Name: Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration (Video) | JoVE Journal
Uploaded: 2017-02-23T13:00:00.000+00:00
Duration: 7 min 3 s
Description: Learn to fabricate a sterilizable calibration target for fluid-immersed endoscopes using laser-etched 316 stainless steel. This method enables rapid intra-operative optical distortion correction for fetal surgery.

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5 minutes

This work was supported through an Innovative Engineering for Health award by the Wellcome Trust [WT101957], the Engineering and Physical Sciences Research Council (EPSRC) [NS/A000027/1], and a National Institute for Health Research Biomedical Research Centre UCLH/UCL High Impact Initiative. Jan Deprest is being funded by the Fonds voor Wetenschappelijk Onderzoek Vlaanderen (FWO; JD as clinical researcher 1.8.012.07). Danail Stoyanov receives funding from the EPSRC (EP/N013220/1, EP/N022750/1), the EU-FP7 project CASCADE (FP7-ICT-2913-601021), and the EU-Horizon2020 project EndoVESPA (H2020-ICT- 2015-688592). Sebastien Ourselin receives funding from the EPSRC (EP/H046410/1, EP/J020990/1, EP/K005278) and the MRC (MR/J01107X/1). Marcel Tella is supported by the EPSRC-funded UCL Centre for Doctoral Training in Medical Imaging (EP/L016478/1).

1. Target Fabrication
<ol>
	<li>Sandblasting
	<ol>
		<li>Prepare a 316 stainless steel sheet with a 1.2-mm thickness. Using a pencil or a nail, draw a 40 mm x 40 mm square onto the sheet with the aid of a ruler.</li>
		<li>Cut the drawn square using a manual metal cutter. CAUTION! Watch the fingers.</li>
		<li>Use a file to round the corners and sides of the sample. CAUTION! They are very sharp; be careful.</li>
		<li>Prepare a straight wooden or metal block slightly larger in size than the stainless steel sheet. Place the cut sheet on it; do this in order to avoid bending the sample during sandblasting.</li>
		<li>Place the assembly in the internal blast chamber. Remember to use a dust collector and to tightly seal the internal blast chamber; otherwise, the sand will spread all over during the process. Wear safety goggles to protect the eyes.</li>
		<li>Position a blast gun perpendicular to and at least 4-5 cm away from the metal surface. Apply the foot control for sandblasting. Put the sample on the piece of wood (1-2 cm thick) using a vice, as the high-pressure sand flow can deform the sample. During sand-blasting, hold the sample tightly by the edge of the piece of wood or by using another vice.</li>
		<li>Repeat the sandblasting on the other side if it is desirable to have a calibration pattern engraved on both sides.</li>
	</ol>
	</li>
	<li>Laser patterning
	<ol>
		<li>Design a pattern of asymmetric circles, as shown in Figure 1.</li>
		<li>Prepare a drawing exchange format (DXF) file of the design either using CAD software or another suitable programming language. 
		NOTE: For convenience, a Python application that can generate DXF files for the design mentioned in this paper is provided as part of the compact GUI application16.</li>
		<li>Import the DXF files into the laser cutting software.</li>
		<li>Set up the following parameters for background etching. Laser Power: 40%, Scan Speed: 80 cm/s, Frequency: 4,000 Hz, Number of Passes: 1.</li>
		<li>Set up the following parameters for etching the pattern. Laser Power: 40%, Scan Speed: 2.1 cm/s, Frequency: 4,000 Hz, Number of Passes: 1.</li>
		<li>Put the sample on the working platform and align the cutting pattern using the software.</li>
		<li>After the laser performs the cut, clean the sample by dipping it in alcohol. Do not use any wipes, as they usually leave undesirable residue.</li>
	</ol>
	</li>
	<li>Sterilization
	<ol>
		<li>Wrap the sterilized sample in a sterilization package and insert it in the sterilization unit (autoclave).</li>
		<li>Add water (not distilled water) to the autoclave and follow the user&#39;s guide/manufacturer&#39;s recommendations to sterilize the target.</li>
	</ol>
	</li>
</ol>
2. Peri-operative Calibration
<ol>
	<li>Calibration software
	<ol>
		<li>Install the &#34;endocal&#34; endoscope calibration software package provided on GitHub16 (follow the instructions in the README file therein). 
		NOTE: This software wraps the OpenCV camera calibration module17 in an easy-to-use convenience application. The provided application runs in two modes: online and offline. The online mode acquires the video stream directly from compatible frame-grabber hardware. The offline mode allows for loading endoscope images either from a video file or a folder with a number of video frames saved as image files. See README for supported hardware and detailed instructions on how to use these two modes.</li>
	</ol>
	</li>
	<li>Endoscopic video acquisition 
	NOTE: The following instructions are for online calibration (as described above), but they are also applicable to offline calibration.
	<ol>
		<li>Place the calibration target in a sterile fluid container, such as a gallipot.</li>
		<li>Fill the container with the target fluid or a similar sterile substance. 
		NOTE: For instance, in fetoscopic procedures, the target fluid is amniotic fluid. Since the optical properties of amniotic fluid are similar to saline water18,19, sterile saline water can be used for calibrating the fetoscope.</li>
		<li>Adjust the zoom and sharpness of the endoscope as desired.</li>
		<li>Immerse the endoscope in the fluid and hold it at a distance from the calibration target similar to the distance from the anatomy that the endoscope will later be used at.</li>
		<li>Launch the calibration application and start the camera acquisition.</li>
		<li>Move the tip of the endoscope slightly for different views while keeping the whole calibration pattern in view of the camera. For optimal performance, keep the elliptical legend around the calibration pattern within the circular view of the endoscope. 
		NOTE: Video frames that are usable for calibration are indicated by a virtual pattern overlay, as seen in Figure 3.</li>
		<li>Acquire at least the minimum number of endoscopic camera views required for calibration (as indicated in the endocal window). 
		NOTE: The current version of endocal requires at least 10 endoscopic camera views for calibration, a heuristically selected number of views where the calibration error appears to be minimal and follow a stable pattern20.</li>
		<li>Press the calibration key, as indicated on the endocal window, to start the calibration process using the images acquired so far.</li>
	</ol>
	</li>
	<li>Saving and using the calibration parameters
	<ol>
		<li>Press the indicated calibration key to save the resulting calibration parameters in a YAML (&#34;YAML Ain&#39;t Markup Language&#34;) file21.</li>
		<li>Group the calibration parameters into the camera matrix and distortion coefficients, as explained in the OpenCV camera calibration module17. 
		NOTE: After performing the calibration, the calibration application automatically displays the distortion-corrected image to the right of the original endoscope image.</li>
		<li>Use the distortion-corrected video feed during a fetoscopic procedure for pure visualization or for real-time placental mosaicing11.</li>
	</ol>
	</li>
</ol>

