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Measurement of head biometrics from fetal ultrasonography images is of keyimportance in monitoring the healthy development of fetuses. However, theaccurate measurement of relevant anatomical structures is subject to largeinter-observer variability in the clinic. To address this issue, an automatedmethod utilizing Fully Convolutional Networks (FCN) is proposed to determinemeasurements of fetal head circumference (HC) and biparietal diameter (BPD). AnFCN was trained on approximately 2000 2D ultrasound images of the head withannotations provided by 45 different sonographers during routine screeningexaminations to perform semantic segmentation of the head. An ellipse is fittedto the resulting segmentation contours to mimic the annotation typicallyproduced by a sonographer. The model's performance was compared withinter-observer variability, where two experts manually annotated 100 testimages. Mean absolute model-expert error was slightly better thaninter-observer error for HC (1.99mm vs 2.16mm), and comparable for BPD (0.61mmvs 0.59mm), as well as Dice coefficient (0.980 vs 0.980). Our resultsdemonstrate that the model performs at a level similar to a human expert, andlearns to produce accurate predictions from a large dataset annotated by manysonographers. Additionally, measurements are generated in near real-time at15fps on a GPU, which could speed up clinical workflow for both skilled andtrainee sonographers.

Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks