laparoscopic surgery training system

Laparoscopic Surgery Training System

MediTronics Inc.

CEO Alexander Hahn CTO Mark Jung CFO Han-Lim Lee

April 2007

Roles in Project

Alexander Hahn (CEO)

- Software developer, Technical writing Mark Jung (CTO)

- Software and Hardware developer, Finance Management

Han-Lim Lee (CFO)

- Hardware developer, Time management

Presentation Outline

• Background• Goals• Proposed Solution• System Overview• Hardware• Software • Business Case• Budget/Timeline• Conclusion

What’s Laparoscopic Surgery?

Minimally invasive surgery

Gas-inflated abdomen

Laparoscope and tools

Why Laparoscopic Surgery?

Small incision

Speed up recovery times

Minimize post-operative pain

Reduce the chances of infection

Minimize the size of scars

The Problems

Unusual surgery

environment

The Problems

Difficulty in use

of the tools

Current Systems in the market

Pure simulation software - Limitation in getting hands-on experience

- Lack of physical feeling

Pure physical training system - No automated feedback

- Eye examination required

Goals

Providing an physical training system Providing an automated feedback &

evaluation system

A hybrid training system of physical and virtual feature

System Overview SurgiBox Computer

System Overview

Tools

System Overview FSR sensor

System Overview

Sensor feedback circuitry

System OverviewMoving task

System Overview Cutting task

System Overview Suturing task

Overall System

Hardware Outline

Hardware System Overview Force during surgeries FSR vs Strain Gauge FSR Verification Transmitter and Receiver Circuit Alternative Design Option Possible Future Work

H.W. System Overview

Force During Surgeries

Highest Force Peak

= 2.3 N Lowest Force Peak

= 0.2 N For liver,

as low as 0.05 N

http://www.mech.kuleuven.be/micro/pub/medic/Paper_Eurosenso s_2003_MIS_sensor_extended.pdf.

Force Limit

Maximum Force measured to tear off beef 2.0 N

( 0.2N < 2.0N <2.3N)

2.0 N is set as a force limit and correspond to

2.9 Volt in the system.

Force Sensing Resistor

How to measure force?

VS

FSR Strain Gauge

• http://www.drrobot.com/products_item.asp?itemNumber=FSR400

• http://www.omega.com/literature/transactions/volume3/strain.html

Force Sensing Resistor

Advantage: Cheaper Ideal for our system

Advantage: Smaller in Size

Disadvantage: Bigger than Semi-

conductor S.G.

FSR

Disadvantage: Strain Changes

without Gripping

Strain Gauge

FSR Verification FSR 400 is used and currently the smallest fsr in the market

Force (g) 

Resistance (kOhm) 

  Day1 Day2 Day3

20 11.95 12 12

50 10 10.02 10

100 5.9 5.85 6

300 3.2 3.2 3.1

500 1.9 1.88 1.91

1000 1.2 1.2 1.22

2000 0.7 0.73 0.69

FSR VerificationResistance vs Force

0

2

4

6

8

10

12

14

0 200 400 600 800 1000 1200

Force (g)

Re

sis

tan

ce

(k

Oh

m)

Day1 Day2 Day3

Transmitter and Receiver

Transmitter Side:

• Force on the gripper is compared with our limit force (2.9V)

• Analog to digital conversion

• Transfer signal serially to the receiver

Transmitter and Receiver Transmitter Side:

Transmitter and ReceiverReceiver Side:

• Transfer the received data to pc through serial port

• Receives signal from transmitter when limit exceeds

Transmitter and ReceiverReceiver Side:

Transmitter and Receiver Transmitter connected with tool

Transmitter and Receiver FSR attached on tool tip

Transmitter and Receiver Transmitter from top-view

Transmitter and ReceiverReceiver with serial port connected

Alternative Design Option

Without using RF module

Alternative Design Option

Use PCB instead of Vector Board

Future Work - Hardware

Use both FSR and Strain Gauge Research and experiment on real human

tissue for setting force limit Varying force limit according to different

surgery types PCB instead of vector board Research on smaller FSR or other

components to measure force

Test Program – Moving Task

Before moving task After moving task

Test Program – Cutting Task

Before cutting task After cutting task

Test Program – Suturing Task

Before suturing task After suturing task

Image Processing

Final Solution : Colour Quantization Simple Effective

User Interface

Simple Interface Main Control “The Green Arrow”

User Interface

Task Selection Very Basic Controls

User Interface

Task Mode

Evaluation

Performance time

– timer in the test program Gripping force

– FSR sensor Accuracy

– Image processing

Evaluation

Quality > Speed

Problems Encountered

Difficult Programming Language MFC

Serial Data Collection FSR Sensor Data

Image Processing Colours Complexity

Future Work - Software

Modifying our test programs

- providing random shape for cutting

- various target locations for moving Add new test programs

- Knot tying

- Suction Add more feedback sensors

- Checking tightness of suturing/tying task

Budget

Component CostSugiBox and surgical tools SFU Robotics Lab

Computer SFU Robotics Lab

Laparoscope SFU Robotics Lab

Vector boards $24.00

Chip components $15.00 & SFU Robotics Lab

CCD board camera $100.00

FSR sensors $30.59

Batteries and holders $23.84

Color paper, needle and tapes $15.00

Total $208.43

Market Plan

Target market

- Hospital

- Medical school

- Research Laboratory Provide an on-site training

Competitors

Simulab Corporation Physical training

system with digital camera (excluding PC)

$1795.00

http://www.simulab.com/LaparoscopicSurgery.htm

Competitors

Simulab Corporation LabTrainer Skill Set $225.00

http://www.simulab.com/LaparoscopicSurgery.htm

Cost and Selling Price

Estimated Cost - Hardware (SurgiBox, camera, tools, surgical

models, circuits, sensors) ~ $250 - Software (Test & Evaluation program) ~$200 Selling Price - Unit selling price of ~ $585 with 30% of margin - Much lower than Simulab Corporation products ($

2020) - Providing both physical and virtual system product

Timeline - Project ScheduleGantt Chart Planned on January 2007

Timeline - Project Schedule

Revised Schedule Planned on March 2007

- Project Completed by Apr.10th, 2007

Final Schedule on Project completion

- Actual Project Completion on Apr.16th, 2007

Timeline - Project Schedule

Main factors that caused delay

- Hardware and software interface

- Longer integration time than expected

- Image processing complexity

Team Work

Very Few Conflicts Good Communication Even Work Distribution Modulated Tasks Good Mix of Skill Sets Respect

What We Learned (Technical) Background knowledge in laparoscopic surgery - Research works in Dr. Payandeh’s Robotics Research Lab - CESEI Tour and meeting with Dr. Qayumi - Research from papers and webs

Hardware - Microcontroller (PIC), RF transceiver, Voltage converter and

Circuit design, PIC programming in Assembly

Software - MFC - Serial port data reading in C++ - OpenCV and GDI+ Image Processing in C++

What We Learned (Team)

Plan the whole project term Plan the project by month Plan the project by week Plan the project by day Go back up the ladder and make

changes where necessary

Acknowledgements

Supervisor SFU Robotics Lab Dr. Shahram Payandeh

CESEI, Director Dr. Karim Quyami

SFU Alumni Wayne Chan

The End

Questions ?