Journal of Robotics, Networking and Artificial Life, Vol. 5, No. 2 (September 2018) 144-148___________________________________________________________________________________________________________

Automated open wound suturing: detection and planning algorithm
Artur Sagitov1, Tatyana Tsoy1, Hongbing Li2, Evgeni Magid1
1Intelligent Robotics Department, Higher School of Information Technology and Information Systems, Kazan Federal
University, Kremlyovskaya str. 35, Kazan, Russian Federation2
Department of Instrument Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
E-mail: sagitov@it.kfu.ru, lihongbing@sjtu.edu.cn, tt@it.kfu.ru, dr.e.magid@ieee.org
http://kpfu.ru/robolab.html

Abstract
Today one of the key disadvantages of robotic surgery is a lack of a haptic feedback, while in traditional surgery asurgeon uses human haptic senses in all tasks. Providing robots with a haptic feedback have a high potential of
reducing time that is spent by a surgeon on suturing subtasks and helping to reduce a surgeon fatigue. Decreasing
manual input of a surgeon enables remote surgery even under long communication links with significant latency. In
this paper we present a framework of wound detection and suture planning. We plan to implement and test ouralgorithms using KUKA iiwa manipulator.
Keywords: Robotics, robot assisted surgery, medical robotics, manipulator, algorithm, modelling, trajectory.

suturing procedure decreased errors at all skill levels.
Although it did not increase the task performance speed,some benefits in precision were noted.
Existing manual suture techniques could betransferred to automatic robot-assisted surgical suture,with all steps of suturing planned, approved and executed
under surgeon expert supervision. Oquendo et al.4
developed a hardware and software system to rate newlaparoscopic suturing exercises automatically based on
previously human assessed experiments. OSATS scores5of their system were comparable with a human expert
assessment. The system facilitates more feedbackintensive surgical training and may yield insights into thefundamental components of surgical skills.
We target to implement procedures of automatic
robot-assisted surgical suture using KUKA iiwa robot.
The robot haptic feedback sensors would facilitate
interactions with soft tissues. The algorithm plans a lineof sutures to hold wound edges in apposition during ahealing process. The line may consist of a single
continuous strand or a series of interrupted suture strands.

1. Introduction
A wound is defined as a body tissue injury that results
into tissue continuity destruction1. Usually injuries arecaused by physical means, and if a tissue had beensignificantly damaged beyond the level of noninterventional synulosis, an elaborated treatment
procedure is required. The procedure includes cleaning
up of foreign objects and dead tissue from a wound,
infection treatment and wound suturing, that would allow
further natural recovery of the wound. The tissue ismaintained in apposition while natural healing processes
strengthen the wound up to the level that it would
withstand mechanical stress without additional supportof sutures, strips, staples, or topical adhesives. Anintervention that allows the wound to go through a repair
process is referred as wound suturing 2.
Robotic assistance may improve precision of surgicaltask performance as suturing is a challenging and timeconsuming task even for experienced surgeons. Zihni etal.3 showed that robotic assistance during intracorporeal

Copyright © 2018, the Authors. Published by Atlantis Press.
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
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conditional distribution of demonstrations, normalizationin time domain and estimation with regard to a reference
trajectory. Their experiments with tying a knot andcutting soft tissues showed that the system is operating in
dynamic conditions robustly, including situations when acontact object changes its stiffness and position.
Jackson and Çavuşoğlu13 described development of
an autonomous needle path planning for a simple
interrupted suture based on well-established manual
suture techniques. Suturing guidelines from surgeon
manuals were analyzed yielding a parametrized path thatspecifies needle approach, bite, reorientation, regrasping
and follow through. The path was optimized to minimize
interaction forces between a tissue and the needle withproper enter/exit locations and orientations. Needle drive
experiments estimated the forces and torques that weregenerated during a suture for both holonomic and nonholonomic drives.
Based on clinical suturing guidelines and using
kinematic model of needle-tissue interaction with asurgeon that specified entry/exit points, Pedram et al.
developed an algorithm for optimal selection of a needle
diameter, shape, and constant curvature path14. The
proposed algorithm was tested experimentally with theRaven II surgical system on a tissue phantom toguarantee minimal tissue trauma and compliance withsuturing requirements.
Yet, all the above mentioned related work required
surgeon input in selecting a suture path with a woundwidth and depth. Jackson and Cavusoglu presented aneedle-tissue interaction model with friction force that
was approximated as a static friction force15. The model
was experimentally validated using a tissue phantom,
allowing a proper modeling of a suture process.
Algorithms that use a stereo image for detection,
initialization, and tracking of a suture thread throughfitting a nonuniform rational B-spline (NURBS) curve
allowed real time tracking a suture thread undergoingtranslations and deformation16. Chanda et al.17 presented
a review for computational modeling of wound suture
and closure mechanics with geometrical modeling,material characterizations, finite element meshing.