<ol><li>Zhang, Z., Matsushita, Y., Ma, Y. Camera calibration with lens distortion from low-rank textures. CVPR 2011 IEEE Conference on Computer Vision and Pattern Recognition, Washington, D.C., USA, , IEEE Computer Society. 2321-2328 (2011).</li>
<li>Devernay, F., Faugeras, O. D. Automatic calibration and removal of distortion from scenes of structured environments. SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation. 62-72 International Society for Optics and Photonics, , International Society for Optics and Photonics. 62-72 (1995).</li>
<li>Duane, C. B. <a target="_blank" href="https://www.researchgate.net/publication/2478035_Close-Range_Camera_Calibration">Close-range camera calibration.</a> Photogramm. Eng. 37 (8), 855-866 (1971).</li>
<li>Mallon, J., Whelan, P. F. <a target="_blank" href="http://doras.dcu.ie/18667/">Which pattern? biasing aspects of planar calibration patterns and detection methods.</a> Pattern recognition letters. 28 (8), 921-930 (2007).</li>
<li>Balletti, C., Guerra, F., Tsioukas, V., Vernier, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Calibration+of+Action+Cameras+for+Photogrammetric+Purposes%5BTitle%5D)+AND+%22Sensors%22%5BJournal%5D)">Calibration of Action Cameras for Photogrammetric Purposes.</a> Sensors. 14 (9), 17471-17490 (2014).</li>
<li>Heikkila, J. <a target="_blank" href="http://ieeexplore.ieee.org/document/879788/">Geometric camera calibration using circular control points.</a> IEEE Transactions on pattern analysis and machine intelligence. 22 (10), 1066-1077 (2000).</li>
<li>Deprest, J. A., et al. Fetal surgery is a clinical reality. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aDeprest%2C+JA+%22Seminars+in+fetal+and+neonatal+medicine%22">Seminars in fetal and neonatal medicine</a>. 15 (1), Elsevier. 58-67 (2009).</li>
<li>Watanabe, M., Flake, A. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fetal+surgery%3A+Progress+and+perspectives%5BTitle%5D)+AND+%22Advances+in+pediatrics%22%5BJournal%5D)">Fetal surgery: Progress and perspectives.</a> Advances in pediatrics. 57 (1), 353-372 (2010).</li>
<li>Lewi, L., Deprest, J., Hecher, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+vascular+anastomoses+in+monochorionic+twin+pregnancies+and+their+clinical+consequences%5BTitle%5D)+AND+%22American+journal+of+obstetrics+and+gynecology%22%5BJournal%5D)">The vascular anastomoses in monochorionic twin pregnancies and their clinical consequences.</a> American journal of obstetrics and gynecology. 208 (1), 19-30 (2013).</li>
<li>Yamashita, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Miniature+bending+manipulator+for+fetoscopic+intrauterine+laser+therapy+to+treat+twin-to-twin+transfusion+syndrome%5BTitle%5D)+AND+%22Surgical+Endoscopy%22%5BJournal%5D)">Miniature bending manipulator for fetoscopic intrauterine laser therapy to treat twin-to-twin transfusion syndrome.</a> Surgical Endoscopy. 22 (2), 430-435 (2008).</li>
<li>Daga, P., et al. <a target="_blank" href="http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2505941">Real-time mosaicing of fetoscopic videos using SIFT.</a> Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling. 97861R. , International Society for Optics and Photonics. (2016).</li>
