Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound
Open accessAbstract
Accurate standard plane (SP) localization is the fundamental step for prenatal ultrasound (US) diagnosis. Typically, dozens of US SPs are collected to determine the clinical diagnosis. 2D US has to perform scanning for each SP, which is time-consuming and operator-dependent. While 3D US containing multiple SPs in one shot has the inherent advantages of less user-dependency and more efficiency. Automatically locating SP in 3D US is very challenging due to the huge search space and large fetal posture variations. Our previous study proposed a deep reinforcement learning (RL) framework with an alignment module and active termination to localize SPs in 3D US automatically. However, termination of agent search in RL is important and affects the practical deployment. In this study, we enhance our previous RL framework with a newly designed adaptive dynamic termination to enable an early stop for the agent searching, saving at most 67% inference time, thus boosting the accuracy and efficiency of the RL framework at the same time. Besides, we validate the effectiveness and generalizability of our algorithm extensively on our in-house multi-organ datasets containing 433 fetal brain volumes, 519 fetal abdomen volumes, and 683 uterus volumes. Our approach achieves localization error of 2.52mm/10.26°, 2.48mm/10.39°, 2.02mm/10.48°, 2.00mm/14.57°, 2.61mm/9.71°, 3.09mm/9.58°, 1.49mm/7.54°for the transcerebellar, transventricular, transthalamic planes in fetal brain, abdominal plane in fetal abdomen, and mid-sagittal, transverse and coronal planes in uterus, respectively. Experimental results show that our method is general and has the potential to improve the efficiency and standardization of US scanning.
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Only three of the included studies investigated the use of AI algorithms in 3D US images, while 24 focused on 2D images.
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892 · see citing works
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473 · see citing works
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273 · see citing works
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083 · see citing works
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_49 · Cited by 7,945 · see citing works
Dueling Network Architectures for Deep Reinforcement Learning
Wang, Z., Schaul, T., Hessel, M., van Hasselt, H., Lanctot, M., & de Freitas, N. (2015) · arXiv · doi:10.48550/arxiv.1511.06581 · Cited by 3,300 · see citing works
Neuronlike adaptive elements that can solve difficult learning control problems
Barto, A. G., Sutton, R. S., & Anderson, C. W. (1983) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1983.6313077 · Cited by 3,238 · see citing works
Schaul, T., Quan, J., Antonoglou, I., & Silver, D. (2015) · arXiv · doi:10.48550/arxiv.1511.05952 · Cited by 3,120 · see citing works
Attention gated networks: learning to leverage salient regions in medical images
Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.012 · Cited by 2,014 · see citing works
The Tradeoffs of Large-Scale Learning
Bottou, L., & Bousquet, O. (2011) · The MIT Press eBooks · doi:10.7551/mitpress/8996.003.0015 · Cited by 1,201 · see citing works
Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan
Salomon, L. J., Alfirevic, Z., Berghella, V., Bilardo, C., Hernandez‐Andrade, E., Johnsen, S. L., Kalache, K., Leung, K. ‐., Malinger, G., Munoz, H., et al. (2011) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.8831 · Cited by 1,006 · see citing works
<scp>ISUOG</scp> Practice Guidelines: performance of first‐trimester fetal ultrasound scan
(2013) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.12342 · Cited by 621 · see citing works
Active Object Localization with Deep Reinforcement Learning
Caicedo, J. C., & Lazebnik, S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.286 · Cited by 454 · see citing works
SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound
Baumgartner, C. F., Kamnitsas, K., Matthew, J., Fletcher, T. P., Smith, S., Koch, L. M., Kainz, B., & Rueckert, D. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2712367 · Cited by 416 · see citing works
Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks
Chen, H., Ni, D., Qin, J., Li, S., Yang, X., Wang, T., & Heng, P. A. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2425041 · Cited by 379 · see citing works
Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans
Ghesu, F.-C., Georgescu, B., Zheng, Y., Grbic, S., Maier, A., Hornegger, J., & Comaniciu, D. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2782687 · Cited by 310 · see citing works
Efficient memory-based learning for robot control
Moore, A. (2021) · doi:10.48456/tr-209 · Cited by 284 · 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
FUIQA: Fetal Ultrasound Image Quality Assessment with Deep Convolutional Networks
Wu, L., Cheng, J.-Z., Li, S., Lei, B., Wang, T., & Ni, D. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2671898 · Cited by 242 · see citing works
