U-NetPlus: A Modified Encoder-Decoder U-Net Architecture for Semantic and Instance Segmentation of Surgical Instrument
This work addresses the challenge of accurate instrument detection in surgical scenes for medical professionals, but it is incremental as it builds on the existing U-Net architecture with modifications.
The paper tackles the problem of tracking surgical instruments in robot-assisted surgery by proposing U-NetPlus, a modified U-Net architecture for semantic and instance segmentation, achieving DICE scores of 90.20% for binary segmentation, 76.26% for instrument part segmentation, and 46.07% for instrument type segmentation, outperforming previous methods on the MICCAI 2017 EndoVis Challenge dataset.
Conventional therapy approaches limit surgeons' dexterity control due to limited field-of-view. With the advent of robot-assisted surgery, there has been a paradigm shift in medical technology for minimally invasive surgery. However, it is very challenging to track the position of the surgical instruments in a surgical scene, and accurate detection & identification of surgical tools is paramount. Deep learning-based semantic segmentation in frames of surgery videos has the potential to facilitate this task. In this work, we modify the U-Net architecture named U-NetPlus, by introducing a pre-trained encoder and re-design the decoder part, by replacing the transposed convolution operation with an upsampling operation based on nearest-neighbor (NN) interpolation. To further improve performance, we also employ a very fast and flexible data augmentation technique. We trained the framework on 8 x 225 frame sequences of robotic surgical videos, available through the MICCAI 2017 EndoVis Challenge dataset and tested it on 8 x 75 frame and 2 x 300 frame videos. Using our U-NetPlus architecture, we report a 90.20% DICE for binary segmentation, 76.26% DICE for instrument part segmentation, and 46.07% for instrument type (i.e., all instruments) segmentation, outperforming the results of previous techniques implemented and tested on these data.