<li>Yang, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Image+mapping+of+untracked+free-hand+endoscopic+views+to+an+ultrasound+image-constructed+3D+placenta+model%5BTitle%5D)+AND+%22The+International+Journal+of+Medical+Robotics+and+Computer+Assisted+Surgery%22%5BJournal%5D)">Image mapping of untracked free-hand endoscopic views to an ultrasound image-constructed 3D placenta model.</a> The International Journal of Medical Robotics and Computer Assisted Surgery. 11 (2), 223-234 (2015).</li>
<li>Liao, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fast+image+mapping+of+endoscopic+image+mosaics+with+three-dimensional+ultrasound+image+for+intrauterine+fetal+surgery%5BTitle%5D)+AND+%22Minimally+invasive+therapy+%26+allied+technologies%22%5BJournal%5D)">Fast image mapping of endoscopic image mosaics with three-dimensional ultrasound image for intrauterine fetal surgery.</a> Minimally invasive therapy & allied technologies. 18 (6), 332-340 (2009).</li>
<li>Chadebecq, F., et al. Practical Dry Calibration With Medium Adaptation For Fluid-Immersed Endoscopy. Hamlyn Symposium on Medical Robotics, , (2015).</li>
<li>Wengert, C., Reeff, M., Cattin, P. C., Székely, G., et al. Bildverarbeitung für die Medizin 2006: Algorithmen Systeme Anwendungen. Proceedings des Workshops vom 19. - 21. März 2006 in Hamburg. Handels, H., et al. , Springer. Berlin Heidelberg. 419-423 (2006).</li>
<li>Shakir, D. I. Compact GUI application for optical distortion calibration of endoscopes. , Available from:
 <a target="_blank" href="https://github.com/gift-surg/endocal">https://github.com/gift-surg/endocal</a> (2016).</li>
<li>Camera calibration With OpenCV. , Available from:
 <a target="_blank" href="http://docs.opencv.org/2.4/doc/tutorials/calib3d/camera_calibration/camera_calibration.html">http://docs.opencv.org/2.4/doc/tutorials/calib3d/camera_calibration/camera_calibration.html</a> (2016).</li>
<li>Reeff, M., Gerhard, F., Cattin, P. C., Székely, G. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aReeff%2C+M+%22Mosaicing+of+endoscopic+placenta+images%22">Mosaicing of endoscopic placenta images</a>. , Citeseer. (2011).</li>
<li>Steigman, S. A., Kunisaki, S. M., Wilkins-Haug, L., Takoudes, T. C., Fauza, D. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Optical+properties+of+human+amniotic+fluid%3A+implications+for+videofetoscopic+surgery%5BTitle%5D)+AND+%22Fetal+diagnosis+and+therapy%22%5BJournal%5D)">Optical properties of human amniotic fluid: implications for videofetoscopic surgery.</a> Fetal diagnosis and therapy. 27 (2), 87-90 (2009).</li>
<li>Zhang, Z. <a target="_blank" href="http://ieeexplore.ieee.org/document/888718/">A flexible new technique for camera calibration.</a> IEEE Transactions on Pattern Analysis and Machine Intelligence. 22 (11), 1330-1334 (2000).</li>
<li>Evans, C. C. The Official YAML Web Site. , Available from:
 <a target="_blank" href="http://yaml.org">http://yaml.org</a> (2016).</li>
<li>Bouguet, J. -Y. Camera Calibration Toolbox for Matlab. , Available from:
 <a target="_blank" href="http://www.vision.caltech.edu/bouguetj/calib_doc/">http://www.vision.caltech.edu/bouguetj/calib_doc/</a> (2015).</li>
<li>Common Interfaces of Feature Detectors. , Available from:
	 <a target="_blank" href="http://docs.opencv.org/2.4/modules/features2d/doc/common_interfaces_of_feature_detectors.html">http://docs.opencv.org/2.4/modules/features2d/doc/common_interfaces_of_feature_detectors.html</a> (2016).</li>
<li>Open Source Computer Vision Library. , Available from:
 <a target="_blank" href="https://github.com/opencv/opencv">https://github.com/opencv/opencv</a> (2016).</li>
</ol>

