# Towards Robotic Knee Arthroscopy: Multi-Scale Network for Tissue-Tool Segmentation

arXiv: https://arxiv.org/abs/2110.02657 · HTML: https://mlsanity.com/paper/2110.02657/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 37 references that are in the map.
- First posted on arXiv 6 Oct 2021.

## Builds on (most cited first, 21 of 21)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond](https://mlsanity.com/paper/1904.11492/) (2019)
- [ResUNet++: An Advanced Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1911.07067/) (2019)
- [Domain-adversarial neural networks to address the appearance variability of histopathology images](https://mlsanity.com/paper/1707.06183/) (2017)
- [MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation](https://mlsanity.com/paper/1812.00352/) (2018)
- [Visual Localization Using Sparse Semantic 3D Map](https://mlsanity.com/paper/1904.03803/) (2019)
- [Permutohedral Attention Module for Efficient Non-Local Neural Networks](https://mlsanity.com/paper/1907.00641/) (2019)
- [Improved Diagnosis of Tibiofemoral Cartilage Defects on MRI Images Using Deep Learning](https://mlsanity.com/paper/2012.00144/) (2020)
- [Arthroscopic Multi-Spectral Scene Segmentation Using Deep Learning](https://mlsanity.com/paper/2103.02465/) (2021)
- [3D Semantic Mapping from Arthroscopy using Out-of-distribution Pose and Depth and In-distribution Segmentation Training](https://mlsanity.com/paper/2106.05525/) (2021)
- [Surgery Scene Restoration for Robot Assisted Minimally Invasive Surgery](https://mlsanity.com/paper/2109.02253/) (2021)

## Abstract (arXiv)

Tissue awareness has a great demand to improve surgical accuracy in minimally invasive procedures. In arthroscopy, it is one of the challenging tasks due to surgical sites exhibit limited features and textures. Moreover, arthroscopic surgical video shows high intra-class variations. Arthroscopic videos are recorded with endoscope known as arthroscope which records tissue structures at proximity, therefore, frames contain minimal joint structure. As consequences, fully conventional network-based segmentation model suffers from long- and short- term dependency problems. In this study, we present a densely connected shape aware multi-scale segmentation model which captures multi-scale features and integrates shape features to achieve tissue-tool segmentations. The model has been evaluated with three distinct datasets. Moreover, with the publicly available polyp dataset our proposed model achieved 5.09 % accuracy improvement.