ToolNet: Holistically-nested real-time segmentation of robotic surgical tools
| Autor: | Luis C. Garcia-Peraza-Herrera, Emmanuel Vander Poorten, George Attilakos, Lucas Fidon, Sebastien Ourselin, Caspar Gruijthuijsen, Danail Stoyanov, Alain Devreker, Jan Deprest, Wenqi Li, Tom Vercauteren |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Deep learning 0206 medical engineering Feature extraction Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Process (computing) 02 engineering and technology Machine learning computer.software_genre 020601 biomedical engineering Imaging phantom 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Benchmark (computing) Key (cryptography) Segmentation Artificial intelligence business computer Parametric statistics |
| Zdroj: | IROS |
| Popis: | Real-time tool segmentation from endoscopic videos is an essential part of many computer-assisted robotic surgical systems and of critical importance in robotic surgical data science. We propose two novel deep learning architectures for automatic segmentation of non-rigid surgical instruments. Both methods take advantage of automated deep-learning-based multi-scale feature extraction while trying to maintain an accurate segmentation quality at all resolutions. The two proposed methods encode the multi-scale constraint inside the network architecture. The first proposed architecture enforces it by cascaded aggregation of predictions and the second proposed network does it by means of a holistically-nested architecture where the loss at each scale is taken into account for the optimization process. As the proposed methods are for real-time semantic labeling, both present a reduced number of parameters. We propose the use of parametric rectified linear units for semantic labeling in these small architectures to increase the regularization ability of the design and maintain the segmentation accuracy without overfitting the training sets. We compare the proposed architectures against state-of-the-art fully convolutional networks. We validate our methods using existing benchmark datasets, including ex vivo cases with phantom tissue and different robotic surgical instruments present in the scene. Our results show a statistically significant improved Dice Similarity Coefficient over previous instrument segmentation methods. We analyze our design choices and discuss the key drivers for improving accuracy. Paper accepted at IROS 2017 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|