Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI
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Automatic and 3D segmentation of the placenta is challenging due to large slice thickness, fetal movement, and variable placenta location/orientation.
“These factors make automatic and 3D segmentation of the placenta a challenging task [62].”
From DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2019) · cites this paper as [62]
Use of multiple pathways to increase context of an FCNN has improved accuracy and has been applied to placental segmentation in MRI and 3D-US.
“Use of multiple pathways, to increase the context of an FCNN, has improved accuracy [14] and has been successfully applied to placental segmentation in both MRI [15] and 3D-US [16].”
From Fully Automated 3-D Ultrasound Segmentation of the Placenta, Amniotic Fluid, and Fetus for Early Pregnancy Assessment (2021) · cites this paper as [15]
Cites
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
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Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498 · cited 3× · see citing works
“We use a 3D fully connected CRF model [4,7] which applies a linear combination of Gaussian kernels to define the pairwise edge potentials.”
MethodSelf-citationEfficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
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Fetal growth is directly related to maternal anthropometry and placental volume
Thame, M. M., Osmond, C., Bennett, F. I., Wilks, R., & Forrester, T. (2004) · European Journal of Clinical Nutrition · doi:10.1038/sj.ejcn.1601909 · Cited by 179 · see citing works
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Automated Localization of Fetal Organs in MRI Using Random Forests with Steerable Features
Keraudren, K., Kainz, B., Oktay, O., Kyriakopoulou, V., Rutherford, M., Hajnal, J. V., & Rueckert, D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_74 · Cited by 18 · see citing works
Self-citationAutomatic Brain Localization in Fetal MRI Using Superpixel Graphs
Alansary, A., Lee, M., Keraudren, K., Kainz, B., Malamateniou, C., Rutherford, M., Hajnal, J. V., Glocker, B., & Rueckert, D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27929-9_2 · Cited by 17 · see citing works
Self-citationFlexible Reconstruction and Correction of Unpredictable Motion from Stacks of 2D Images
Kainz, B., Alansary, A., Malamateniou, C., Keraudren, K., Rutherford, M., Hajnal, J. V., & Rueckert, D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24571-3_66 · Cited by 15 · see citing works
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A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Wang, G., Zuluaga, M. A., Li, W., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., et al. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2840695 · Cited by 502 · see citing works
A deep learning framework for supporting the classification of breast lesions in ultrasound images
Han, S., Kang, H.-K., Jeong, J.-Y., Park, M.-H., Kim, W., Bang, W.-C., & Seong, Y.-K. (2017) · Physics in Medicine & Biology · doi:10.1088/1361-6560/aa82ec · Cited by 358 · see citing works
DeepMedic for Brain Tumor Segmentation
Kamnitsas, K., Ferrante, E., Parisot, S., Ledig, C., Nori, A. V., Criminisi, A., Rueckert, D., & Glocker, B. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-55524-9_14 · Cited by 346 · see citing works
Self-citationTowards automated semantic segmentation in prenatal volumetric ultrasound
Yang, X., Yu, L., Li, S., Wen, H., Luo, D., Bian, C., Qin, J., Ni, D., & Heng, P.-A. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2858779 · Cited by 115 · see citing works
Looney, P., Stevenson, G. N., Nicolaides, K. H., Plasencia, W., Molloholli, M., Natsis, S., & Collins, S. L. (2018) · JCI Insight · doi:10.1172/jci.insight.120178 · Cited by 111 · see citing works
Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects
Torrents-Barrena, J., Piella, G., Masoller, N., Gratacós, E., Eixarch, E., Ceresa, M., & Ballester, M. Á. G. (2019) · Medical Image Analysis · doi:10.1016/j.media.2018.10.003 · Cited by 97 · see citing works
Automatic 3D ultrasound segmentation of the first trimester placenta using deep learning
Looney, P., Stevenson, G. N., Nicolaides, K. H., Plasencia, W., Molloholli, M., Natsis, S., & Collins, S. L. (2017) · 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) · doi:10.1109/isbi.2017.7950519 · Cited by 47 · see citing works
Torrents-Barrena, J., Monill, N., Piella, G., Gratacós, E., Eixarch, E., Ceresa, M., & González Ballester, M. A. (2021) · Academic Radiology · doi:10.1016/j.acra.2019.11.006 · Cited by 42 · see citing works
Looney, P., Yin, Y., Collins, S. L., Nicolaides, K. H., Plasencia, W., Molloholli, M., Natsis, S., & Stevenson, G. N. (2021) · IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control · doi:10.1109/tuffc.2021.3052143 · Cited by 40 · see citing works
- Authors
- Alansary, A., Kamnitsas, K., Davidson, A., Khlebnikov, R., Rajchl, M., Malamateniou, C., Rutherford, M., Hajnal, J. V., Glocker, B., Rueckert, D., & Kainz, B.
- Affiliations
- Imperial College London (GB)
- King's College London (GB)
- Corresponding author
- Alansary, Amir
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 589-597
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2476575773
- Cited by
- 77
- References
- 10
- Citations by year
- 2026: 1 · 2024: 5 · 2023: 12 · 2022: 7 · 2021: 15 · 2020: 9 · 2019: 11 · 2018: 6 · 2017: 9 · 2016: 2
- FWCI
- 3.24
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Research Portal (King's College London) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/N024494/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- placenta segmentation, placenta, motion artifacts, fetal movement, T2-weighted MRI, dense conditional random fields, 3D reconstruction, fetal growth restriction, fetal imaging, placental volume
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