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Intrapartum Ultrasound -

Intrapartum Ultrasound

MICCAI 2024 Grand Challenge, IUGC 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings

Jieyun Bai, Yaosheng Lu (Herausgeber)

Buch | Softcover
X, 126 Seiten
2025
Springer International Publishing (Verlag)
978-3-031-96317-9 (ISBN)
CHF 164,75 inkl. MwSt
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This LNCS book constitutes the proceedings of the MICCAI 2024 Grand Challenge on Intrapartum Ultrasound, IUGC 2024, which was held in conjunction with MICCAI 2024, in Marrakesh, Morocco, during October 6, 2024. 

The 9 full papers included in this book were carefully reviewed and selected from 15 submissions. The proceedings comprise papers submitted by participants to describe their innovative solutions for automating fetal head station assessment using intrapartum ultrasound imaging, based on the official dataset released for this challenge.

Baseline Method at the Intrapartum Ultrasound Grand Challenge 2024.- Classification and Segmentation of Intrapartum Ultrasound Images with Deep Learning Models.- Accurate Fetal Head Descent Assessment During Labor Using Video Swin Transformer and Wavelet-Based Multitask Learning for 2024 MICCAI Challenge IUGC.- DSSAU-Net:U-Shaped Hybrid Network for Pubic Symphysis and Fetal Head Segmentation.- Intrapartum Ultrasound Grand Challenge, MICCAI 2024.- Multi-Frequency Attention Approach for Enhanced Ultrasound Image Segmentation.- Multi-Task Weakly Supervised Intrapartum Ultrasound Measurements.- Evaluation of the Video-based Network for Angle of Progression Measurement.- Estimation of Labor Progress Parameters on Intrapartum Transperineal Ultrasound Images via Multi-Path Refinement U-Net Combining Recurrent Residuals and Attention.

Erscheinungsdatum
Reihe/Serie Lecture Notes in Computer Science
Zusatzinfo X, 126 p. 39 illus., 35 illus. in color.
Verlagsort Cham
Sprache englisch
Maße 155 x 235 mm
Themenwelt Informatik Grafik / Design Digitale Bildverarbeitung
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Schlagworte Angle of progression • Biometry • Cesarean section • challenge • classification • convolutional neural network • Deep learning • Fetal head • Fetal station • Fetal Ultrasound • Head symphysis distance • Intrapartum ultrasound • obstructed labor • pubic symphysis • Segmentation • Transformer • ultrasound imaging
ISBN-10 3-031-96317-2 / 3031963172
ISBN-13 978-3-031-96317-9 / 9783031963179
Zustand Neuware
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