Date post: | 16-Jul-2015 |
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Irene Rivas Blanco
Department of System Engineering and Automation
University of Malaga
MANEUVERS RECOGNITION IN
LAPAROSCOPIC SURGERY: ARTIFICIAL
NEURAL NETWORK AND HIDDEN
MARKOV MODEL APPROACHES
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I. Introduction
II. Maneuver Recognition System
III. Implementation & Experiments
IV. Conclusions
INDEX
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I. INTRODUCTION
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I. INTRODUCTION
• Surgical assistant robot interfaces:
Direct-Teleoperation Head tracking
Voice commands
Eyes trackingVision algorithms
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I. INTRODUCTION
(HMM) (HMM)
• Intuitive and natural Human-Machine Interface: Maneuver Recognition System
• Based on modeling the surgeon’s movements
• Comparison between two modeling approaches: Artificial Neural Networks (ANN) and Hidden Markov Models (HMM)
Four degrees of freedom
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II. MANEUVER RECOGNITION
SYSTEM
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II. MANUEVER RECOGNITION SYSTEM
Surgical Protocol Maneuver
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II. MANUEVER RECOGNITION SYSTEM
SENSORIAL SYSTEM
PREPROCESSING DATA
CODING DATA
Surgical Tools’ movement
Kalman filter
Two Tracking 3D sensors
ANN Fourier
RECOGNITION SYSTEM
HMM ANN
Maneuver code
Maneuver code
Data numerical description
Modeling of surgeon’s movements
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II. MANUEVER RECOGNITION SYSTEM
• ANN: Recognizes the trajectory of the tools’ tip
FOURIERTrajectories ANNManeuver
Code
Set of vectors
CODING DATA RECOGNITION SYSTEM
• HMM: Recognizes the interaction between the tools
ANNCharacteristic
vector
HMM1
Maneuver Code
Observablecode
CODING DATA
RECOGNITION SYSTEM
HMM2
HMMn
…
MAX
PROB.
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III. IMPLEMENTATION &
EXPERIMENTS
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III. IMPLEMENTATION & EXPERIMENTS
Polaris Spectra
Markers
CODE MANEUVER
1 Cutting
2 Suturing
3 Transporting
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III. IMPLEMENTATION & EXPERIMENTS
• Experiments: maneuvers recognition
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Maneuver Training Validation Hits
Suturing 150 35 94,29%
Cutting 150 35 91,43%
Transporting 150 35 94,29%
ANN
Maneuver Training Validation Hits
Suturing 150 35 88,57%
Cutting 150 35 82,86%
Transporting 150 35 85,71%
HMM
III. IMPLEMENTATION & EXPERIMENTS
• Experiments results:
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IV. CONCLUSIONS
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IV. CONCLUSIONS
• Human-Machine interface to recognize maneuvers during a laparoscopic surgery
• Comparison between two modeling approaches: Artificial Neural Networks and Hidden Markov Models
• ANN is an intuitive approach, but it is based on trajectories analysis in a specific reference frame.
• HMM provides an independent reference movement frame recognition system.