ArticleInternational journal of computer assisted radiology and surgery2019
Deep-learned placental vessel segmentation for intraoperative video enhancement in fetoscopic surgery.
Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- Artificial intelligence and perinatology: a study on accelerated academic production- a bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Comparing artificial intelligence and healthcare professional performance in surgical and interventional video analysis: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Deep learning-based vessel and nerve recognition model for lateral lymph node dissection: a retrospective feasibility study.Langenbeck's archives of surgery · 2025Article
- The use of artificial intelligence in sexual and reproductive health: a comprehensive scoping review.npj women's health · 2025Article
- Intraoperative detection of parathyroid glands using artificial intelligence: optimizing medical image training with data augmentation methods.Surgical endoscopy · 2024Article
- Patient-specific placental vessel segmentation with limited data.Journal of robotic surgery · 2024Article
- Placental vessel segmentation and registration in fetoscopy: Literature review and MICCAI FetReg2021 challenge findings.Medical image analysis · 2024Review
- Placental Vessel Segmentation Using Pix2pix Compared to U-Net.Journal of imaging · 2023Article
- Computer-assisted fetal laser surgery in the treatment of twin-to-twin transfusion syndrome: Recent trends and prospects.Prenatal diagnosis · 2022Review
- A massively multi-scale approach to characterizing tissue architecture by synchrotron micro-CT applied to the human placenta.Journal of the Royal Society, Interface · 2021Article
- Deep-Learning-Based Cerebral Artery Semantic Segmentation in Neurosurgical Operating Microscope Vision Using Indocyanine Green Fluorescence Videoangiography.Frontiers in neurorobotics · 2021Article
- FetNet: a recurrent convolutional network for occlusion identification in fetoscopic videos.International journal of computer assisted radiology and surgery · 2020Article
- Artificial Intelligence in Imaging: The Radiologist's Role.Journal of the American College of Radiology : JACR · 2019Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
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Abstract
introductionTwin-to-twin transfusion syndrome (TTTS) is a potentially lethal condition that affects pregnancies in which twins share a single placenta. The definitive treatment for TTTS is fetoscopic laser photocoagulation, a procedure in which placental blood vessels are selectively cauterized. Challenges in this procedure include difficulty in quickly identifying placental blood vessels due to the many artifacts in the endoscopic video that the surgeon uses for navigation. We propose using deep-learned segmentations of blood vessels to create masks that can be recombined with the original fetoscopic video frame in such a way that the location of placental blood vessels is discernable at a glance.
methodsIn a process approved by an institutional review board, intraoperative videos were acquired from ten fetoscopic laser photocoagulation surgeries performed at Yale New Haven Hospital. A total of 345 video frames were selected from these videos at regularly spaced time intervals. The video frames were segmented once by an expert human rater (a clinician) and once by a novice, but trained human rater (an undergraduate student). The segmentations were used to train a fully convolutional neural network of 25 layers.
resultsThe neural network was able to produce segmentations with a high similarity to ground truth segmentations produced by an expert human rater (sensitivity = 92.15% ± 10.69%) and produced segmentations that were significantly more accurate than those produced by a novice human rater (sensitivity = 56.87% ± 21.64%; p < 0.01).
conclusionA convolutional neural network can be trained to segment placental blood vessels with near-human accuracy and can exceed the accuracy of novice human raters. Recombining these segmentations with the original fetoscopic video frames can produced enhanced frames in which blood vessels are easily detectable. This has significant implications for aiding fetoscopic surgeons-especially trainees who are not yet at an expert level.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.