Automated Placenta Segmentation with a Convolutional Neural Network Weighted by Acoustic Shadow Detection
Abstract
Placental assessment through routine obstetrical ultrasound is often limited to documenting its location and ruling out placenta previa. However, many obstetrical complications originate from abnormal focal or global placental development. Technical difficulties in assessing the placenta as well as a lack of established objective criteria to classify echotexture are barriers to diagnosis of pathology by ultrasound imaging. As a first step towards the development of a computer aided placental assessment tool, we developed a fully automated method for placental segmentation using a convolutional neural network. The network contains a novel layer weighted by automated acoustic shadow detection to recognize artifacts specific to ultrasound. In order to develop a detection algorithm usable in different imaging scenarios, we acquired a dataset containing 1364 fetal ultrasound images from 247 patients acquired over 47 months was taken with different machines, operators, and at a range of gestational ages. Mean Dice coefficients for automated segmentation on the full dataset with and without the acoustic shadow detection layer were 0.92±0.04 and 0.91±0.03 when comparing to manual segmentation. Mean Dice coefficients on the subset of images containing acoustic shadows with and without acoustic shadow detection were 0.87±0.04 and 0.75±0.05. The method requires no user input to tune the detection. The automated placenta segmentation method can serve as a preprocessing step for further image analysis in artificial intelligence methods requiring large scale data processing of placental images.
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Hu et al. achieved an accuracy of 81% on classification of placentas as normal or abnormal defined by the presence of preeclampsia or fetal growth restriction at birth.
“in a much larger study by Hu et al.12 authors achieved an accuracy of 81% on the classification of placentas as either normal or abnormal defined by the presence of preeclampsia or fetal growth restriction at birth.”
From Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization (2023) · cites this paper as [12]
Hu et al. used manual segmentation as ground truth and showed that including a layer containing shadow detection improves performance in images with acoustic shadows.
“Among these, Hu et al. used manual segmentation as ground truth and showed that including a layer containing shadow detection improves performance in images with acoustic shadows12.”
From Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization (2023) · cites this paper as [12]
Studies examining 2D ultrasound placenta segmentation are scarce.
“Studies examining 2D ultrasound placenta segmentation are scarce.”
From Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization (2023) · cites this paper as [12]
Sonographic placental assessment is mainly restricted to the identification of placental location, adhesion, or insertion site of umbilical cord.
“To date, sonographic placental assessment is mainly restricted to the identification of placental location, adhesion, or insertion site of umbilical cord [108]”
Hu et al. performed an echotexture analysis in 2D placental images.
“Hu et al. performed an echotexture analysis in 2D placental images [108]”
Hu et al. proposed a FCNN with a novel convolutional layer weighted by automated acoustic shadow detection, and the acoustic shadow detection scheme improved segmentation accuracy.
“In both cases, the acoustic shadow detection scheme was proven to be able to improve segmentation accuracy.”
From Deep Learning Strategies for Ultrasound in Pregnancy (2020) · cites this paper as [32]
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- Authors
- Hu, R., Singla, R., Yan, R., Mayer, C., & Rohling, R. N.
- Affiliations
- University of British Columbia (CA)
- Venue
- 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2019-07
- Volume
- 2019
- Pages
- 6718-6723
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W2980035653
- Cited by
- 27
- References
- 22
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 4 · 2023: 5 · 2022: 8 · 2021: 2 · 2020: 2
- FWCI
- 0.98
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- placenta segmentation, prenatal ultrasound, convolutional neural networks, ultrasound artifacts, placenta previa, computer-aided diagnosis, fetal ultrasound images
- MeSH terms
- Acoustics, Algorithms, Artificial Intelligence, Female, Humans, Image Processing, Computer-Assisted, Placenta, Pregnancy, Neural Networks, Computer
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