Ultrasound Standard Plane Detection Using a Composite Neural Network Framework
Chen, H., Wu, L., Dou, Q., Qin, J., Li, S., Cheng, J.-Z., Ni, D., & Heng, P.-A. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2685080 · Cited by 164 · see citing works
Multi-task learning for quality assessment of fetal head ultrasound images
Lin, Z., Li, S., Ni, D., Liao, Y., Wen, H., Du, J., Chen, S., Wang, T., & Lei, B. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101548 · Cited by 129 · see citing works
Cascaded Fully Convolutional Networks for automatic prenatal ultrasound image segmentation
Wu, L., Yang, X., Li, S., Wang, T., Heng, P., & Ni, D. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950607 · Cited by 126 · see citing works
Standard Plane Localization in Ultrasound by Radial Component Model and Selective Search
Ni, D., Yang, X., Chen, X., Chin, C.-T., Chen, S., Heng, P. A., Li, S., Qin, J., & Wang, T. (2014) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2014.06.006 · Cited by 65 · see citing works
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network
Li, Y., Khanal, B., Hou, B., Alansary, A., Cerrolaza, J. J., Sinclair, M., Matthew, J., Gupta, C., Knight, C., Kainz, B., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00928-1_45 · Cited by 61 · see citing works
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents
Alansary, A., Folgoc, L. L., Vaillant, G., Oktay, O., Li, Y., Bai, W., Passerat‐Palmbach, J., Guerrero, R., Kamnitsas, K., Hou, B., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00928-1_32 · Cited by 55 · see citing works
Zhang, L., Chen, S., Chin, C. T., Wang, T., & Li, S. (2012) · Medical Physics · doi:10.1118/1.4736415 · Cited by 53 · see citing works
VP-Nets : Efficient automatic localization of key brain structures in 3D fetal neurosonography
Huang, R., Xie, W., & Noble, J. A. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.04.004 · Cited by 47 · see citing works
Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video
Huang, W., Bridge, C. P., Noble, J. A., & Zisserman, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66185-8_39 · Cited by 43 · see citing works
Nie, S., Yu, J., Chen, P., Wang, Y., & Zhang, J. Q. (2017) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2016.08.034 · Cited by 42 · see citing works
Gao, Y., & Noble, J. A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66185-8_35 · Cited by 37 · see citing works
Automated 3D Ultrasound Biometry Planes Extraction for First Trimester Fetal Assessment
Ryou, H., Yaqub, M. K., Cavallaro, A., Roseman, F., Papageorghiou, A. T., & Noble, J. A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47157-0_24 · Cited by 31 · see citing works
Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound
Dou, H., Yang, X., Qian, J., Xue, W., Qin, H., Wang, X., Yu, L., Wang, S., Xiong, Y., Heng, P., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32254-0_33 · Cited by 26 · see citing works
Chykeyuk, K., Yaqub, M. K., & Noble, J. A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-05530-5_6 · Cited by 24 · see citing works
Automatic recognition of fetal standard plane in ultrasound image
Lei, B., Liu, Z., Chen, S., Li, S., Ni, D., & Wang, T. (2014) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2014.6867815 · Cited by 23 · see citing works
Fetal Abdominal Standard Plane Localization through Representation Learning with Knowledge Transfer
Chen, H., Ni, D., Yang, X., Li, S., & Heng, P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10581-9_16 · Cited by 17 · see citing works
Standard plane localization in ultrasound by radial component
Yang, X., Ni, D., Qin, J., Li, S., Wang, T., Chen, S., & Heng, P. (2014) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2014.6868086 · Cited by 14 · see citing works
Automated abdominal plane and circumference estimation in 3D US for fetal screening
Cristian Lorenz, A., Brosch, T., Ciofolo, C., Klinder, T., Lefevre, T., Salim, I., Papageorghiou, A. T., Raynaud, C., Roundhill, D. N., Rouet, L., et al. (2018) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2292729 · Cited by 13 · see citing works
Offset regression networks for view plane estimation in 3D fetal ultrasound
Schmidt-Richberg, A., Schadewaldt, N., Klinder, T., Lenga, M., Trahms, R., Canfield, E., Roundhill, D. N., & Cristian Lorenz, A. (2019) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2512697 · Cited by 8 · see citing works
Chykeyuk, K., Yaqub, M. K., & Noble, J. A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-14104-6_6 · Cited by 8 · see citing works
Guideline-Based Machine Learning for Standard Plane Extraction in 3D Cardiac Ultrasound
Zhu, P., & Li, Z. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-61188-4_13 · Cited by 7 · see citing works
Automatic quality assessment for 2D fetal sonographic standard plane based on multi-task learningPreprint
Luo, H., Liu, H., Li, K., & Zhang, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.05260 · Cited by 7 · see citing works
Diagnostic Plane Extraction from 3D Parametric Surface of the Fetal Cranium.
