In surgical robotics, the ability to achieve high levels of autonomy is often limited by the complexity of the surgical scene. Autonomous interaction with soft tissues requires machines able to examine and understand the endoscopic video streams in real-time and identify the features of interest. In this work, we show the first example of spatio-temporal neural networks, based on the U-Net, aimed at segmenting soft tissues in endoscopic images. The networks, equipped with Long Short-Term Memory and Attention Gate cells, can extract the correlation between consecutive frames in an endoscopic video stream, thus enhancing the segmentation's accuracy with respect to the standard U-Net. Initially, three configurations of the spatiotemporal layers are compared to select the best architecture. Afterwards, the parameters of the network are optimised and finally the results are compared with the standard U-Net. An accuracy of 83.77% ± 2.18% and a precision of 78.42% ± 7.38% are achieved by implementing both Long Short Term Memory (LSTM) convolutional layers and Attention Gate blocks. The results, although originated in the context of surgical tissue retraction, could benefit many autonomous tasks such as ablation, suturing and debridement.

We propose a two dimensional model to simulate microtubule dynamics. Microtubules are polymers that are important in many cell functions including cell division. In particular, chemotherapy targets microtubule dynamics in order to slow cancer cell reproduction. Traditional stochastic or chemical models for microtubule dynamics are one-dimensional, focusing on one variable such as length or concentration. We combine a traditional microtubule instability model and a chemical model and propose a two dimensional space for these models to interact. This gives a more realistic simulation of microtubule dynamics as it allows interaction of different microtubules. It can also simulate microtubule movement under different conditions. Our approach quantifies microtubule images and models microtubule dynamics within a synthesis-analysis framework.

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