Segmentation-Based vs. Regression-Based Biomarker Estimation: A Case Study of Fetus Head Circumference Assessment from Ultrasound Images
Open accessAbstract
The fetus head circumference (HC) is a key biometric to monitor fetus growth during pregnancy, which is estimated from ultrasound (US) images. The standard approach to automatically measure the HC is to use a segmentation network to segment the skull, and then estimate the head contour length from the segmentation map via ellipse fitting, usually after post-processing. In this application, segmentation is just an intermediate step to the estimation of a parameter of interest. Another possibility is to estimate directly the HC with a regression network. Even if this type of segmentation-free approaches have been boosted with deep learning, it is not yet clear how well direct approach can compare to segmentation approaches, which are expected to be still more accurate. This observation motivates the present study, where we propose a fair, quantitative comparison of segmentation-based and segmentation-free (i.e., regression) approaches to estimate how far regression-based approaches stand from segmentation approaches. We experiment various convolutional neural networks (CNN) architectures and backbones for both segmentation and regression models and provide estimation results on the HC18 dataset, as well agreement analysis, to support our findings. We also investigate memory usage and computational efficiency to compare both types of approaches. The experimental results demonstrate that even if segmentation-based approaches deliver the most accurate results, regression CNN approaches are actually learning to find prominent features, leading to promising yet improvable HC estimation results.
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Five studies used the freely available 'HC18' data set for training and testing algorithms, which contained 1334 2D images from 551 women of the standard fetal head plane.
“Five studies used the freely available ‘HC18’ data set [45] for training and testing the algorithms, which contained 1334 2D images from 551 women of the standard fetal head plane [18,34,37,38,40].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [38]
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566 · see citing works
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939 · see citing works
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380 · see citing works
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Tan, M., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1905.11946 · Cited by 17,072 · see citing works
Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.660 · Cited by 16,438 · see citing works
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00889-5_1 · Cited by 10,642 · see citing works
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524 · see citing works
DeepPose: Human Pose Estimation via Deep Neural Networks
Toshev, A., & Szegedy, C. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.214 · Cited by 3,288 · see citing works
LinkNet: Exploiting encoder representations for efficient semantic segmentation
Chaurasia, A., & Culurciello, E. (2017) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip.2017.8305148 · Cited by 1,972 · see citing works
Deep Convolutional Network Cascade for Facial Point Detection
Sun, Y. J., Wang, X., & Tang, X. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.446 · Cited by 1,405 · see citing works
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
Jha, D., Riegler, M., Johansen, D., Halvorsen, P., & Johansen, H. D. (2020) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms49503.2020.00111 · Cited by 973 · see citing works
A review of segmentation methods in short axis cardiac MR images
Petitjean, C., & Dacher, J. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.12.004 · Cited by 732 · see citing works
Self-citationLoss odyssey in medical image segmentation
Ma, J., Chen, J., Ng, M., Huang, R., Li, Y., Li, C., Yang, X., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102035 · Cited by 662 · see citing works
A Comprehensive Analysis of Deep Regression
Lathuilière, S., Mesejo, P., Alameda-Pineda, X., & Horaud, R. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2910523 · Cited by 331 · see citing works
Automated measurement of fetal head circumference using 2D ultrasound images
van den Heuvel, T. L. A., de Bruijn, D., de Korte, C. L., & Ginneken, B. V. (2018) · PLOS ONE · doi:10.1371/journal.pone.0200412 · Cited by 280 · see citing works
Intra‐ and interobserver variability in fetal ultrasound measurements
Sarris, I., Ioannou, C., Chamberlain, P., Ohuma, E., Roseman, F., Hoch, L., Altman, D. G., Papageorghiou, A. T., & for the International Fetal and Newborn Growth Consortium for the 21 Century (INTERGROWTH‐21) (2012) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.10082 · Cited by 195 · see citing works
Sobhaninia, Z., Rafiei, S., Emami, A., Karimi, N., Najarian, K., Samavi, S., & Soroushmehr, S. M. R. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856981 · Cited by 141 · see citing works
Li, J., Wang, Y., Lei, B., Cheng, J.-Z., Qin, J., Wang, T., Li, S., & Ni, D. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2703890 · Cited by 120 · see citing works
Lü, W., Tan, J., & Floyd, R. C. (2005) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2005.04.002 · Cited by 106 · see citing works
3D head pose estimation with convolutional neural network trained on synthetic images
Liu, X.-B., Liang, W., Wang, Y., Li, S., & Pei, M.-T. (2016) · International Conference on Information Photonics · doi:10.1109/icip.2016.7532566 · Cited by 106 · see citing works
Segmentation of fetal ultrasound images
Jardim, S., & Figueiredo, M. A. T. (2005) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2004.11.003 · Cited by 84 · see citing works
Automatic evaluation of fetal head biometry from ultrasound images using machine learning