The paper concentrates on a procedure for automaticwound parameters detection and intervention planning,
thus providing a first step toward the suture automationand extends our previous work6.
2. Related Work
Autonomous suturing with a help of surgical robots is a
rapidly developing research field with challenges oftransferring sensory-motor human skills to robotic
manipulator systems.7 Previous studies examined various
separate parts of a suture, including knot tying and thread
tracking, or described an entire suturing procedure.
Automated knot-tying for suturing with standard surgicalinstruments using skill transfer through a user
demonstration was developed by Knoll et al.8 Every
recorded user demonstration (a trajectory, sensory
readings etc.) was decomposed into a chainable action
primitives and a knot-tying task templates werereconstructed through feature extraction and fluid-based
adaption of trajectories.
Apprenticeship learning approach with assumptionthat a trajectory should be smooth was demonstrated toautonomously execute learned trajectories for knot-tying
with superior performance by Berg et al. for Berkeley
Surgical Robots9. The experimental results showed that
10 times speedup was achieved comparing to average
speed of the reference demonstrations.
Several autonomous knot tying algorithms withassociated kinematic requirement were discussed by
Kang and Wen implementing algorithms of collaborative
operation of a surgeon and a robot10. Their suture control
strategies included suture length modelling (tracking theeffective length) and tension control, which required tosecure a knot and was influenced by frictional forces.
Supervised automation of multi-throw suturing waspresented by Sen et al., applying a custom low-costmechanical needle guide to reduce needle pose
uncertainty11. Using this guide and a real-time needle
tracking system to provide a closed loop feedback, they
were able to optimize process parameters (a trajectory
and a needle type) to achieve multilateral needle hand-off
for multi-throw suturing.
Osa et al. 12 proposed an online trajectory planningand a force control system for surgery task automationthat is applicable in real time suturing on a master-slave
system with a velocity controller for combined spatial
motion control and force tracking control. The task wasmodeled using Gaussian Process Regression with a

3. Suturing procedures
Our research focuses at open skin wounds. Skin consistsof an epithelium and an underlying dermis, which require
sharp stitching needle in order to minimize tissue trauma.
Sutures are required to withstand a natural skin tension

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3.1. Suturing techniques

while holding wound edges in apposition. A skin closure
suturing technique could be continuous or interrupted,
but the latter is usually preferred18. Each suture strand ispasses only once through the skin, thus reducing a chanceof a cross-contamination across the entire suture line. The
following factors affect successful suturing intervention:
Closing tension. Tension in the suture should be
sufficient to approximate tissue and eliminate dead space,
while being loose enough to prevent tissue ischemia,
necrosis and patient discomfort. Therefore, a proper
control for closing tension is required.
Needle. A surgical needle is attached to a suture
strand to permit repeated passes through the tissue while
suturing tissues. An algorithm should integrate particularneedle type parameters into a preplanning procedure
because multiple variety of needles may be applied.
Suture. A suture is a strand of material being used topull tissue edges together. A suture is characterized withseveral parameters including suture material size, a knot
tensile strength, and a number of strands. In practice thesmallest diameter suture that could hold the mending
wounded tissue is utilized in order to minimize tissue
trauma. Knot tensile strength corresponds to a force thata suture strand can withstand before it breaks while being
knotted. The suture tensile strength should not surpass
the tissue tensile strength, but the sutures should be atleast as strong as regular tissue of wound edges18. The
material size is measured numerically, while both strand
diameter and tensile strength decrease proportionally tothe suture size decrease. Sutures are classified according
to a number of strands they contain. A monofilament
suture contains a single strand of material and encounter
less resistance as it passes through a tissue comparing toa multifilament suture. Multifilament sutures consist of
several strands, twisted or braided together to increase
tensile strength, pliability, and flexibility.
Knot tying. Each knot that is placed for tissues
approximation should be tied with a high precision andheld with a proper tension. A particular knot type to be
used depends on the suture, the depth, and the amount ofstress that will be placed postoperatively. After the knot
is tied, the ends of a surgical gut should be cut.
From wound geometry, wound depth and suturematerial properties we can estimate and characterize thesuture force requirements accurately. For most areas of
the body, except a human face, sutures should be placed
into skin 3–4mm from a wound edge and 5–10 mmapart19.

There are several types of suture techniques. With
regard to continuity we distinguish continuous andinterrupted sutures. Continuous sutures are made with asingle strand that is connected to itself at each end, orwith both ends being connected together. As mechanicalstrength comes from tension being distributed over all
strand length, we should produce enough tension, while
avoiding excess tension that leads to tissue strangulation.
Excessive tension can also lead to full suture failure.
Interrupted sutures combine multiple strands to close awound with each stich being tied separately after aprocedure completion. This provides a more secure
closure, as while individual stitch breaks, the rest of thestitches would hold the wound edges close to each other.
Our long term goal considers continuous and interrupted
sutures with a dynamic model of a surgical thread.

