# Beyond Benchmarks of IUGC: Rethinking Requirements of Deep Learning Methods for Intrapartum Ultrasound Biometry from Fetal Ultrasound Videos arXiv: https://arxiv.org/abs/2602.12922 ยท HTML: https://mlsanity.com/paper/2602.12922/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Builds on 29 of its 127 references that are in the map. - First posted on arXiv 13 Feb 2026. ## Builds on (most cited first, 29 of 29) - [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023) - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019) - [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018) - [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016) - [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021) - [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021) - [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023) - [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019) - [Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks](https://mlsanity.com/paper/1807.07356/) (2018) - [nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation](https://mlsanity.com/paper/2404.09556/) (2024) - [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017) - [Wider or Deeper: Revisiting the ResNet Model for Visual Recognition](https://mlsanity.com/paper/1611.10080/) (2016) - [SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound](https://mlsanity.com/paper/1612.05601/) (2016) - [Methods and open-source toolkit for analyzing and visualizing challenge results](https://mlsanity.com/paper/1910.05121/) (2019) - [BIAS: Transparent reporting of biomedical image analysis challenges](https://mlsanity.com/paper/1910.04071/) (2019) - [Online Hyper-parameter Learning for Auto-Augmentation Strategy](https://mlsanity.com/paper/1905.07373/) (2019) - [A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis](https://mlsanity.com/paper/2201.12260/) (2022) - [ACC-UNet: A Completely Convolutional UNet model for the 2020s](https://mlsanity.com/paper/2308.13680/) (2023) - [Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression](https://mlsanity.com/paper/2110.13675/) (2021) - [ConvFormer: Plug-and-Play CNN-Style Transformers for Improving Medical Image Segmentation](https://mlsanity.com/paper/2309.05674/) (2023) - [MMSummary: Multimodal Summary Generation for Fetal Ultrasound Video](https://mlsanity.com/paper/2408.03761/) (2024) - [Intrapartum Ultrasound Image Segmentation of Pubic Symphysis and Fetal Head Using Dual Student-Teacher Framework with CNN-ViT Collaborative Learning](https://mlsanity.com/paper/2409.06928/) (2024) - [PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images](https://mlsanity.com/paper/2409.10980/) (2024) - [Baseline Method of the Foundation Model Challenge for Ultrasound Image Analysis](https://mlsanity.com/paper/2602.01055/) (2026) - [Deep Learning Fetal Ultrasound Video Model Match Human Observers in Biometric Measurements](https://mlsanity.com/paper/2205.13835/) (2022) - [Automated Fetal Biometry Assessment with Deep Ensembles using Sparse-Sampling of 2D Intrapartum Ultrasound Images](https://mlsanity.com/paper/2505.14572/) (2025) - [DSSAU-Net:U-Shaped Hybrid Network for Pubic Symphysis and Fetal Head Segmentation](https://mlsanity.com/paper/2506.03684/) (2025) - [FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation](https://mlsanity.com/paper/2601.15572/) (2026) - [DSTCS: Dual-Student Teacher Framework with Segment Anything Model for Semi-Supervised Pubic Symphysis Fetal Head Segmentation](https://mlsanity.com/paper/2601.19446/) (2026) ## Built on by (most cited first, 1 of 1) - [FADA: Accessible fetal ultrasound interpretation and annotation with a selectively distilled unified vision-language model](https://mlsanity.com/paper/2606.11106/) (2026) ## Abstract (arXiv) A substantial proportion (45%) of maternal deaths, neonatal deaths, and stillbirths occur during the intrapartum phase, with a particularly high burden in low- and middle-income countries. Intrapartum biometry plays a critical role in monitoring labor progression; however, the routine use of ultrasound in resource-limited settings is hindered by a shortage of trained sonographers. To address this challenge, the Intrapartum Ultrasound Grand Challenge (IUGC), co-hosted with MICCAI 2024, was launched. The IUGC introduces a clinically oriented multi-task automatic measurement framework that integrates standard plane classification, fetal head-pubic symphysis segmentation, and biometry, enabling algorithms to exploit complementary task information for more accurate estimation. Furthermore, the challenge releases the largest multi-center intrapartum ultrasound video dataset to date, comprising 774 videos (68,106 frames) collected from three hospitals, providing a robust foundation for model training and evaluation. In this study, we present a comprehensive overview of the challenge design, review the submissions from eight participating teams, and analyze their methods from five perspectives: preprocessing, data augmentation, learning strategy, model architecture, and post-processing. In addition, we perform a systematic analysis of the benchmark results to identify key bottlenecks, explore potential solutions, and highlight open challenges for future research. Although encouraging performance has been achieved, our findings indicate that the field remains at an early stage, and further in-depth investigation is required before large-scale clinical deployment. All benchmark solutions and the complete dataset have been publicly released to facilitate reproducible research and promote continued advances in automatic intrapartum ultrasound biometry.