Name: robotized testing of camera positions to determine ideal configuration for stereo 3d visualization of open-heart surgery
Uploaded: 2021-08-12T14:45:00
Description: Watch this Scientific Journal Video about Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery at JoVE.com

The research was carried out with funding from Vinnova (2017-03728, 2018-05302 and 2018-03651), Heart-Lung Foundation (20180390), Family Kamprad Foundation (20190194), and Anna-Lisa and Sven Eric Lundgren Foundation (2017 and 2018).

The experiments were approved by the local Ethics Committee in Lund, Sweden. The participation was voluntary, and the patients&#39; legal guardians provided informed written consent.
1. Robot setup and configuration
NOTE: This experiment used a dual-arm collaborative industrial robot and the standard control panel with a touch display. The robot is controlled with RobotWare 6.10.01 controller software and robot integrated development environment (IDE) RobotStudio 2019...

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During live surgery, the total time of the experiment used for 3D video data collection was limited to be safe for the patient. If the object is unfocused or overexposed, the data cannot be used. The critical steps are during camera tool calibration and setup (step 2). The camera aperture and focus cannot be changed when the surgery has started; the same lighting conditions and distance should be used during setup and surgery. The camera calibration in steps 2.1-2.4 must be carried out carefully to ensure that the heart ...

In surgery, 3D visualization can be used for education, diagnoses, pre-operative planning, and post-operative evaluation1,2. Realistic depth perception can improve understanding3,4,5,6 of normal and abnormal anatomies. Simple 2D video recordings of surgical procedures are a good start. However, the lack of depth perception can make it hard for the non-surgical colleagues to fully understand the antero-posterior relationships between different anatomical structures and therefore also introduce a risk of misinterpretation of the anatomy7,8,9,10.
The 3D viewing experience is affected by five factors: (1) Camera configuration can either be parallel or toed-in as shown in Figure 1, (2) Baseline distance (the separation between the cameras). (3) Distance to the object of interest and other scene characteristics such as the background. (4) Characteristics of viewing devices such as screen size and viewing position1,11,12,13. (5) Individual preferences of the viewers14,15.
Designing a 3D camera setup begins with the capture of test videos recorded at various camera baseline distances and configurations to be used for subjective or automatic evaluation16,17,18,19,20. The camera distance must be constant to the surgical field to capture sharp images. Fixed focus is preferred because autofocus will adjust to focus on hands, instruments, or heads that may come into view. However, this is not easily achievable when the scene of interest is the surgical field. Operating rooms are restricted access areas because these facilities must be kept clean and sterile. Technical equipment, surgeons, and scrub nurses are often clustered closely around the patient to secure a good visual overview and an efficient workflow. To compare and evaluate the effect of camera positions on the 3D viewing experience, one complete test range of camera positions should be recording the same scene because the object characteristics such as shape, size, and color can affect the 3D viewing experience21.
For the same reason, complete test ranges of camera positions should be repeated on different surgical procedures. The entire sequence of positions must be repeated with high accuracy. In a surgical setting, existing methods that require either manual adjustment of the baseline distance22 or different camera pairs with fixed baseline distances23 are not feasible because of both space and time constraints. To address this challenge, this robotized solution was designed.
The data was collected with a dual-arm collaborative industrial robot mounted in the ceiling in the operating room. Cameras were attached to the wrists of the robot and moved along an arc-shaped trajectory with increasing baseline distance, as shown in Figure 2.
To demonstrate the approach, 10 test series were recorded from 4 different patients with 4 different congenital heart defects. Scenes were chosen when a pause in surgery was feasible: with the beating hearts just before and after surgical repair. Series were also made when the hearts were arrested. The surgeries were paused for 3 min and 20 s to collect forty 5-ssequences with different camera convergence distances and baseline distances to capture the scene. The videos were later post-processed, displayed in 3D for the clinical team, who rated how realistic the 3D video was along a scale from 0-5.
The convergence point for toed-in stereo cameras is where the center points of both images meet. The convergence point can, by principle, be placed either in front, within, or behind the object, see Figure 1A-C. When the convergence point is in front of the object, the object will be captured and displayed left of the midline for the left camera image and right of the midline for the right camera image (Figure 1A). The opposite applies when the convergence point is behind the object (Figure 1B). When the convergence point is on the object, the object will also appear in the midline of the camera images (Figure 1C), which presumably should yield the most comfortable viewing since no squinting is required to merge the images. To achieve comfortable stereo 3D video, the convergence point must be located on, or slightly behind, the object of interest, else the viewer is required to voluntarily squint outwards (exotropia).