Namburete, A. I. L., Stebbing, R. V., & Noble, J. A. (2014) · MIUA · Cited by 6 · see citing works
Flagged first, then by citations
5 of the 53 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [32][32] C. J. Watkins and P. Dayan, 'Q-learning,' Machine Learning , vol. 8, no. 3-4, pp. 279-292, 1992. -
Cited by
Sorted by citations
A review on deep-learning algorithms for fetal ultrasound-image analysis
Fiorentino, M. C., Villani, F. P., Cosmo, M. D., Frontoni, E., & Moccia, S. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102629 · Cited by 209 · 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
“Pluym et al. and Yang et al. analyzed 3D US volumes for the localization and measurement of various intracranial standard planes, such as the transventricular, transthalamic, or transcerebellar plane [23,24].”
BackgroundAlzubaidi, M. S., Agus, M., Alyafei, K. A., Al-Thelaya, K., Shah, U., Abd-Alrazaq, A. A., Anbar, M., Makhlouf, M. A., & Househ, M. S. (2022) · iScience · doi:10.1016/j.isci.2022.104713 · Cited by 40 · see citing works
RecON: Online learning for sensorless freehand 3D ultrasound reconstruction
Luo, M., Yang, X., Wang, H., Dou, H., Hu, X., Huang, Y., Ravikumar, N., Xu, S., Zhang, Y., Xiong, Y., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102810 · Cited by 37 · see citing works
HFSCCD: A Hybrid Neural Network for Fetal Standard Cardiac Cycle Detection in Ultrasound Videos
Pu, B., Li, K., Chen, J., Lu, Y., Zeng, Q., Yang, J., & Li, S. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3370507 · Cited by 30 · see citing works
FetSAM: Advanced Segmentation Techniques for Fetal Head Biometrics in Ultrasound Imagery
Alzubaidi, M. S., Shah, U., Agus, M., & Househ, M. S. (2024) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2024.3382487 · Cited by 20 · see citing works
Fast fetal head compounding from multi-view 3D ultrasound
Wright, R., Gómez, A., Zimmer, V. A., Toussaint, N., Khanal, B., Matthew, J., Skelton, E., Kainz, B., Rueckert, D., Hajnal, J. V., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102793 · Cited by 15 · see citing works
Comprehensive review of reinforcement learning for medical ultrasound imaging
Elmekki, H., Islam, S., Alagha, A., Sami, H., Spilkin, A., Zakeri, E., Zanuttini, A., Bentahar, J., Kadem, L., Xie, W., et al. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11268-w · Cited by 14 · see citing works
A comprehensive review on fetal health surveillance using ultrasound imagery
Mushtaq, G., & Veningston, K. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-06644-8 · Cited by 14 · see citing works
FetusMapV2: Enhanced fetal pose estimation in 3D ultrasound
Chen, C., Yang, X., Huang, Y., Shi, W., Cao, Y., Luo, M., Hu, X., Zhu, L., Yu, L., Yue, K., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103013 · Cited by 12 · see citing works
Cheng, J., Wang, H., Li, R., Li, X., Zhou, X., Yang, X., Wang, Y., Xiong, L., Fan, H., Wang, T., et al. (2022) · Medical Physics · doi:10.1002/mp.15548 · Cited by 10 · see citing works
He, G., Li, Z., Zhu, Z., Han, T., Cao, Y., Chen, C., Huang, Y., Dou, H., Liang, L., Zhang, F., et al. (2025) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-024-07108-4 · Cited by 9 · see citing works
Gong, Z., Cai, G., Zeng, J., Wen, B., Liu, H., Lin, J., Meng, X., Zeng, P., Shi, J., Xie, R., et al. (2025) · BMEMat · doi:10.1002/bmm2.70010 · Cited by 8 · see citing works
Zou, Y., Dou, H., Huang, Y., Yang, X., Qian, J., Zhen, C., Ji, X., Ravikumar, N., Chen, G., Huang, W., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16440-8_29 · Cited by 8 · see citing works
Flip Learning: Weakly supervised erase to segment nodules in breast ultrasound
Huang, Y., Chang, A., Dou, H., Xing, T., Zhou, X., Cao, Y., Huang, R., Frangi, A. F., Bao, L., Yang, X., et al. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103552 · Cited by 7 · see citing works
Standard plane localization using denoising diffusion model with multi-scale guidance
Dou, H., Huang, Y., Huang, Y., Yang, X., Zhen, C., Zhang, Y., Xiong, Y., Huang, W., & Ni, D. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108619 · Cited by 5 · see citing works
Computational methods for the analysis of early-pregnancy brain ultrasonography: a systematic review
Bastiaansen, W., Klein, S., Koning, A. H. J., Niessen, W. J., Steegers‐Theunissen, R. P. M., & Rousian, M. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104466 · Cited by 5 · see citing works
