TTTS-GPS: Patient-specific preoperative planning and simulation platform for twin-to-twin transfusion syndrome fetal surgery. (October 2019) - Torrents-Barrena, Jordina; López-Velazco, Rocío; Piella, Gemma; Masoller, Narcís; Valenzuela-Alcaraz, Brenda; Gratacós, Eduard; Eixarch, Elisenda; Ceresa, Mario; Ángel González Ballester, Miguel
Highlights: We present the very first Twin-to-Twin Transfusion Syndrome (TTTS) planner and simulator oriented to clinical use. Novel computer vision and deep learning algorithms are fully integrated to create a 3D model of the womb from MRI and 3D US. Our platform allows selecting the fetoscope entry point taking into account the umbilical cords, tracks the fetoscope trajectory, simulates the laser ablation, and mimics the fetoscope camera visualization to explore the whole model. The proposed software can aid doctors by generating a map of the maternal intrauterine environment ahead of surgery, thereby improving the current clinical workflow and the surgery outcome. The algorithms developed for each application module are quantitatively and qualitatively validated. An extensive usability study is also performed on users with different levels of expertise and backgrounds. 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.