An improved method of, and apparatus for, ultrasound examination to extract fetal characteristics

Abstract

There is provided a method of performing ultrasound examination of a foetus in utero comprising the steps of: a) acquiring a plurality of ultrasound images of the foetus in utero using an ultrasound i

Claims

1. A method of performing ultrasound examination of a fetus in utero, the method comprising the steps of:a) acquiring a plurality of ultrasound images of the fetus in utero using an ultrasound imaging apparatus, the ultrasound images comprising image data representative of the anatomical structure and appearance of a head, an abdomen and a femur of a fetus in utero;b) utilising, on a computing system, a plurality of sets of the ultrasound images in an ensemble machine learning model comprising one or more neural networks to determine an estimated fetal weight and generate an associated uncertainty estimate for the estimated fetal weight;c) providing, based on the uncertainty estimate, a notification to the operator of the ultrasound imaging apparatus in respect of one or more parameters indicative of the accuracy of one or more of the obtained ultrasound images.

2. A method according to claim 1, wherein step a) further comprises obtaining:at least one head image of the fetus;at least one abdomen image of the fetus; andat least one femur image of the fetus; andwherein each set of ultrasound images of the fetus comprises a head image, an abdomen image and a femur image.

3. A method according to claim 1 or 2, wherein step b) further comprises:d) utilising the obtained ultrasound images as a first set of ultrasound images in theensemble machine learning model to determine an estimated fetal weight;e) applying one or more transforms to the plurality of ultrasound images to generate an augmented set of ultrasound images; andf) utilising the augmented set of ultrasound images in the ensemble machine learning model to determine an uncertainty estimate.

4. A computer-implemented method according to claim 3, wherein the steps e) and f) are repeated N times to generate N estimates of fetal weight for uncertainty determination.

5. A method according to claim 2, 3 or 4, wherein step c) comprises:g) providing, based on the uncertainty estimate, a notification to the operator of the ultrasound imaging apparatus regarding whether the one or more parameters indicative of the accuracy of the one or more of the obtained ultrasound images has met one or more predetermined thresholds.

6. A method according to claim 5, wherein if, at step g) one or more predetermined thresholds have not been met for one or more ultrasound images, providing an instruction to the operator to repeat step a) to acquire further ultrasound images.

7. A method according to claim 1 or 2, wherein step b) further comprises:h) utilising a first subset of the obtained ultrasound images as a first set of ultrasoundimages in the ensemble machine learning model to determine an estimated fetal weight;i) applying one or more transforms to the first set of ultrasound images to generate an augmented set of ultrasound images; andj) utilising the augmented set of ultrasound images in the ensemble machine learning model to determine an uncertainty estimate for the first subset of the obtained ultrasound images; andk) repeating steps h) to g) for second and subsequent subsets of ultrasound images;wherein step c) comprises:I) providing a notification to the operator in relation to the uncertainty for one or more subsets of ultrasound images.

8. A method according to claim 7, wherein step I) comprises providing a notification to the operator in relation to subset of ultrasound images having the lowest uncertainty.

9. A method according to claim 8, wherein the method further comprises:a) automatically selecting the subset of ultrasound images having the lowest uncertainty.

10. A method according to claim 7, 8 or 9, wherein each subset of ultrasound images ofthe fetus comprises a head image, an abdomen image and a femur image.

11. A method according to claim 10, wherein the head image, the abdomen image and femur image are taken in or close to respective standard planes.

12. A method according to any one of the preceding claims, wherein the ensemble machine learning model comprises:a first machine learning model comprising one or more neural networks, the first machine learning model being configured to utilise a plurality of ultrasound images comprising image data representative of the anatomical structure of a head, an abdomen and a femur of a fetus in utero to generate one or more latent space representations of the anatomical structure and appearance of the fetus and one or more predicted measurements of the anatomical structure of the fetus; anda second machine learning model configured to utilise the one or more predicted measurements and the one or more latent space representations of the anatomical structure to generate an estimated weight of the fetus.

