Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
352-02-012 / 352-02-012 ISMRM Abstract

Accurate Segmentation of Placenta Accreta Spectrum: An Uncertainty-Guided Fusion Network for Multi-sequence MRI

Primary: Body - Placenta
Secondary: Analysis Methods - Segmentation and Detection
352-02-012 · Segmentation: Automated, Scalable, and Clinically Oriented MRI Analysis · Monday, 11 May, 1:50 PM–3:26 PM · Power Pitch Theatre 2
352-02-012 · Segmentation: Automated, Scalable, and Clinically Oriented MRI Analysis · Monday, 11 May, 1:50 PM–3:26 PM · Power Pitch Theatre 2
Keywords: Multi-Contrast Placenta Segmentation Image processing Deep learning
Accepted
Le Fu 1, Haima Yang2, Peicheng li2, Jie Shi3, Jianli Yu1, Jiejun Cheng1
1Radiology, Shanghai first maternity and infant hospital, Shanghai, China
2School of Optical Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
3MR Research, Beijing, China
Presenting Author: Le Fu

Synopsis

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References

1. Burton GJ, Fowden AL. The placenta: a multifaceted, transient organ. Philosophical Transactions of the Royal Society B: Biological Sciences. 2015;370(1663):20140066. doi:10.1098/rstb.2014.0066 [doi]
2. D’Antonio F, Iacovella C, Palacios-Jaraquemada J, et al. Prenatal identification of invasive placentation using magnetic resonance imaging: systematic review and meta-analysis. Ultrasound in Obstetrics & Gynecology. 2014;44(1):8-16. doi:10.1002/uog.13327 [doi]
3. Einerson BD, Gilner JB, Zuckerwise LC. Placenta accreta spectrum. Obstetrics & Gynecology. 2023;142(1):31-50. doi:10.1097/AOG.0000000000005229 [doi]
4. Jauniaux E, Jurkovic D, Hussein AM, Burton GJ. New insights into the etiopathology of placenta accreta spectrum. American Journal of Obstetrics & Gynecology. 2022;227(3):384-391. doi:10.1016/j.ajog.2022.02.038 [doi]
5. Jauniaux E, Collins S, Burton GJ. Placenta accreta spectrum: pathophysiology and evidence-based anatomy for prenatal ultrasound imaging. American Journal of Obstetrics & Gynecology. 2018;218(1):75-87. doi:10.1016/j.ajog.2017.05.067 [doi]
6. Shahedi M, Spong CY, Dormer JD, et al. Deep learning-based segmentation of the placenta and uterus on MR images. Journal of Medical Imaging. 2021;8(5):054001. doi:10.1117/1.JMI.8.5.054001 [doi]
7. Liu Y, Zabihollahy F, Yan R, et al. Evaluation of spatial attentive deep learning for automatic placental segmentation on longitudinal MRI. Journal of Magnetic Resonance Imaging. 2023;57(5):1533-1540. doi:10.1002/jmri.28403 [doi]
8. Woo S, Debnath S, Hu R, et al. ConvNeXt V2: Co-designing and scaling ConvNets with masked autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2023:16133-16142. doi:10.1109/CVPR52729.2023.01546 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/352-02-012