Keywords

1 Introduction

The liver is a large and soft organ. Several blood vessels (arteries, veins, portals, etc.) present in the liver are not externally visible. Surgeons use X-ray imaging, computed tomography (CT) or magnetic resonance imaging (MRI) to preoperatively determine the position of these vessels in the liver. During surgery, incision points are located by touching the targeted incision site using fingers and feeling for a pulse-beat. The accuracy of this method is poor and can result in bleeding and injury to the blood vessels.

Several surgical support and navigation systems are commercially available [1,2,3]. These systems determine the position of the surgical tools, synchronize with the tomographic images captured prior to surgery, and navigate the tool to the target position. These systems are mainly used for orthopedic, dental, or brain surgery. The body parts targeted in orthopedic or dental surgery are rigid and exhibit negligible deformation. The tissue targeted during brain surgery is soft and is subject to deformation. During surgery, the hard skull bone, which shields the brain, is fixed to the operating table, which results in minimal deformation. These surgery support and navigation systems are successfully implemented in practice owing to the negligible level of deformation achieved before and during the surgery.

The liver is soft and continuously deforms during an operation due to the patient’s breathing, organ pulsation, the incision process, and the manual operation of the surgeon. The shape of the liver must be constantly measured during surgery. Existing support systems are insufficient and cannot be reliably applied to liver surgery. Research on liver surgery support systems has been limited, and no effective system have been developed yet. We have been developing a liver surgery support system with Kansai Medical University Hospital. The proposed system aims to remove the cancer tissue completely and save the liver without causing injury to the inner vessels. In this paper, our developed knife attachment is described. It indicates the proximity of the blood vessels by illuminating light emitting diodes (LEDs) and by generating audible beeping sounds to prevent blood vessel injury (Fig. 1).

Fig. 1.
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Overview of our liver surgical support system

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2 Liver Surgical Support System

The proposed system consists of two depth cameras with different characteristics. The first camera is lower precision but a wide measurement range used for determining the shape of the liver during surgery. We preoperatively prepare a three-dimensional (3D) model using CT or MRI data. The model contains the shape of the liver, inner blood vessels, and tumors. By matching the low precision depth image with the 3D model during surgery, the position of the invisible blood vessels and tumors is estimated. The details are given in [4,5,6,7]. The second camera is higher precision and performs single point measurements using single markers [8] to determine the tip position of the surgical knife. By merging the camera and 3D model information, the distance between the knife tip and the vessels or tumors is calculated and the proximity of the knife to the arteries or portals is determined. The distance between the knife tip and tumor can be calculated [9] and the navigation to remove cancers preformed.

To indicate the proximity to the vessels or tumors, we have developed a surgical knife attachment with LEDs and an audible alarm. We chose a versatile attachment device, which uses a clamp for easy attachment of a knife or other surgical tools.

3 Surgical Knife Attachment with Proximity Indicators

Multiple markers (Fig. 2) are attached to the top of the knife to estimate the tip position during surgery. The attachment is connected to a clamp (GoPro Sportsman Mount), which is easily clipped to the end of a surgical tool (Fig. 3). When the knife approaches a liver part that must not be cut, such as an artery or a portal, the attachment generates an alarm by gradually turning on lighting LEDs and producing a beeping sound. The attachment contains a micro-computer (Arduino Nano), a LED bar module (OSX10201-GYR1) [10], and a piezoelectric speaker (Figs. 4 and 5). The LED module has 10 LEDs arranged in the order of five green, three yellow and two red. The cubic case is fabricated from plastic by using a 3D printer, and measures approximately 70 × 70 × 70 [mm]. The micro-computer is connected to a primary PC through a USB 2.0 interface. This interface is used to supply power and transmit the proximity data indicated by the number of illuminated LEDs. The micro-computer controls the LEDs and the frequency of the beeping sound (Fig. 6). For simplicity, the connection between the PC and micro-computer is currently hardwired. It is not difficult, however, to convert to a wireless interface for future implementations.

Fig. 2.
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Design and size of markers on the surgical knife attachment

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Fig. 3.
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Surgical knife attachment with proximity indicators (The markers in the left image are for demonstration.)

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Fig. 4.
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Circuit diagram

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Fig. 5.
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Inner structure of the attachment (left: main case, right: top lid)

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Fig. 6.
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Experimental results of trajectory of each subject

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4 Knife Tip Position Calibration

The knife tip position is estimated from the markers on the surface of the cubic attachment. Prior to surgery, the relative vectors from each marker to the tip of the knife are calibrated. The calibration procedure is simple and recalibration can be performed as required.

