Intrapartum Ultrasound

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

AvJieyun Bai,Yaosheng Lu

Häftad, Engelska, 2025

Del 15689 i serien Lecture Notes in Computer Science

1 230 kr

Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt över 249 kr.

Beskrivning

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.

Produktinformation

Utgivningsdatum:2025-08-12
Mått:155 x 235 x 8 mm
Vikt:219 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:126
Förlag:Springer International Publishing AG
ISBN:9783031963179

Utforska kategorier

Artificiell intelligens inom Data och IT
Systemvetenskap och AI inom Data och IT

Innehållsförteckning

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.