Details

Autor(en) / Beteiligte

• Chen, Ziyang
• Pierini, Alice
• Pollini, Eleonora
• Salama, Raffaella
• Pauvel, Theo
• Lievore, Elena
• Ferrigno, Giancarlo
• De Momi, Elena

Titel

RARPL6: Development of A Clinical Dataset for Surgical Workflow Recognition from Robot-Assisted Radical Prostatectomy with Lymphadenectomy

Ist Teil von

• Proceedings of the 2023 8th International Conference on Biomedical Signal and Image Processing, 2023, p.98-103

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2023

Quelle

ACM Digital Library

Beschreibungen/Notizen

• Surgical workflow recognition has attracted widespread attention in robot-assisted surgery since it can provide surgical context information automatically, which releases the cognitive burden of the surgeons and allows more appropriate surgical decisions. One major dilemma in this community is the limitation of clinical datasets with annotated ground truth, because it requires experienced surgeons to provide specific recognition information during the annotation progress. In this paper, we developed a clinical dataset with annotated workflow information, and we provided a potential baseline for the evaluation of this dataset by predicting different surgical steps. Specifically, our dataset was captured from the robot-assisted radical prostatectomy with lymphadenectomy performed on six patients, using the da Vinci Xi robot at European Institute of Oncology, Milan, Italy, and all annotated outputs concerning various surgical information were obtained under the supervision of an experienced surgeon. Furthermore, an advanced neural network was adopted to predict surgical steps based on this dataset by using two different training strategies (i.e., the entire dataset and the downsampled one for the balance of class), and it presented a potential baseline (0.7825 DICE and 0.7918 DICE, respectively). It is expected that this dataset could promote the development of surgical workflow recognition in the medical image community, and this dataset is now accessible at the link: https://zenodo.org/record/7644037.