Camera and knife coordinate systems are defined as \( \mathop \sum \nolimits_{c} \) and \( \mathop \sum \nolimits_{k} \) respectively. One of the markers attached to the knife is designated as \( {\text{M}}_{knife} \), and the fixed marker on the table is designated as \( {\text{M}}_{table} \). To acquire a relative vector from \( {\text{M}}_{knife} \) to the knife tip, the knife tip is placed at the origin point \( p_{table}^{c} \) of \( {\text{M}}_{table} \), and the position and posture of each marker are measured in \( \mathop \sum \nolimits_{c} \). The position \( P_{knife}^{c} \) and orientation \( R_{knife}^{c} \) of the marker attached to the knife \( {\text{M}}_{knife} \) are measured in \( \mathop \sum \nolimits_{c} \). The relative vector \( P_{rel}^{c} \) is calculated by the following equation \( {\text{P}}_{\text{rel}}^{\text{k}} \).

$$ P_{rel}^{c} = P_{table}^{c} - P_{knife}^{c} $$

To convert \( P_{rel}^{c} \) to \( \mathop \sum \nolimits_{k} \) coordinates, the following is used.

$$ P_{rel}^{k} = (R_{knife}^{c} )^{ - 1} \cdot P_{rel}^{c} $$

Finally, the knife tip position \( P_{tip}^{c} \) in \( \mathop \sum \nolimits_{c} \) is estimated as follows.

$$ P_{tip}^{c} = R_{knife}^{c} \, \cdot P_{rel}^{k} + P_{knife}^{c} $$

If multiple markers are detected and multiple tip positions are estimated at the same time, the positions are averaged. The estimation error in the knife tip position is less than 1 [mm]. The details of the error evaluation are given in [11].

5 Navigation Experiment and Result

We conducted experiments to validate operator navigation using the attachment. The task is to trace an invisible target circle with the tip of the knife based on the LED information. The beeping sound was not used for this experiment. The target circle is set on a flat horizontal table in front of the subject (Fig. 7). The diameter of the circle is 100 [mm]. To measure the navigation error precisely, the attachment cube was fixed to a hard steel rod (130 [mm] in length, 6 [mm] in diameter) using a strong adhesive (Fig. 8). The LED array is illuminated incrementally in 1 [mm] step as the distance to the target circle is reduced. All LEDs are illuminated when the tip touches the target circle. The six subjects (A to F) are not surgeons, they are undergraduate or postgraduate students, in their twenties, from the Osaka Electro-Communication University.

Fig. 7.
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Experiment environment

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Fig. 8.
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Tool for navigation experiment

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With \( \left( {x_{\text{tip}} , y_{\text{tip}} } \right) \) as the knife tip position, \( (x_{\text{c}} , y_{\text{c}} ) \) as the center of the circle, and \( r \) as the radius of the circle, a distance \( {\text{L}} \) between the knife tip and the circle is calculated from the following equation.

$$ {\text{L}} = \left| {r - \sqrt {(x_{c} - x_{tip} )^{2} + (y_{c} - y_{tip} )^{2} } } \right| $$

The LED’s are gradually illuminated between \( 1 \le {\text{L}} \le 10\, [{\text{mm}}] \). The LED array contains 10 LEDs; therefore, we can navigate over a range in \( \pm 10\,\left[ {\text{mm}} \right] \) of the circle.

The experimental results of the trajectory of the tip position are shown in Fig. 9. The red circle is the target circle and the purple dots show the trajectory. The LEDs turn on within the range between the blue and yellow circles. The average navigation errors and standard deviation for each subject are shown in Fig. 10. The results show that the maximum error is 14 [mm], and the error remained within a LED illumination range of 20 [mm]. These results confirm that the LED based navigation works correctly.

Fig. 9.
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Experimental results of trajectory of each subject

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Fig. 10.
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Navigation errors

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6 Preliminary Experiment for Sterilization

A sterile surgical environment is important. We conducted a preliminary experiment to determine if the marker can be measured while covered with a transparent sterile sheet. The initial results showed that when the marker was loosely covered, there were measurement failures due to light reflection or refraction. However, by ensuring that the marker was tightly covered with no wrinkling (Fig. 11), no problems were encountered and stable measurements were achieved. The transparent sterile sheet used in this experiment was for medical use and was provided by Kyoto University Hospital.

Fig. 11.
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Marker block covered by transparent sterile sheet

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7 Conclusion

We developed a surgical knife attachment that indicates the proximity of the blood vessels by illuminating LEDs and generating beeping sounds to prevent blood vessel injury. To validate operator navigation using this device, navigation experiments using six subject were conducted. Based on the results we conclude that the navigation of this device works well. The maximum navigation error is limited to 14 [mm]. In the future, we will improve the accuracy of navigation, downsize the device, and conduct experiments in an actual surgery support setting.