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

摘要

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