A computer-implemented method of generating a model for estimating an anatomical body measurement value from at least one 2-D ultrasound image including a contour of the anatomical body, the method comprising: providing a set of 3-D ultrasound images of the anatomical body; for each of said 3-D ultrasound images, determining a ground truth value of the anatomical body measurement, for example by manual measurement of the value or by mapping a segmentation model onto the 3-D image; generating from the set of 3-D images a set of 2-D ultrasound image planes each including a contour of the anatomical body and, for each of the 2-D ultrasound image planes, estimating a value of the anatomical body measurement from at least one of an outline contour measurement and a cross-sectional measurement of the anatomical body in the 2-D ultrasound image plane; generating said model by training a machine-learning algorithm so that it will generate an estimate of the anatomical body measurement value from inputs comprising at least one of an outline contour measurement and a cross-sectional measurement of a contour of the anatomical body that are determined by a user from a 2-D ultrasound image, the training being done by inputting to the machine-learning algorithm the ground truth values, the corresponding estimated values and at least one of the corresponding outline contour measurements and the corresponding cross-sectional measurements; and training the machine-learning algorithm to generate a further estimator function (g) to estimate the uncertainty in the estimated anatomical body measurement value that it generates in use, the training being done by inputting to the machine-learning algorithm the obtained ground truth values, the corresponding estimated values of the anatomical body measurement and the corresponding outline contour measurement and/or cross-sectional measurement of the anatomical body in the 2-D ultrasound image planes. providing a set of 3-D ultrasound images of the anatomical body; for each of said 3-D ultrasound images, determining a ground truth value of the anatomical body measurement, for example by manual measurement of the value or by mapping a segmentation model onto the 3-D image; generating from the set of 3-D images a set of 2-D ultrasound image planes each including a contour of the anatomical body and, for each of the 2-D ultrasound image planes, estimating a value of the anatomical body measurement from at least one of an outline contour measurement and a cross-sectional measurement of the anatomical body in the 2-D ultrasound image plane; generating said model by training a machine-learning algorithm so that it will generate an estimate of the anatomical body measurement value from inputs comprising at least one of an outline contour measurement and a cross-sectional measurement of a contour of the anatomical body that are determined by a user from a 2-D ultrasound image, the training being done by inputting to the machine-learning algorithm the ground truth values, the corresponding estimated values and at least one of the corresponding outline contour measurements and the corresponding cross-sectional measurements; and training the machine-learning algorithm to generate a further estimator function (g) to estimate the uncertainty in the estimated anatomical body measurement value that it generates in use, the training being done by inputting to the machine-learning algorithm the obtained ground truth values, the corresponding estimated values of the anatomical body measurement and the corresponding outline contour measurement and/or cross-sectional measurement of the anatomical body in the 2-D ultrasound image planes. <EOS>