The authors have nothing to disclose.

Sandblasting is an important step in the fabrication process because the raw metal surface prominently reflects the endoscope light, making it impossible for the circles to be detected. It is difficult to distinguish the circles even with the naked eye (see Figure 5). Note that the surface of the target shown was already etched with a laser. However, this does not diminish light reflection.
<img alt="Optical distortion calibration setup; correcting lens distortions; image processing procedure." src="/files/ftp_upload/55298/55298fig5.jpg" data-altupdatedat="1788250015000" data-alt="Figure 5"> 
Figure 5: Calibration target with no sandblasting applied. As seen from the endoscope view on the left, the glare from the endoscope light on the material surface makes it difficult even for the naked eye to distinguish the circles (there is a circle just to the southeast of the large reflection). Note that the surface of this target (i.e., the "background") was already etched, but this is not helpful in the absence of sandblasting. <a href="http://cloudflare.jove.com/files/ftp_upload/55298/55298fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Prior to pattern etching, it is also important to etch the surface of the whole sample. This is necessary because the sandblasted surface has many specular reflections (see Figure 6), which interfere with blob detection.
<img alt="Optical distortion calibration, diagram; visual correction process with marked points for adjustment." src="/files/ftp_upload/55298/55298fig6.jpg" data-altupdatedat="1788250015000" data-alt="Figure 6"> 
Figure 6: Sandblasted surface with no etching. Although not as prominent as the raw metal surface, the relatively small specular reflections (some of which are highlighted with yellow arrows) are still sufficient to prevent blob detection from succeeding, so no calibration can be performed with this target. <a href="http://cloudflare.jove.com/files/ftp_upload/55298/55298fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Applying the laser at different speeds gives different background colors. The background color plays a significant role in the contrast between the circles and the background. Hence, it is vital to determine the optimal background color. For this purpose, a plate with circles etched against a set of different backgrounds was created (see Figure 7). The backgrounds were tested using the feature detection module of OpenCV23, which is used in the OpenCV camera calibration module17. In this work, the target was made of stainless steel, as it is the most common and reliable material used in clinics for medical devices. This material is freely available, not expensive, robust, and easy to sterilize. Other materials could potentially be used for the calibration target, such as aluminum or iodized metals, but this is the scope of future work.
<img alt="Optical distortion calibration diagram, correcting for image distortions using grid pattern analysis." src="/files/ftp_upload/55298/55298fig7.jpg" data-altupdatedat="1788250015000" data-alt="Figure 7"> 
Figure 7: Stainless steel plate featuring a palette of different background colors etched with the laser. Practical experiments were conducted in conjunction with the OpenCV feature detection module to determine which background color gives the optimal result in terms of blob-to-background contrast23. The endoscope view on the left shows the plate. The moderate background colors (i.e., those other that the darkest and lightest ones) in this palette yield better blob detection. <a href="http://cloudflare.jove.com/files/ftp_upload/55298/55298fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
One of the advantages of this work is that performing a calibration using the fabricated target takes 2-3 min. Most of the effort goes to manually stabilizing the endoscope to obtain decent views of the calibration pattern. Using a custom-built endoscope holder could eliminate the need for manual stabilization, which in turn could significantly shorten calibration time.
<img alt="Static equilibrium, surface tension experiment setup, liquid film interaction analysis." src="/files/ftp_upload/55298/55298video1.jpg" data-altupdatedat="1788250015000" data-alt="Video 1"> 
Video 1: Video showing how optical distortion calibration can be performed using the developed calibration target together with the endocal software. <a href="http://cloudflare.jove.com/files/ftp_upload/55298/fetoscope-calibration-480.mov" target="_blank">Please click here to view this video. (Right-click to download.)</a>
An advantage of our work compared to the work of Wengert et al.15 is that the OpenCV camera calibration module17 can be used as is for calibration, without requiring any modification or custom parameterization. Because OpenCV is a well-established and well-maintained software package and is very popular in the computer vision community, using it eliminates the need for writing and maintaining custom software. For the reader's convenience, a compact GUI application is provided16, which the reader can easily install and use to test new calibration targets. One disadvantage of our method compared to Wengert et al.15 is that their method is more robust to occlusions of the pattern, as it does not require the detection of all blobs.
Initially, a calibration target with a checkerboard pattern was fabricated for this work. However, this type of calibration target proved to be unsuitable in experiments due to the difficulty of detecting the corners of the checkerboard squares. Corner detection relies on histogram-based image binarization (see the OpenCV source code24). This implies the need for a clear color contrast between the dark and light squares, which could not be guaranteed with our checkerboard pattern, partially due to specular reflections, like the ones shown in Figure 6. Such specular reflections are present even after background etching; however, the detection of the circles seems to be less sensitive to this shortcoming.
In the current setup, only perpendicular views of the calibration target allow for successful blob detection. This is due to the specular reflections from the target surface hampering blob detection at oblique angles. We are working to further improve the target so as to allow for the acquisition of views at a wider range of angles, which could potentially improve the quality of performed calibrations20.
In the real-time placental mosaicing pipeline that was previously proposed11, the computation of the transformation that maps image pairs relies on the successful detection and grouping of features. Optical distortions, on the other hand, cause a group of features with a rigid geometry to appear different across images. As a consequence, this difference leads to inaccuracies in the computed transformations, which cause drifts in the resulting image mosaics. Because the most prominent optical distortions are present towards the edges, endoscopic images are currently cropped to their innermost regions. A good correction for optical distortions would potentially allow for the incorporation of a larger part of each image into the mosaicing process. The advantage of this method is two-fold. First, it would increase the number of detected features in each image, potentially improving the computation of the image transformations. Second, it would allow for the whole target anatomical surface to be reconstructed in a shorter time.

