach overcomes the limitations of the traditional laparoscopic and open surgeries. However, one of its major limitations is the lack of force feedback. Since there is no direct interaction between the surgeon and the tissue, there is no way of knowing how much force the surgeon is applying which can result in irreversible injuries. The use of force sensors is not practical since they impose different constraints. Thus, we make use of a neuro-visual approach to estimate the applied forces, in which the 3D shape recovery together with the geometry of motion are used as input to a deep network based on LSTM-RNN architecture. When deep networks are used in real time, pre-processing of data is a key factor to reduce complexity and improve the network performance. A common pre-processing step is dimensionality reduction which attempts to eliminate redundant and insignificant information by selecting a subset of relevant features to use in model construction. In this work, we show the effects of dimensionality reduction in a real-time application: estimating the applied force in Robotic-Assisted Surgeries. According to the results, we demonstrated positive effects of doing dimensionality reduction on deep networks including: faster training, improved network performance, and overfitting prevention. We also show a significant accuracy improvement, ranging from about 33% to 86%, over existing approaches related to force estimation.
Computer-assisted cardiac surgeries had major advances throughout the years and are gaining more popularity over conventional cardiac procedures as they offer many benefits to both patients and surgeons. One obvious advantage is that they enable surgeons to perform delicate tasks on the heart while it is still beating, avoiding the risks associated with cardiac arrest. Consequently, the surgical system needs to accurately compensate the physiological motion of the heart which is a very challenging task in medical robotics since there exist different sources of disturbances. One of which is the bright light reflections, known as specular highlights, that appear on the glossy surface of the heart and partially occlude the field of view. This work is focused on developing a robust approach that accurately detects and removes those highlights to reduce their disturbance to the surgeon and the motion compensation algorithm. As a first step, we exploit both color attributes and Fuzzy edge detector to identify specular regions in each acquired image frame. These two techniques together work as restricted thresholding and are able to accurately identify specular regions. Then, in order to eliminate the specularity artifact and give the surgeon a better perception of the heart, the second part of our solution is dedicated to correct the detected regions using inpainting to propagate and smooth the results. Our experimental results, which we carry out in realistic datasets, reveal how efficient and precise the proposed solution is, as well as demonstrate its robustness and real-time performance.
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