mlsanityData to 7 Oct 2026

Deep Learning Fetal Ultrasound Video Model Match Human Observers in Biometric Measurements

arXiv 2205.13835 ↗First posted 27 May 2022eess.IV cs.AI cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

Objective. This work investigates the use of deep convolutional neural networks (CNN) to automatically perform measurements of fetal body parts, including head circumference, biparietal diameter, abdominal circumference and femur length, and to estimate gestational age and fetal weight using fetal ultrasound videos. Approach. We developed a novel multi-task CNN-based spatio-temporal fetal US feature extraction and standard plane detection algorithm (called FUVAI) and evaluated the method on 50 freehand fetal US video scans. We compared FUVAI fetal biometric measurements with measurements made by five experienced sonographers at two time points separated by at least two weeks. Intra- and inter-observer variabilities were estimated. Main results. We found that automated fetal biometric measurements obtained by FUVAI were comparable to the measurements performed by experienced sonographers The observed differences in measurement values were within the range of inter- and intra-observer variability. Moreover, analysis has shown that these differences were not statistically significant when comparing any individual medical expert to our model. Significance. We argue that FUVAI has the potential to assist sonographers who perform fetal biometric measurements in clinical settings by providing them with suggestions regarding the best measuring frames, along with automated measurements. Moreover, FUVAI is able perform these tasks in just a few seconds, which is a huge difference compared to the average of six minutes taken by sonographers. This is significant, given the shortage of medical experts capable of interpreting fetal ultrasound images in numerous countries.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 20 of its 57 references that are in the map. First posted on arXiv 27 May 2022.

Builds on

20 of 57 references.

References · 20

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  4. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
  5. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  6. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  7. 2017Cascaded Pyramid Network for Multi-Person Pose Estimationcited by 341
  8. 2018Attention Gated Networks: Learning to Leverage Salient Regions in Medical Imagescited by 271
  9. 2019Multi-scale self-guided attention for medical image segmentationcited by 84
  10. 2016SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasoundcited by 71
  11. 2018Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Imagescited by 42
  12. 2017Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networkscited by 26
8 more
  1. 20183D Context Enhanced Region-based Convolutional Neural Network for End-to-End Lesion Detectioncited by 24
  2. 2018Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networkscited by 16
  3. 2018Simultaneous Segmentation and Classification of Bone Surfaces from Ultrasound Using a Multi-feature Guided CNNcited by 16
  4. 2021AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planescited by 13
  5. 2021FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurementscited by 13
  6. 2019Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographerscited by 10
  7. 2018Attaining human-level performance with atlas location autocontext for anatomical landmark detection in 3D CT datacited by 8
  8. 2018A multi-level convolutional LSTM model for the segmentation of left ventricle myocardium in infarcted porcine cine MR imagescited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2025Accurate and Efficient Fetal Birth Weight Estimation from 3D Ultrasoundcited by 1
  2. 2026Beyond Benchmarks of IUGC: Rethinking Requirements of Deep Learning Methods for Intrapartum Ultrasound Biometry from Fetal Ultrasound Videoscited by 1
  3. 2024Measuring proximity to standard planes during fetal brain ultrasound scanningcited by 0