Zhang, Q., Han, X., Zhu, L. Q., & Liu, L. (2023) · Biomimetic Intelligence and Robotics · doi:10.1016/j.birob.2023.100129 · Cited by 5 · see citing works
Qu, Z., Zhuo, L., Xu, N., Yin, H., Wang, Z., & Li, X. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3557065 · Cited by 4 · see citing works
Deep reinforcement learning in medical imaging
Zhou, S. K., & Wang, Q. (2024) · Elsevier eBooks · doi:10.1016/b978-0-32-385124-4.00010-6 · Cited by 4 · see citing works
Huang, Y., Xu, Y., Dou, H., Deng, J. C., Yang, X., Zheng, H., & Ni, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04965-0_61 · Cited by 3 · see citing works
Agent Based Fetal Face Segmentation for Standard Plane Localization in 3D Ultrasound
Huang, J., Wang, R., Jiang, W., Shao, S., & Chen, T. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10393929 · Cited by 2 · see citing works
Ideal Midsagittal Plane Detection Using Deep Hough Plane Network for Brain Surgical Planning
Qin, C., Zhou, W.-X., Chang, J., Chen, Y., Wu, D., Liu, Y., Feng, M., Wang, R., Yang, W., & Yao, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16449-1_56 · Cited by 2 · see citing works
Yu, Y., Chen, Z., Zhuang, Y., Yi, H., Han, L., Chen, K., & Lin, J. (2022) · Journal of X-Ray Science and Technology · doi:10.3233/xst-221278 · Cited by 2 · see citing works
Shen, C., Deng, Z., Wang, J., Wang, S., & Chen, C. (2023) · WRC Symposium on Advanced Robotics and Automation (WRC SARA) · doi:10.1109/wrcsara60131.2023.10261822 · Cited by 1 · see citing works
3D Heart Reconstruction from Sparse Pose-Agnostic 2D Echocardiographic Slices
Chen, Z., Chen, J., Zhuo, W., Xue, W., & Ni, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-06329-8_4 · Cited by 1 · see citing works
Unsupervised spatial registration of fetal cranial ultrasound slices for radiomics analysis
Jiang, S., Chen, Z., Zhuang, Y., Han, L., Xiao, Y., Hua, Z., Wang, R., Feng, Q., Chen, K., & Lin, J. (2025) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2025.101602
Aoyama, G., Zhou, Z., Yang, H., Li, Y., Han, B., Chapman, J. V., Asami, M., Nozaki, Y. O., Fujimoto, S., & Aoyagi, K. (2026) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ae4808
Deep Learning–Based Segmentation of Fetal Anatomical Structures in the First Trimester
Hong, S., Kim, O. H., Kang, B. S., Won, S., Ko, H. S., Byun, J. H., Wie, J. H., Kwon, J. Y., Lee, K. E., Shin, J. E., et al. (2026) · Prenatal Diagnosis · doi:10.1002/pd.70156
Deep Learning-based Intelligent Diagnosis of Congenital Uterine Anomalies in 3-D Ultrasound
Xu, Y., Huang, Y., Deng, J., Zhang, Y., Zhang, H., Qu, J., Zheng, S., Tang, X., Chen, H., Chen, C., et al. (2026) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2026.08.015
Anatomy-Aware Standard Plane Localization in 3D Ultrasound with Geometric Constraints
Nie, J., Ouyang, X., Zhou, Y., Wan, Q., Xue, Z., Cao, X., & Shen, D. G. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38236-8_5
Tjondronegoro, D., Yuwono, E. I., & Costa, F. D. S. (2026) · Research Square · doi:10.21203/rs.3.rs-10833481/v1
- Authors
- Yang, X., Dou, H., Huang, R., Xue, W., Huang, Y., Qian, J., Zhang, Y., Luo, H., Guo, H., Wang, T., Xiong, Y., & Ni, D.
- Affiliations
- Shenzhen University (CN)
- ORCID
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2021-07
- Volume
- 40
- Issue
- 7
- Pages
- 1950-1961
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W3142022860
- Cited by
- 32
- References
- 53
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 3 · 2023: 8 · 2022: 6
- FWCI
- 1.53
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Research Explorer (The University of Manchester) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Key Research and Development Program of China (2019YFC0118300)
- Shenzhen Peacock Plan (KQTD2016053112051497, KQJSCX20180328095606003)
- Natural Science Foundation of Shenzhen (JCYJ20190808115419619)
- 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 › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- 3D ultrasound, warm start, deep reinforcement learning, prenatal ultrasound, fetal brain, uterus, inference efficiency
- MeSH terms
- Abdomen, Algorithms, Female, Humans, Pregnancy, Ultrasonography, Ultrasonography, Prenatal, Imaging, Three-Dimensional
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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