Better Feature Matching for Placental Panorama Construction

  • Praneeth SaddaEmail author
  • John A. Onofrey
  • Mert O. Bahtiyar
  • Xenophon Papademetris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11076)


Twin-to-twin transfusion syndrome is a potentially fatal placental vascular disease of twin pregnancies. The only definitive treatment is surgical cauterization of problematic vascular formations with a fetal endoscope. This surgery is made difficult by the poor visibility conditions of the intrauterine environment and the limited field of view of the endoscope. There have been efforts to address the limited field of view of fetal endoscopes with algorithms that use visual correspondences between successive fetoscopic video frames to stitch those frames together into a composite map of the placental surface. The existing work, however, has been evaluated primarily on ex vivo images of placentas, which tend to have more visual features and fewer visual distractors than the in vivo images that would be encountered in actual surgical procedures. This work shows that guiding feature matching with deep learned segmentations of placental vessels and grid-based motion statistics can make feature-based registration tractable even in in vivo images that have few distinctive visual features.


Feature matching Fetoscopy Grid-based motion statistics Mosaic construction Twin-to-twin transfusion syndrome 



This work was supported by the National Institutes of Health grant number T35DK104689 (NIDDK Medical Student Research Fellowship). The authors would like to thank Andreas Lauritzen for his assistance with data collection.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Praneeth Sadda
    • 1
    Email author
  • John A. Onofrey
    • 1
    • 2
  • Mert O. Bahtiyar
    • 1
    • 3
  • Xenophon Papademetris
    • 1
    • 2
    • 4
  1. 1.School of MedicineYale UniversityNew HavenUSA
  2. 2.Departments of Radiology and Biomedical ImagingYale UniversityNew HavenUSA
  3. 3.Obstetrics, Gynecology, and Reproductive SciencesYale UniversityNew HavenUSA
  4. 4.Biomedical EngineeringYale UniversityNew HavenUSA

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