Camera calibration is a well-known problem in the computer vision field that has been intensively studied over the years1,2,3. A key step of camera calibration procedures is to estimate the parameters of a distortion model, as well as the intrinsic camera parameters, by extracting a grid of points with a known geometry from camera images with sub-pixel accuracy. Calibration targets with a checkerboard pattern featuring black and white squares are commonly used for this purpose. Circular blobs offer an alternative pattern4,5,6.
In recent years, there has been a growing interest in the development of surgical navigation technology for fetal surgery procedures, such as the treatment of twin-to-twin transfusion syndrome (TTTS) on fetuses7,8,9,10. As the field of view of the fetoscope (i.e., an endoscope used in fetal surgical procedures) is very limited, methods for mapping the placental vasculature without the use of external trackers have been proposed to aid TTTS surgery11,12,13. Optical distortions within fetoscopic images have adverse effects on these computational mosaicing methods that rely on visual information extraction11. Thus, there is an unmet need for a cost-efficient and fast tool for peri-operatively calibrating fetoscopes so that optical distortion compensation can be done in real-time during the intervention.
Due to the fact that the fetoscope is immersed in amniotic fluid during the intervention, the refraction index difference between air and amniotic fluid renders classical in-air camera calibration methods unsuitable for fetal surgery procedures. Estimating fluid-immersed camera parameters from in-air camera parameters is a difficult task and requires at least one image of the fluid-immersed calibration target14. Furthermore, peri-operative, fluid-immersed fetoscopic camera calibration is currently impractical due to sterilization requirements and restrictions on the materials allowed in the operating theater. Due to these reasons, calibrating endoscopes for optical distortions is typically not part of the current clinical workflow. The work in this manuscript is an attempt to close this camera calibration gap by designing and producing a sterilizable and practical optical distortion calibration target featuring a pattern of asymmetric circles. Previously, Wengert et al. fabricated a custom calibration device featuring an oxidized aluminum plate as the calibration target. Their method, however, works only in conjunction with the custom calibration algorithm they developed15.

<table><tbody><tr><td>1.2 mm Metal sheet 316 Grade, 40 mm by 40 mm</td><td></td><td></td><td></td></tr><tr><td>Water container at least 50 mm by 50 mm by 30 mm</td><td></td><td></td><td></td></tr><tr><td>A sterilization package</td><td></td><td></td><td></td></tr><tr><td>Saline water</td><td></td><td></td><td></td></tr><tr><td>Manual metal cutter</td><td></td><td></td><td></td></tr><tr><td>A file to round up the corners</td><td></td><td></td><td></td></tr><tr><td>A wooden or metal block 50 mm by 50 mm at least 10 mm thick</td><td></td><td></td><td></td></tr><tr><td>A vice (desirable but not required)</td><td></td><td></td><td></td></tr><tr><td>Sand Blasting machine</td><td></td><td></td><td></td></tr><tr><td>GUI application to create .dxf file with the pattern</td><td></td><td></td><td>https://github.com/gift-surg/endocal</td></tr><tr><td>PC</td><td></td><td></td><td></td></tr><tr><td>Laser Cutter</td><td></td><td></td><td></td></tr><tr><td>Autoclave</td><td></td><td></td><td></td></tr><tr><td>An endoscope calibration software</td><td></td><td></td><td>from GitHub: https://github.com/gift-surg/endocal</td></tr><tr><td>Endoscope</td><td></td><td></td><td></td></tr><tr><td>OpenCV camera calibration module</td><td></td><td></td><td>http://docs.opencv.org/2.4/doc/tutorials/calib3d/camera_calibration/camera_calibration.html</td></tr><tr><td>Safety goggles</td><td></td><td></td><td></td></tr><tr><td>A lab coat</td><td></td><td></td><td></td></tr><tr><td>A ruler and a marker</td><td></td><td></td><td></td></tr><tr><td>Alcohol (preferably ethanol) for dust removal and cleaning</td><td></td><td></td><td></td></tr></tbody></table>

