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Conventional therapy approaches limit surgeons' dexterity control due tolimited field-of-view. With the advent of robot-assisted surgery, there hasbeen a paradigm shift in medical technology for minimally invasive surgery.However, it is very challenging to track the position of the surgicalinstruments in a surgical scene, and accurate detection & identification ofsurgical tools is paramount. Deep learning-based semantic segmentation inframes of surgery videos has the potential to facilitate this task. In thiswork, we modify the U-Net architecture named U-NetPlus, by introducing apre-trained encoder and re-design the decoder part, by replacing the transposedconvolution operation with an upsampling operation based on nearest-neighbor(NN) interpolation. To further improve performance, we also employ a very fastand flexible data augmentation technique. We trained the framework on 8 x 225frame sequences of robotic surgical videos, available through the MICCAI 2017EndoVis Challenge dataset and tested it on 8 x 75 frame and 2 x 300 framevideos. Using our U-NetPlus architecture, we report a 90.20% DICE for binarysegmentation, 76.26% DICE for instrument part segmentation, and 46.07% forinstrument type (i.e., all instruments) segmentation, outperforming the resultsof previous techniques implemented and tested on these data.

U-NetPlus: A Modified Encoder-Decoder U-Net Architecture for Semantic and Instance Segmentation of Surgical Instrument