The data was collected using a dual-arm collaborative industrial robot to position the cameras (Figure 2A-B). The robot weighs 38 kg without equipment. The robot is intrinsically safe; when it detects an unexpected impact, it stops moving. The robot was programmed to position the 5 Megapixel cameras with C-mount lenses along an arc-shaped trajectory stopping at predetermined baseline distances (Figure 2C). The cameras were attached to the robot hands using adaptor plates, as shown in Figure 3. Each camera recorded at 25 frames per second. Lenses were set at f-stop 1/8 with focus fixed on the object of interest (approximated geometrical center of the heart). Every image frame had a timestamp which was used to synchronize the two video streams.
Offsets between the robot wrist and the camera were calibrated. This can be achieved by aligning the crosshairs of the camera images, as shown in Figure 4. In this setup, the total translational offset from the mounting point on the robot wrist and the center of the camera image sensor was 55.3 mm in the X-direction and 21.2 mm in the Z-direction, displayed in Figure 5. The rotational offsets were calibrated at a convergence distance of 1100 mm and a baseline distance of 50 mm and adjusted manually with the joystick on the robot control panel. The robot in this study had a specified accuracy of 0.02 mm in Cartesian space and 0.01 degrees rotational resolution24. At a radius of 1100 m, an angle difference of 0.01 degrees offsets the center point 0.2 mm. During the full robot motion from 50-240 mm separation, the crosshair for each camera was within 2 mm from the ideal center of convergence.
The baseline distance was increased stepwise by symmetrical separation of the cameras around the center of the field of view in increments of 10 mm ranging from 50-240 mm (Figure 2). The cameras were kept at a standstill for 5 s in each position and moved between the positions at a velocity of 50 mm/s. The convergence point could be adjusted in X and Z directions using a graphical user interface (Figure 6). The robot followed accordingly within its working range.
The accuracy of the convergence point was estimated using the uniform triangles and the variable names in Figure 7A and B. The height &#39;z&#39; was calculated from the convergence distance &#39;R&#39; with the Pythagorean theorem as
<img alt="Equation 1" src="/files/ftp_upload/62786/62786eq01.jpg" />
When the real convergence point was closer than the desired point, as shown in Figure 7A, the error distance &#39;f1&#39; was calculated as
<img alt="Equation 2" src="/files/ftp_upload/62786/62786eq02.jpg" />
Similarly, when the convergence point was distal to the desired point, the error distance &#39;f2&#39; was calculated as
<img alt="Equation 3" src="/files/ftp_upload/62786/62786eq03.jpg" />
Here, &#39;e&#39; was the maximum separation between the crosshairs, at most 2 mm at maximum baseline separation during calibration (D = 240 mm). For R = 1100 mm (z = 1093 mm), the error was less than &#177; 9.2 mm. For R = 1400 mm (z = 1395 mm), the error was &#177; 11.7 mm. That is, the error of the placement of the convergence point was within 1% of the desired. The two test distances of 1100 mm and 1400 mm were therefore well separated.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>2 C-mount lenses (35 mm F2.1, 5 M pixel)</td>
			<td>Tamron</td>
			<td>M112FM35</td>
			<td>Rated for 5 Mpixel</td>
		</tr>
		<tr>
			<td>3D glasses (DLP-link active shutter)</td>
			<td>Celexon</td>
			<td>G1000</td>
			<td>Any compatible 3D glasses can be used</td>
		</tr>
		<tr>
			<td>3D Projector</td>
			<td>Viewsonic</td>
			<td>X10-4K</td>
			<td>Displays 3D in 1080, can be exchanged for other 3D projectors</td>
		</tr>
		<tr>
			<td>6 M2 x 8 screws</td>
			<td></td>
			<td></td>
			<td>To attach the cXimea cameras to the camera adaptor plates</td>
		</tr>
		<tr>
			<td>8 M2.5 x 8 screws</td>
			<td></td>
			<td></td>
			<td>To attach the circular mounting plates to the robot wrist</td>
		</tr>
		<tr>
			<td>8 M5 x 40 screws</td>
			<td></td>
			<td></td>
			<td>To mount the robot</td>
		</tr>
		<tr>
			<td>8 M6 x 10 screws with flat heads</td>
			<td></td>
			<td></td>
			<td>For attaching the circular mounting plate and the camera adaptor plates</td>
		</tr>
		<tr>
			<td>Calibration checker board plate (25 by 25 mm)</td>
			<td></td>
			<td></td>
			<td>Any standard checkerboard can be used, including printed, as long as the grid is clearly visible in the cameras</td>
		</tr>
		<tr>
			<td>Camera adaptor plates, x2</td>
			<td></td>
			<td></td>
			<td>Designed by the authors in robot_camera_adaptor_plates.dwg, milled in aluminium.</td>
		</tr>
		<tr>
			<td>Circular mounting plates, x2</td>
			<td></td>
			<td></td>
			<td>Distributed with the permission of the designer Julius Klein and printed with ABS plastic on an FDM 3D printer. License Tecnalia Research &#38; Innovation 2017. Attached as Mountingplate_ROBOT_SIDE_ 
			NewDesign_4.stl</td>
		</tr>
		<tr>
			<td>Fix focus usb cameras, x2 (5 Mpixel)</td>
			<td>Ximea</td>
			<td>MC050CG-SY-UB</td>
			<td>With Sony IMX250LQR sensor</td>
		</tr>
		<tr>
			<td>Flexpendant</td>
			<td>ABB</td>
			<td>3HAC028357-001</td>
			<td>robot touch display</td>
		</tr>
		<tr>
			<td>Liveview</td>
			<td></td>
			<td></td>
			<td>recording application</td>
		</tr>
		<tr>
			<td>RobotStudio</td>
			<td></td>
			<td></td>
			<td>&#160;robot integrated development environment (IDE)</td>
		</tr>
		<tr>
			<td>USB3 active cables (10.0 m), x2</td>
			<td></td>
			<td></td>
			<td>Thumbscrew lock connector, water proofed.</td>
		</tr>
		<tr>
			<td>YuMi dual-arm robot</td>
			<td>ABB</td>
			<td>IRB14000</td>
			<td></td>
		</tr>
	</tbody>
</table>

robotized testing of camera positions to determine ideal configuration for stereo 3d visualization of open-heart surgery

