RT Journal Article
T1 Instrument Detection and Descriptive Gesture Segmentation on a Robotic Surgical Maneuvers Dataset
A1 Rivas-Blanco, Irene
A1 López-Casado, Carmen
A1 Herrera-López, Juan María
A1 Cabrera-Villa, José
A1 Pérez del Pulgar, Carlos J.
K1 Cirugía - Aparatos e instrumentos
K1 Robótica
AB Large datasets play a crucial role in the progression of surgical robotics, facilitating advancements in the fields of surgical task recognition and automation. Moreover, public datasets enable the comparative analysis of various algorithms and methodologies, thereby assessing their effectiveness and performance. The ROSMA (Robotics Surgical Maneuvers) dataset provides 206 trials of common surgical training tasks performed with the da Vinci Research Kit (dVRK). In this work, we extend the ROSMA dataset with two annotated subsets: ROSMAT24, which contains bounding box annotations for instrument detection, and ROSMAG40, which contains high and low-level gesture annotations. We propose an annotation method that provides independent labels for the right-handed tools and the left-handed tools. For instrument identification, we validate our proposal with a YOLOv4 model in two experimental scenarios. We demonstrate the generalization capabilities of the network to detect instruments in unseen scenarios. On the other hand, for gesture segmentation, we propose two label categories: high-level annotations that describe gestures at a maneuvers level, and low-level annotations that describe gestures at a fine-grain level. To validate this proposal, we have designed a recurrent neural network based on a bidirectional long-short term memory layer. We present results for four cross-validation experimental setups, reaching up to a 77.35% mAP.
PB MDPI
YR 2024
FD 2024
LK https://hdl.handle.net/10630/32775
UL https://hdl.handle.net/10630/32775
LA eng
NO Rivas-Blanco, I.; López-Casado, C.; Herrera-López, J.M.; Cabrera-Villa, J.; Pérez-delPulgar, C.J. Instrument Detection and Descriptive Gesture Segmentation on a Robotic Surgical Maneuvers Dataset. Appl. Sci. 2024, 14, 3701. https:// doi.org/10.3390/app14093701
NO PID2021-125050OA-I00
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026