29 Szymon Płotka Computer Vision News Computer Vision News distinguishing the type of delivery: vaginal or Cesarean section. Building on these findings, he introduced a novel method for predicting fetal weight during pregnancy based exclusively on the fetal abdominal view. This method can reduce bias in the estimation of fetal weight by measuring only one fetal body part to achieve the goal. Furthermore, he presented a fast and effective neural network tailored for segmenting and highlighting placental vessels during fetoscopic laser photocoagulation in cases of Twin-to-Twin Transfusion Syndrome (TTTS), aiming to assist surgeons during fetal surgery in clinical environments. The proposed method may aid surgeons during real-time The figure presents sample US frames extracted from fetal US videos. The frames depict standard planes of the fetal head, abdomen, and femur, displayed from left to right. Computer Vision News Publisher: RSIP Vision Copyright: RSIP Vision Editor: Ralph Anzarouth All rights reserved Unauthorized reproduction is strictly forbidden. Our editorial choices are fully independent from IEEE, CVPR and all conference organizers. real-time fetoscopic fetal surgery to accurately identify critical structures and ultimately improve outcomes of TTTS treatments. To conclude, Szymon's contributions have the potential to significantly advance prenatal care by providing more accurate and efficient tools for monitoring and assessing fetal health. His work exemplifies the intersection of deep learning and medical research, offering promising solutions to critical challenges in maternal and fetal medicine.
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