medical-grade sterilizable target for fluid-immersed fetoscope optical distortion calibration

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

We have developed a calibration target for use with fluid-immersed endoscopes within the context of the GIFT-Surg (Guided Instrumentation for Fetal Therapy and Surgery) project. One of the aims of this project is to engineer novel, real-time image processing methods for intra-operative use in the treatment of congenital birth defects, such as spina bifida and the twin-to-twin transfusion syndrome. The developed target allows for the sterility-preserving optical distortion calibration of endoscopes within a few minutes. Good optical distortion calibration and compensation are important for mitigating undesirable effects like radial distortions, which not only hamper accurate imaging using existing endoscopic technology during fetal surgery, but also make acquired images less suitable for potentially very useful image computing applications, like real-time mosaicing. In this paper proposes a novel fabrication method to create an affordable, sterilizable calibration target suitable for use in a clinical setup. This method involves etching a calibration pattern by laser cutting a sandblasted stainless steel sheet. This target was validated using the camera calibration module provided by OpenCV, a state-of-the-art software library popular in the computer vision community.

We created a sterilizable calibration target by etching a pattern of asymmetric circle on a sandblasted stainless steel metal sheet, whose design is shown in Figure 1. An exemplar setup showing this calibration target in action together with a fetoscope is shown in Figure 2. To feed this design into the laser etching software, a custom application was implemented in the Python programming language16. Creating the design pattern involves iteratively etching parallel lines on a metal sheet. For the pattern to have a consistent color in the end, the distance between these lines should be less than the width of the laser beam (see the inset of Figure 1)—this value is 45 µm for the Violino (Laservall) laser cutter.
<img alt="Pollen grain diagram, surface detail inset, scale 1 mm, microscopic analysis, plant biology study." src="/files/ftp_upload/55298/55298fig1.jpg" data-altupdatedat="1788250015000" data-alt="Figure 1"> 
Figure 1: Design of the engraved pattern featuring a 3-by-11 grid of asymmetric circles. Inset: zoomed-in view of the grid of asymmetric circles. The distance between the lines is 45 µm (equal to the laser beam width), and each circle has a diameter of 1 mm. Other sizes could be used for the grid as well, but this was found to be optimal with respect to the fetoscope field of view. <a href="http://cloudflare.jove.com/files/ftp_upload/55298/55298fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Electroplating experiment setup, ion deposition process, coin and metal substrate in solution tray." src="/files/ftp_upload/55298/55298fig2.jpg" data-altupdatedat="1788250015000" data-alt="Figure 2"> 
Figure 2: Exemplar setup with the calibration target in use. The tip of the water-immersed fetoscope is directed at the calibration target on the right. On the left is a British penny to provide scale information. <a href="http://cloudflare.jove.com/files/ftp_upload/55298/55298fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The fabricated calibration target allows for the detection of the circular pattern in the endoscopic video stream with OpenCV17, whose locations are then sorted into the pre-defined asymmetric circular grid (see Figure 3). Using this information in conjunction with the already-known grid geometry, internal camera parameters can be estimated. These include the camera matrix and the distortion coefficients. The camera matrix consists of the focal lengths and the optical centers along the x- and y-axis of the 2D image plane. The distortion coefficients are based on the Brown-Conrady model3. Note that for this work, only the radial distortion parameters were estimated. For a brief discussion of the theory, with practical examples, see the webpage of the OpenCV camera calibration module17 and the MATLAB camera calibration toolbox22. More details about the camera calibration procedure are available in Zhang's work20. The endocal software repository features a sample dataset of 10 endoscopic views of the fabricated calibration target16. Using this dataset, a calibration with an average re-projection error of 0.28 pixels (min: 0.16, max: 0.45) was obtained. This is comparable to the 0.25 pixels reported by Wengert et al. using their custom calibration algorithm15. The same research group, however, reported a re-projection error of 0.6 pixels in a more recent paper when using the method in15 for calibrating an endoscopic camera used for placental mosaicing18.
<img alt="Optical distortion calibration; diagram with colored dot pattern for image acquisition analysis." src="/files/ftp_upload/55298/55298fig3.jpg" data-altupdatedat="1788250015000" data-alt="Figure 3"> 
Figure 3: Real-time detection of the calibration pattern. A screenshot from the calibration application16 featuring the detected calibration pattern overlaid onto the live video stream using the virtual reality visualization from OpenCV17. Note that each detected column of the calibration pattern is emphasized by a different color. The detected circles, in conjunction with the known geometry, are used for computing the camera parameters. <a href="http://cloudflare.jove.com/files/ftp_upload/55298/55298fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The estimated camera parameters are used for optical distortion correction. Figure 4 shows a rectangular chessboard pattern, as viewed using a fetoscope, where optical distortions make the lines appear as curves. Note that the lines appear normal in the distortion-corrected image.
<img alt="Optical distortion calibration diagram; checkered pattern with yellow lines; lens distortion study." src="/files/ftp_upload/55298/55298fig4.jpg" data-altupdatedat="1788250015000" data-alt="Figure 4"> 
Figure 4: Optical distortion correction. A screenshot from the calibration application16 featuring the live video image from a fetoscope recording from the checkerboard pattern (left) with the distortion-corrected image (right). Three exemplary lines are drawn in both images, each from one corner to another, where the trajectory is linear. Due to the optical distortions, these lines appear as curves in the original fetoscope images. <a href="http://cloudflare.jove.com/files/ftp_upload/55298/55298fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