Fig. 1. Continuous (left) and interrupted (right) suture models.

Other types of primary sutures, such as deep sutures,buried sutures, purse-string sutures, and subcuticularsutures, are used for specific purposes. Deep sutures may
be continuous or interrupted sutures, which are entirely
hidden under an epidermal skin layer, and are not further
removed postoperatively. Buried sutures are placed witha knot protruding to the inside, under a layer, which is to
be closed. Purse-string sutures are continuous sutures
being placed around a lumen and tightened like adrawstring to invert the opening.
3.2. Suturing algorithm overview
Figure 2 describes a suturing procedure algorithm.
An image from a robot camera is captured, dimensionalparameters of a wound are estimated and a supervisingsurgeon specifies suture technique parameters (intervaland spacing) and material to be applied. The robot
generates an intervention initial plan that sets up an
exit/entry and a knot point locations. Based upon
calculated points and formalized suture techniques anindividual point to point needle path planning is146

Journal of Robotics, Networking and Artificial Life, Vol. 5, No. 2 (September 2018) 144-148___________________________________________________________________________________________________________

5.

A wound centerline approximation spline isconstructed;
6. An upper and a lower wound suture splines areconstructed by placing points tangentially to local
vicinity by stich width;
7. Exit/entry points are generated with respect to suture
model and placed on boundary splines;
8. Point processing order is assigned according to aselected suturing technique;
9. Knotting points are added at the ends of eachcontinuous stitch.
The combination of ordered list of entry/exit points andknots comprise our initial suture plan.

performed. The suture plan is delivered to the surgeon for
a confirmation, and the plan is executed under his/hersupervision. Sensory data and trajectories are recorded
for post operational analysis for further use in training,
best practices selection, errors and malfunctions
recording, quality control, and possible independent auditof the entire operation. Finally, the suture undergoes
quality control by the supervising surgeon.

Fig. 2. Automatic suture procedure.

3.3. Wound detection and exit/entry points
selection
Using an image from an operation area camera, suturing
procedure parameters are estimated. Since a wound is askin irregularity, a clustering procedure allows possible
wound locations detection. The algorithm for woundboundaries and locations of exit/entry points estimating
works as follows (Fig.3):
1. Low-pass filtering averages out rapid changes inintensity;
2. Mean shift clustering method is used to extract
cluster components;
3. Clusters that are wound representations are selectedfrom cluster components;
4. Feature points are extracted from a wound cluster
image using a topological skeleton transform(medial axis transform);

Fig. 3. Wound borders and exit/entry point evaluated step bystep for a continuous suture. The input photo (top) is obtained
from www.thinglink.com/scene/911241389793083393.

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3.4. Process supervision

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Murnaghan, C. Hutchison and M. Brown, Objective
structured assessment of technical skill (OSATS) for
surgical residents. British J. of surgery, 84 (2), 1997, pp.
273-278.
6. A. Sagitov, H. Li and E. Magid, Toward automated open
wound suturing using haptic feedback: detecting wounds
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7. M. Yip and N. Das, Robot autonomy for surgery. arXiv
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X.-Y. Fu, K. Goldberg, and P. Abbeel, Superhumanperformance of surgical tasks by robots using iterative
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Goldberg, Automating multi-throw multilateral surgicalsuturing with a mechanical needle guide and sequential
convex optimization, IEEE Int. Conf. on Robotics andAutomation, 2016, pp. 4178-4185.
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The automatically generated initial suture plan ispresented to a supervising surgeon, which can modify theplan by moving/adding/deleting points/knots andchanging the order of stitching. After the surgeon
approval the assisting robot can proceed to plan andexecute individual point operations, while the surgeon
continuously observes a current action of the robot, next
actions’ plan and suture tension. Moreover, he/she isauthorized to correct the robot at any moment. All actionsof the assisting robot and sensory data are carefullyrecorded for future analysis. Using artificial machine
intelligence, we can improve outcomes of surgicalprocedures and significantly reduce surgeon fatigue.
4. Conclusions
This paper presented initial stages of autonomous
supervised suturing including a wound boundary
detection and exit/entry points generation, that are
followed by knot points allocation, being specific toparticular selected technique and parameters. Our future
work is to adopt the existing methods in needle path
planning and integrate them into our framework toevaluate performance on KUKA iiwa robot using haptic
feedback and a ZED camera stereo vision for a woundprofile reconstruction.
Acknowledgements
This work was supported by the Russian Foundation for
Basic Research (RFBR) and the Government of the
Republic of Tatarstan, project ID 18-48-160037. Part of
the work was performed according to the Russian
Government Program of Competitive Growth of Kazan
Federal University.
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