TTTS-GPS: Patient-specific preoperative planning and simulation platform for twin-to-twin transfusion syndrome fetal surgery
Abstract
Twin-to-twin transfusion syndrome (TTTS) is a serious condition that may occur in pregnancies when two or more fetuses share the same placenta. It is characterized by abnormal vascular connections in the placenta that cause blood to flow unevenly between the babies. If left untreated, perinatal mortality occurs in 90% of cases, whilst neurological injuries are still present in TTTS survivors. Minimally invasive fetoscopic laser surgery is the standard and optimal treatment for this condition, but is technically challenging and can lead to complications. Acquiring and maintaining the required surgical skills need consistent practice, and a steep learning curve. An accurate preoperative planning is thus vital for complex TTTS cases. To this end, we propose the first TTTS fetal surgery planning and simulation platform. The soft tissue of the mother, the uterus, the umbilical cords, the placenta and its vascular tree are segmented and registered automatically from magnetic resonance imaging and 3D ultrasound using computer vision and deep learning techniques. The proposed state-of-the-art technology is integrated into a flexible C++ and MITK-based application to provide a full exploration of the intrauterine environment by simulating the fetoscope camera as well as the laser ablation, determining the correct entry point, training doctors' movements and trajectory ahead of operation, which allows improving upon current practice. A comprehensive usability study is reported. Experienced surgeons rated highly our TTTS planner and simulator, thus being a potential tool to be implemented in real and complex TTTS surgeries.
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Torrents-Barrena et al. proposed a new AI algorithm for the simulation and planning of fetoscopic surgery through the detection and mapping of maternal soft tissue, uterus, placenta, and umbilical cord via MRI in combination with the detection of the placenta and its vascular tree in 3D US.
“They proposed a new AI algorithm for the simulation and planning of fetoscopic surgery through the detection and mapping of the maternal soft tissue, uterus, placenta, and umbilical cord via MRI in combination with the detection of the placenta and its vascular tree in 3D US.”
The model by Torrents-Barrena et al. fully simulates the intraabdominal environment and enables correct entry point planning and surgeon's training.
“This model fully simulates the intraabdominal environment and enables the correct entry point planning and surgeon’s training [127,128].”
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References 26 · Cited by 25
- Authors
- Torrents-Barrena, J., López-Velazco, R., Piella, G., Masoller, N., Valenzuela-Alcaraz, B., Gratacós, E., Eixarch, E., Ceresa, M., & Ángel González Ballester, M.
- Affiliations
- Universitat Pompeu Fabra (ES)
- Universitat de Barcelona (ES)
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 179
- Pages
- 104993
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W2963569911
- Cited by
- 25
- References
- 26
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 1 · 2023: 3 · 2022: 7 · 2021: 1 · 2020: 5 · 2019: 2
- FWCI
- 3.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Methods and Programs in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ministry of Economy and Competitiveness (MDM-2015-0502)
- Topics
- Health Sciences › Medicine › Surgery › Surgical Simulation and Training
- Physical Sciences › Engineering › Biomedical Engineering › Anatomy and Medical Technology
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- twin-twin transfusion syndrome, fetoscopic laser surgery, surgical planning, surgical simulation, fetal surgery, 3D ultrasound, magnetic resonance imaging, deep learning segmentation, image registration, surgical training, MITK, usability testing
- MeSH terms
- Algorithms, Computer Graphics, Computer Simulation, Female, Fetofetal Transfusion, Fetoscopy, Humans, Infant, Newborn, Magnetic Resonance Imaging, Models, Anatomic, Placenta, Pregnancy, User-Computer Interface, Uterus, Imaging, Three-Dimensional, Laser Therapy, Patient-Specific Modeling
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