This article describes the design and development of a sterilizable custom camera optical distortion calibration target for the peri-operative, fluid-immersed calibration of endoscopes during endoscopic interventions.

We have developed a calibration target for use with fluid-immersed endoscopes within the context of the GIFT-Surg (Guided Instrumentation for Fetal ...

The aim of the project is to develop a sterilizable endoscopic camera calibration device that cam be used in operating theaters by clinicians. Medical imaging methods such as real time tracking of the human cardiovascular chart, currently suffer from optical distortion effects that could be mitigated by a standardized calibration process. This device can serve as a quick and practical tool for the peer operative calibration of endoscopes used in fetal surgery procedures, such as twin to twin transfusion syndrome.Before sandblasting use a ruler to draw a 40 by 40 millimeter square onto a 1.2 millimeter thick, 316 stainless steel sheet. Then use a manual metal cutter to cut out the square and use a file to round the corners and sides of the sample. Next place the steel onto a one to two centimeter thick block that is slightly larger than the square sample.Use one vice to secure the sample onto the block and another to hold the sample tightly by the edge of the block during the sanding. Then move the entire assembly into an internal blast chamber turn on the compressed airflow, and tightly seal the chamber. Then don a pair of safety goggles and position a blast gun perpendicular to and at least four to five centimeters away from the metal surface.Next apply the foot control to sandblast the sample. Then sandblast the other side as just demonstrated as appropriate. For laser patterning of the sample first design a pattern of asymmetric circles and use the appropriate design software to prepare a drawing exchange format file of the design.Import the file into the laser cutting software and set the background etching and the etching pattern perimeters. Go to the laser tab of the smartest software. When you choose stainless steel as the material predefined perimeters will appear.Adjust the power, shot frequency, scan speed, and number of passes perimeters accordingly. Adjust the X and Y position of the pattern to be etched if required. To recognize the pattern using end accounts of squares the color of the block and background should be optimized.The correct performance and the perimeter setting of the laser should also be checked before beginning the patterning. Next, put the sample on the working platform, focus the surface of the sample using the Z axis command window to change the height of the laser. Check that the start test option is activated on the software, and switch on the laser diode on the laser panel, and then adjust the height of the laser.When the test spot coincides with the diode laser spot the focus of the laser is exactly on the surface. Use the software to align the cutting pattern. Dip the pattern sample into alcohol to clean the surface.Then wrap the sample in a sterilization package for autoclaving. To calibrate the endoscope first install the Endocal endoscope calibration software package provided on GitHub. Then place the calibration target in a steril fluid container, such as a gallipot.Fill the container with the target fluid and adjust the zoom and sharpness of the endoscope as desired. Next immerse the endoscope in the fluid, holding the endoscope at a distance from the calibration from the calibration target, similar to the distance at which the endoscope will later be used. Now launch the calibration application and begin the camera acquisition, moving the tip of the endoscope slightly for different views while keeping the whole calibration pattern in the view of the camera.Acquire the minimum number of endoscopic camera views required for the calibration, as indicated in the Endocal window. This sterilizable calibration target was created by using an asymmetrical circle pattern onto a sandblasted stainless steel sheet as illustrated in these images. For the pattern to have a consistent color in the end the distance between the lines should be less than the width of the laser beam.Here an exemplary set up of this calibration target in action with an endoscope is shown. The fabricated calibration target allows detection of the circular blobs in the endoscopic video stream with open CV.The locations of which are then sorted into predefined asymmetric circular grid geometry and subsequently used for estimating the optical distortion perimeters. Optical distortions can be corrected using the estimated perimeters.For example, in this representative endoscopic view of a rectangular chess board pattern, the optical distortions make the lines appear as curves. After distortion correction however, the lines appear normal. This technique allows the affordable fabrication of a sterilizable device for endoscopic camera calibration from readily available medical grade material.Building on the positive results from this work we are moving toward the development of a fully fledged ergonomic calibration device that will require only minimal user interaction. This production method can help answer key questions in the interpretive medical imaging field such as, how can the sterilability of the endoscopic camera be preserved during the calibration process? Our calibration device works with state of the art camera calibration software like Open CV, further reducing the complexity of integrating the device with many potential clinical software applications.

