RT info:eu-repo/semantics/article
T1 Real-time tool localization for laparoscopic surgery using convolutional neural network
A1 Benavides Cobos, Diego
A1 Cisnal de la Rica, Ana
A1 Fontúrbel Mediavilla, Carlos
A1 Fuente López, Eusebio de la
A1 Fraile Marinero, Juan Carlos
K1 Artificial intelligence
K1 Biomedical engineering
K1 Ingenieria biomédica
K1 Image processing
K1 Imágenes, Tratamiento de las
K1 Laparoscopic surgery
K1 Abdomen - Cirugía
K1 Cirugía laparoscópica
K1 Abdominal surgery
K1 Abdomen - Cirugía
K1 Real-time data processing
K1 Tiempo real
K1 Neural networks (Computer science)
K1 Redes neuronales (Informática)
K1 Medical technology
K1 1203.04 Inteligencia Artificial
K1 32 Ciencias Médicas
K1 3213.01 Cirugía Abdominal
K1 3314 Tecnología Médica
AB Partially automated robotic systems, such as camera holders, represent a pivotal step towards enhancing efficiency and precision in surgical procedures. Therefore, this paper introduces an approach for real-time tool localization in laparoscopy surgery using convolutional neural networks. The proposed model, based on two Hourglass modules in series, can localize up to two surgical tools simultaneously. This study utilized three datasets: the ITAP dataset, alongside two publicly available datasets, namely Atlas Dione and EndoVis Challenge. Three variations of the Hourglass-based models were proposed, with the best model achieving high accuracy (92.86%) and frame rates (27.64 FPS), suitable for integration into robotic systems. An evaluation on an independent test set yielded slightly lower accuracy, indicating limited generalizability. The model was further analyzed using the Grad-CAM technique to gain insights into its functionality. Overall, this work presents a promising solution for automating aspects of laparoscopic surgery, potentially enhancing surgical efficiency by reducing the need for manual endoscope manipulation.
PB MDPI
SN 1424-8220
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/68303
UL https://uvadoc.uva.es/handle/10324/68303
LA eng
NO Sensors, 2024, Vol. 24, Nº. 13, 4191
NO Producción Científica
DS UVaDOC
RD 12-dic-2024