Stereo 3D video from surgical procedures can be highly valuable for medical education and improve clinical communication. But access to the operating room and the surgical field is restricted. It is a sterile environment, and the physical space is crowded with surgical staff and technical equipment. In this setting, unobscured capture and realistic reproduction of the surgical procedures are difficult. This paper presents a method for rapid and reliable data collection of stereoscopic 3D videos at different camera baseline distances and distances of convergence. To collect test data with minimum interference during surgery, with high precision and repeatability, the cameras were attached to each hand of a dual-arm robot. The robot was ceiling-mounted in the operating room. It was programmed to perform a timed sequence of synchronized camera movements stepping through a range of test positions with baseline distance between 50-240 mm at incremental steps of 10 mm, and at two convergence distances of 1100 mm and 1400 mm. Surgery was paused to allow 40 consecutive 5-s video samples. A total of 10 surgical scenarios were recorded.

An acceptable evaluation video with the right image placed at the top in top-bottom stereoscopic 3D is shown in Video1. A successful sequence should be sharp, focused, and without unsynchronized image frames. Unsynchronized video streams will cause blur, as shown in the file Video 2. The convergence point should be centered horizontally, independent of the camera separation, as seen in Figure 9A,B. When the robot transitions between the posi...

Watch this Scientific Journal Video about Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery at JoVE.com

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

The human depth perception of 3D stereo videos depends on the camera separation, point of convergence, distance to, and familiarity of the object. This paper presents a robotized method for rapid and reliable test data collection during live open-heart surgery to determine the ideal camera configuration.

Stereo 3D video from surgical procedures can be highly valuable for medical education and improve clinical communication. But access to the operating room ...

It enables capturing of high-quality 3D videos for training and education. It can be applied to most operating room settings with open surgery. With a robotized approach, we can quickly and accurately collect the data to evaluate the camera positions.This method is used to rapidly test camera configuration for 3D visions applications. It can also be used to investigate how different subjects perceive depth. To begin mounting the camera to the robot, first, mount the lens to the camera, and then the camera to the adapter plate with three M2 screws.Next, mount the circular mounting plate to the camera adapter plate with four M4 screws to the opposite side of the camera. Once all the cameras are mounted to the robot, observe the mirroring of the resulting assemblies. Mount the adapter plate to the robot wrist with four M2.5 screws.For a table-mounted robot, attach the left camera to the right robot arm. Connect the USB cables to the cameras and the Ubuntu computer. On the robot touch display, press the Menu button and select Stereo 2 to start the robot application.On the main screen, press Go to start for 1, 100 millimeters in the robot application, and wait for the robot to move to the start position. Remove the protective lens cap from the cameras. Place a printed calibration grid at 1, 100 millimeters from the camera sensors.To correctly identify the corresponding squares, place a small screw nut or mark somewhere in enter of the grid. Start the recording application on the Ubuntu computer to start the interface. And then, adjust the aperture and focus on the lens with the aperture and focus rings.In the recording application, check Crosshair to visualize the crosshairs. In the recording application, ensure that the crosshairs align with the calibration grid in the same position in both camera images. After pressing the gear icon in the robot application, position the cameras relative to the patient.Change the X direction by pressing the plus or minus for Hand distance from robot, and Z direction by pressing the plus or minus for Height to capture the surgical field in the images. Change the Y direction by manually moving the robot or patient. After pausing the surgery and informing the OR personnel about starting the experiment, press Record in the recording application, and then press Run Experiment in the robot application.After a while, when the experiment is finished, Done will appear on the touch display. Then, press Stop Recording in the recording application to stop recording. Inform the OR personnel that the experiment has finished and resume the surgery.For evaluation, display the video in top-bottom 3D format with an active 3D projector. The correct evaluation video with a right image placed at the top in top-bottom stereoscopic 3D is shown. A successful sequence should be sharp, focused, and without unsynchronized image frames.The unsynchronized video streams create blur. The convergence point should be centered horizontally, independent of the camera separation. With a very large separation between the right and left images, the brain cannot fuse the images into one 3D image.Several reasons can cause failure in the center positioning of the heart, such as the distance from the convergence point to the heart. For correct configuration of the camera tool coordinate system, ensure that the crosshairs in the recording application point at the same object, the calibration object is in the center position, and the correct debayering format of colors. 3D videos with changing baseline distances enable us to study how depth information affects anatomical understanding among medical staff.

Research

JoVE Journal

Medicine

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