Long Term Safety Area Tracking (LT-SAT) with online failure detection and recovery for robotic minimally invasive surgery

Autor: Xiaofei Du, Veronica Penza, Danail Stoyanov, Antonello Forgione, Leonardo S. Mattos, Elena De Momi
Rok vydání: 2018
Předmět:
Computer science
Robotic minimally invasive surgery
Optical flow
Health Informatics
Context (language use)
02 engineering and technology
Bayesian inference
Tracking failure detection
030218 nuclear medicine & medical imaging
03 medical and health sciences
Postoperative Complications
0302 clinical medicine
Robotic Surgical Procedures
Robustness (computer science)
SAFER
Abdomen
Image Processing
Computer-Assisted
Long-term tissue tracking
Model Update Strategy
0202 electrical engineering
electronic engineering
information engineering
Image noise
Humans
Minimally Invasive Surgical Procedures
Computer Simulation
Radiology
Nuclear Medicine and imaging
Computer vision
Radiological and Ultrasound Technology
business.industry
Bayes Theorem
Computer Graphics and Computer-Aided Design
Term (time)
Surgery
Computer-Assisted
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Artificial intelligence
business
Algorithms
Gesture
Zdroj: Medical Image Analysis. 45:13-23
ISSN: 1361-8415
Popis: Despite the benefits introduced by robotic systems in abdominal Minimally Invasive Surgery (MIS), major complications can still affect the outcome of the procedure, such as intra-operative bleeding. One of the causes is attributed to accidental damages to arteries or veins by the surgical tools, and some of the possible risk factors are related to the lack of sub-surface visibilty. Assistive tools guiding the surgical gestures to prevent these kind of injuries would represent a relevant step towards safer clinical procedures. However, it is still challenging to develop computer vision systems able to fulfill the main requirements: (i) long term robustness, (ii) adaptation to environment/object variation and (iii) real time processing. The purpose of this paper is to develop computer vision algorithms to robustly track soft tissue areas (Safety Area, SA), defined intra-operatively by the surgeon based on the real-time endoscopic images, or registered from a pre-operative surgical plan. We propose a framework to combine an optical flow algorithm with a tracking-by-detection approach in order to be robust against failures caused by: (i) partial occlusion, (ii) total occlusion, (iii) SA out of the field of view, (iv) deformation, (v) illumination changes, (vi) abrupt camera motion, (vii), blur and (viii) smoke. A Bayesian inference-based approach is used to detect the failure of the tracker, based on online context information. A Model Update Strategy (MUpS) is also proposed to improve the SA re-detection after failures, taking into account the changes of appearance of the SA model due to contact with instruments or image noise. The performance of the algorithm was assessed on two datasets, representing ex-vivo organs and in-vivo surgical scenarios. Results show that the proposed framework, enhanced with MUpS, is capable of maintain high tracking performance for extended periods of time ( ≃ 4 min - containing the aforementioned events) with high precision (0.7) and recall (0.8) values, and with a recovery time after a failure between 1 and 8 frames in the worst case.
Databáze: OpenAIRE
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