Kim, H. P., Lee, S. M., Kwon, J.-Y., Park, Y., Kim, K. C., & Seo, J. K. (2019) · Physiological Measurement · doi:10.1088/1361-6579/ab21ac · Cited by 61 · see citing works
Inequalities for the Perimeter of an Ellipse
Barnard, R., Pearce, K., & Schovanec, L. E. (2001) · Journal of Mathematical Analysis and Applications · doi:10.1006/jmaa.2000.7128 · Cited by 60 · see citing works
Visualization of neural networks using saliency maps
Mørch, N. J. S., Kjems, U., Hansen, L. K., Svarer, C., Law, I. G., Lautrup, B. E., Strother, S. C., & Rehm, K. (2002) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/icnn.1995.488997 · Cited by 58 · see citing works
A regression framework to head-circumference delineation from US fetal images
Fiorentino, M. C., Moccia, S., Capparuccini, M., Giamberini, S., & Frontoni, E. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105771 · Cited by 54 · see citing works
Direct Quantification of Coronary Artery Stenosis Through Hierarchical Attentive Multi-View Learning
Zhang, D., Yang, G. Z., Zhao, S. G., et al. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3017275 · Cited by 52 · see citing works
Understanding Deep Neural Networks for Regression in Leaf Counting
Dobrescu, A., Giuffrida, M. V., & Tsaftaris, S. A. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00316 · Cited by 43 · see citing works
Confident Head Circumference Measurement from Ultrasound with Real-Time Feedback for Sonographers
Budd, S., Sinclair, M. D., Khanal, B., Matthew, J., Lloyd, D. F. A., Gómez, A., Toussaint, N., Robinson, E. C., & Kainz, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32251-9_75 · Cited by 36 · see citing works
Transfer Learning for Brain Tumor Segmentation
Wacker, J., Ladeira, M., & Nascimento, J. E. V. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72084-1_22 · Cited by 32 · see citing works
Blanc‐Durand, P., Campedel, L., Mulé, S., Jégou, S., Luciani, A., Pigneur, F., & Itti, E. (2020) · European Radiology · doi:10.1007/s00330-019-06630-w · Cited by 32 · see citing works
Luo, G., Dong, S., Wang, W., Wang, K., Cao, S., Tam, C., Zhang, H., Howey, J., Ohorodnyk, P., & Li, S. (2020) · Medical Image Analysis · doi:10.1016/j.media.2019.101591 · Cited by 30 · cited 1× · see citing works
“[8] propose multi-task learning for the measurement of cardiac volumes from MRI.”
BackgroundMoccia, S., Fiorentino, M. C., & Frontoni, E. (2021) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-021-02430-0 · Cited by 30 · see citing works
Direct estimation of fetal head circumference from ultrasound images based on regression CNN
Zhang, J., Petitjean, C., Lopez, P., et al. (2020) · Medical Imaging with Deep Learning · Cited by 17 · see citing works
Self-citationSegmentation-Free Estimation of Kidney Volumes in CT with Dual Regression Forests
Hussain, M. A., Hamarneh, G., O’Connell, T., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47157-0_19 · Cited by 15 · see citing works
Explainability for Regression CNN in Fetal Head Circumference Estimation from Ultrasound Images
Zhang, J., Petitjean, C., Yger, F., & Aïnouz, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61166-8_8 · Cited by 15 · see citing works
Self-citationDirect Automated Quantitative Measurement of Spine via Cascade Amplifier Regression Network
Pang, S., Leung, S., Nachum, I. B., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00934-2_104 · Cited by 13 · see citing works
Direct volume estimation without segmentation
Zhen, X., Wang, Z., Islam, A., et al. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2081377 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [1]Van den Heuvel T.L.A. de Bruijn D. de Korte C.L. Ginneken B.V. Automated measurement of fetal head circumference using 2D ultrasound images [Data set] Zenodo 2018 10.5281/zenodo.1322001 PMC6107118 30138319 · DOI
- [30]Simonyan K. Zisserman A. Very deep convolutional networks for large-scale image recognition arXiv 2015 1409.1556
- [37]Howard A.G. Zhu M. Chen B. Kalenichenko D. Wang W. Weyand T. Andreetto M. Adam H. Mobilenets: Efficient convolutional neural networks for mobile vision applications arXiv 2017 1704.04861
Cited by
Sorted by citations
Nazir, S., Dickson, D. M., & Akram, M. U. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106668 · Cited by 283 · see citing works
“The estimation was performed using ultrasound images and Regression CNN [249].”
MethodInfluentialArtificial intelligence in obstetric ultrasound: A scoping review.
Horgan, R., Nehme, L., & Abuhamad, A. (2023) · Prenatal Diagnosis · doi:10.1002/pd.6411 · Cited by 72 · see citing works
Jost, E., Kosian, P., Jimenez Cruz, J., Albarqouni, S., Gembruch, U., Strizek, B., & Recker, F. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12216833 · Cited by 44 · see citing works
“Five studies used the freely available ‘HC18’ data set [45] for training and testing the algorithms, which contained 1334 2D images from 551 women of the standard fetal head plane [18,34,37,38,40].”
Method
- Authors
- Zhang, J., Petitjean, C., & Ainouz, S.
- Affiliations
- Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes (FR)
- Normandie University, INSA Rouen, UNIROUEN, UNIHAVRE, LITIS, 76000 Rouen, France
- Corresponding author
- Zhang, Jing
- Venue
- Journal of Imaging
- Publisher
- MDPI AG
- Published
- 2022-01-25
- Volume
- 8
- Issue
- 2
- Pages
- 23
- ISSN
- 2313-433X (ISSN-L)
- PMC
- PMC8877769
- OpenAlex
- W4210734788
- Cited by
- 21
- References
- 41
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 3 · 2023: 6 · 2022: 2
- FWCI
- 1.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Imaging (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- China Scholarship Council (CSC201801810060)
- 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
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- fetal head circumference, fetal biometry, ultrasound image analysis, regression estimation, convolutional neural networks, fetal growth monitoring, computational efficiency, memory usage
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