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Research

JoVE Journal

Bioengineering

Herein, we describe a procedure that employs microscale schlieren technique to measure mixing inhomogeneity in a microfluidic device. Through calibration, distribution of concentration gradient can be derived from the micro-schlieren image.

Analyzing Mixing Inhomogeneity in a Microfluidic Device by Microscale Schlieren Technique

We present a protocol to create cell-based neurotransmitter fluorescent engineered reporters (CNiFERs) for the optical detection of volumetric neurotransmitter release.

Construction of Cell-based Neurotransmitter Fluorescent Engineered Reporters (CNiFERs) for Optical Detection of Neurotransmitters In Vivo

We report a refined procedure of the ferric chloride (FeCl3)-induced thrombosis models on carotid and mesenteric artery as well as vein, characterized efficiently using intravital microscopy to monitor time to occlusive thrombi formation.

Ferric Chloride-induced Murine Thrombosis Models

Practicing the specific skills required for fetoscopic laser coagulation of monochorionic placental anastomoses on realistic models can aid less experienced surgeons in overcoming the steep learning curve associated with this procedure that is now regarded as the standard of care for twin-twin transfusion syndrome.

Model Surgical Training: Skills Acquisition in Fetoscopic Laser Photocoagulation of Monochorionic Diamniotic Twin Placenta Using Realistic Simulators

Iris neovascularization, a common complication of ischemic retinal disease, may lead to sight-threatening neovascular glaucoma. Here, we describe a murine protocol for inducing experimental iris neovascularization that may be used for noninvasive evaluation of angiogenesis-modulating substances.

Puncture-Induced Iris Neovascularization as a Mouse Model of Rubeosis Iridis

Described here is a stereotactic procedure that can target challenging and difficult-to-reach brain regions (due to spatial limitations) using an angled coronal approach. This protocol is adaptable to both mouse and rat models and can be applied to diverse neuroscientific applications, including cannula implantation and microinjections of viral constructs.

Adaptable Angled Stereotactic Approach for Versatile Neuroscience Techniques

We present a protocol to expose the brainstem of adult mouse from the ventral side. By using a gradient-refractive index lens with a miniature microscope, calcium imaging can be used to examine the activity of inferior olive neural somata in vivo.

In vivo Calcium Imaging in Mouse Inferior Olive

Open globe eye injuries may go untreated for multiple days in rural or military-relevant scenarios, resulting in blindness. Therapeutics are needed to minimize loss of vision. Here, we detail an organ culture open globe injury model. With this model, potential therapeutics for stabilizing these injuries can be properly evaluated.

Anterior Segment Organ Culture Platform for Tracking Open Globe Injuries and Therapeutic Performance

We present a protocol for the use of fiberoptic confocal laser microendoscopy (CLM) to non-invasively study the spatio-temporal distribution of liposomes in the eye after subconjunctival injection.

Spatio-Temporal In Vivo Imaging of Ocular Drug Delivery Systems using Fiberoptic Confocal Laser Microendoscopy

This manuscript describes the design and operation of a microtensiometer/confocal microscope to do simultaneous measurements of interfacial tension and surface dilatational rheology while visualizing the interfacial morphology. This provides the real-time construction of structure-property relationships of interfaces important in technology and physiology.

Microtensiometer for Confocal Microscopy Visualization of Dynamic Interfaces

We present an automated method for characterizing the effective elastic modulus of an ocular lens using a compression test.

Automated Compression Testing of the Ocular Lens

Learn to fabricate a sterilizable calibration target for fluid-immersed endoscopes using laser-etched 316 stainless steel. This method enables rapid intra-operative optical distortion correction for fetal surgery.

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

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