Ultrasound is increasingly becoming a 3D modality. Mechanical and matrix array transducers are able to deliver 3D images with good spatial and temporal resolution. The 3D imaging facilitates the application of automated image analysis to enhance workflows, which has the potential to make ultrasound a less operator dependent modality. However, the analysis of the more complex 3D images and definition of all examination standards on 2D images pose barriers to the use of 3D in daily clinical practice. In this paper, we address a part of the canonical fetal screening program, namely the localization of the abdominal cross-sectional plane with the corresponding measurement of the abdominal circumference in this plane. For this purpose, a fully automated pipeline has been designed starting with a random forest based anatomical landmark detection. A feature trained shape model of the fetal torso including inner organs with the abdominal cross-sectional plane encoded into the model is then transformed into the patient space using the landmark localizations. In a free-form deformation step, the model is individualized to the image, using a torso probability map generated by a convolutional neural network as an additional feature image. After adaptation, the abdominal plane and the abdominal torso contour in that plane are directly obtained. This allows the measurement of the abdominal circumference as well as the rendering of the plane for visual assessment. The method has been trained on 126 and evaluated on 42 abdominal 3D US datasets. An average plane offset error of 5.8 mm and an average relative circumference error of 4.9 % in the evaluation set could be achieved.
C. Lorenz, T. Brosch, C. Ciofolo-Veit, T. Klinder, T. Lefevre, A. Cavallaro, I. Salim, A. T. Papageorghiou, C. Raynaud, D. Roundhill, L. Rouet, N. Schadewaldt, and A. Schmidt-Richberg, "Automated abdominal plane and circumference estimation in 3D US for fetal screening," Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105740I (Presented at SPIE Medical Imaging: February 11, 2018; Published: 2 March 2018); https://doi.org/10.1117/12.2292729.
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