Multi-Parametric Fusion of 3D Power Doppler Ultrasound for Fetal Kidney Segmentation Using Fully Convolutional Neural Networks
Open accessAbstract
Kidney development is key to the long-term health of the fetus. Renal volume and vascularity assessed by 3D ultrasound (3D-US) are known markers of wellbeing, however, a lack of real-time image segmentation solutions preclude these measures being used in a busy clinical environment. In this work, we aimed to automate kidney segmentation using fully convolutional neural networks (fCNNs). We used multi-parametric input fusion incorporating 3D B-Mode and power Doppler (PD) volumes, aiming to improve segmentation accuracy. Three different fusion strategies and their performance were assessed versus a single input (B-Mode) network. Early input-level fusion provided the best segmentation accuracy with an average Dice similarity coefficient (DSC) of 0.81 and Hausdorff distance (HD) of 8.96 mm, an improvement of 0.06 DSC and reduction of 1.43 mm HD compared to our baseline network. Compared to manual segmentation for all models, repeatability was assessed by intra-class correlation coefficients (ICC) indicating good to excellent reproducibility (ICC 0.93). The framework was extended to support multiple graphics processing units (GPUs) to better handle volumetric data, dense fCNN models, batch normalization and complex fusion networks. This work and available source code provides a framework to increase the parameter space of encoder-decoder style fCNNs across multiple GPUs and shows that application of multi-parametric 3D-US in fCNN training improves segmentation accuracy.
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Two studies focused on amniotic fluid, with one evaluating automatic measurement of amniotic fluid index and the other assessing segmentation of amniotic fluid and fetal tissue.
“Two studies focused on amniotic fluid with one evaluating automatic measurement of amniotic fluid index 120 and the other assessing segmentation of amniotic fluid and fetal tissue.121”
From Artificial intelligence in obstetric ultrasound: A scoping review. (2023) · cites this paper as [121]
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- Authors
- Weerasinghe, N. H., Lovell, N. H., Welsh, A. W., & Stevenson, G. N.
- Affiliations
- UNSW Sydney (AU)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2021-06
- Volume
- 25
- Issue
- 6
- Pages
- 2050-2057
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W3090745929
- Cited by
- 27
- References
- 29
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 6 · 2023: 8 · 2022: 4 · 2021: 3
- FWCI
- 1.46
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
- Funders
- Leslie Stevens Fund for Newborn Care, Sydney, Australia
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- 3D power Doppler ultrasound, fully convolutional network, renal volume, B-mode ultrasound, encoder-decoder architecture, intra-class correlation
- MeSH terms
- Humans, Image Processing, Computer-Assisted, Kidney, Reproducibility of Results, Neural Networks, Computer, Ultrasonography, Doppler
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