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Laparosc Endosc Robot Surg. 2018 Sep;1(2):27-32. doi: 10.1016/j.lers.2018.06.001. Epub 2018 Jun 22.

Real-time computerized video enhancement for minimally invasive fetoscopic surgery.

Author information

1
Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.
2
Department of Obstetrics, Gynecology and Reproductive Sciences, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.
3
Department of Biomedical Engineering, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.
4
Department of Obstetrics and Gynecology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.
5
Yale Fetal Care Center, Yale-New Haven Hospital, 1 Long Wharf Drive, New Haven, CT 06510, USA.
6
University of Western Ontario, 1151 Richmond Street, London, ON N6A 3K7, Canada.

Abstract

Background:

The only definitive treatment for twin-to-twin transfusion syndrome is minimally invasive fetoscopic surgery for the selective coagulation of placental blood vessels. Fetoscopic surgery is a technically challenging operation, mainly due to the poor visibility conditions in the uterine environment. We present the design of an algorithm for the computerized enhancement of fetoscopic video and show that the enhanced video increases the ability of human users to identify blood vessels within fetoscopic video rapidly and accurately.

Methods:

A computer algorithm for the enhancement of fetoscopic video frames was created. First, optical fiber artifacts were removed via a modification of unsharp masking. Second, image contrast was increased via Contrast Limited Adaptive Histogram Equalization (CLAHE). Third, the effect of contrast enhancements on stationary features was removed by normalizing to a windowed mean of the video frames. Fourth, color information was reincorporated by combining the mean-normalized result with the unnormalized contrast enhanced image using the soft light blending algorithm. Medical trainees (n = 16) were recruited into a study to validate the algorithm. Subjects were shown enhanced or unenhanced fetoscopic video frames on a screen and were asked to identify whether a randomly placed marker fell on a blood vessel or on background. The accuracy of their responses was recorded.

Results:

On the subset of images where subjects had the lowest mean accuracy in identifying the placement of the marker, subjects performed better when viewing video frames enhanced by the computer (accuracy 74.27%; SE 0.97) than when viewing unenhanced video frames (accuracy 63.78%; SE 2.79). This result was statistically significant (p < 0.01).

Conclusion:

Real-time computerized enhancement of fetoscopic video has the potential to ease the readability of video in poor lighting conditions, thus providing a benefit to the surgeon intraoperatively.

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