13. A method according to claim 12 when dependent upon claim 2, wherein the first machine learning model comprises a plurality of sub-networks, a discrete sub-network being allocated for each of the head, abdomen and femur images.

14. A method according to claim 13, wherein each sub-network comprises a first neural network for generating a respective latent space representation of anatomical structure and appearance and a second neural network for generating, from the respective latent space representation, one or more predicted measurements of the anatomical structure of the fetus.

15. A method according to claim 14, wherein the first neural network comprises a convolutional neural network (CNN) and / or the second neural network comprises a dense neural network (DNN) and / or the second machine learning model comprises a dense neural network (DNN).

16. A computer-implemented method according to any one of claims 13 to 15, wherein the sub-network allocated to the head image is operable to generate a latent space representation corresponding to the anatomical structure and appearance of the head of the fetus and a predicted measurement of the head circumference (HC) and / or the biparietal diameter (BPD) of the fetus; and / orwherein the sub-network allocated to the abdominal image is operable to generate a latent space representation corresponding to the anatomical structure and appearance of the abdomen of the fetus and a predicted measurement of the abdominal circumference (AC) of the fetus; and / orwherein the sub-network allocated to the femur image is operable to generate a latent space representation corresponding to the anatomical structure and appearance of the femur of the fetus and a predicted measurement of the femur length (FL) of the fetus.

17. An ultrasound imaging apparatus comprising:an ultrasound scanner configured to obtain a plurality of ultrasound images of a fetus in utero, the ultrasound images comprising image data representative of the anatomical structure and appearance of a head, an abdomen and a femur of the fetus; anda computing system comprising at least one hardware processor and configured toutilise a plurality of sets of the ultrasound images in an ensemble machine learning model comprising one or more neural networks to determine an estimated fetal weight and generate an associated uncertainty estimate for the estimated fetal weight;provide, based on the uncertainty estimate, a notification to the operator of the ultrasound in respect of one or more parameters indicative of the accuracy of one or more of the obtained ultrasound images.

18. An ultrasound imaging apparatus according to claim 17, wherein the ultrasound scanner is configured to obtain:at least one head image of the fetus;at least one abdomen image of the fetus; andat least one femur image of the fetus; andwherein each set of ultrasound images of the fetus comprises a head image, an abdomen image and a femur image.

19. An ultrasound imaging apparatus according to claim 17 or 18, wherein the computing system is further configured to:utilise the obtained ultrasound images as a first set of ultrasound images in the ensemble machine learning model to determine an estimated fetal weight;apply one or more transforms to the plurality of ultrasound images to generate an augmented set of ultrasound images; andutilise the augmented set of ultrasound images in the ensemble machine learning model to determine an uncertainty estimate.

20. An ultrasound imaging apparatus according to claim 19, further configured to repeat the applying and utilising steps N times to generate N estimates of fetal weight for uncertainty determination.

21. An ultrasound imaging apparatus according to claim 20, further configured to:provide, based on the uncertainty estimate, a notification to the operator of the ultrasound imaging apparatus regarding whether the one or more parameters indicative of the accuracy of the one or more of the obtained ultrasound images has met one or more predetermined thresholds.

22. An ultrasound imaging apparatus according to claim 21, wherein if one or more predetermined thresholds have not been met for one or more ultrasound images, the computer system is further configured to provide an instruction to the operator to repeat the acquisition to acquire further ultrasound images.

23. An ultrasound imaging apparatus according to claim 17 or 18, wherein the computer system is further configured to:utilise a first subset of the obtained ultrasound images as a first set of ultrasound images in the ensemble machine learning model to determine an estimated fetal weight;apply one or more transforms to the first set of ultrasound images to generate an augmented set of ultrasound images; andutilise the augmented set of ultrasound images in the ensemble machine learning model to determine an uncertainty estimate for the first subset of the obtained ultrasound images; andrepeat the applying and utilising for second and subsequent subsets of ultrasound images;and wherein the computer system is further configured to:provide a notification to the operator in relation to the uncertainty for one or more subsets of ultrasound images.

24. An ultrasound imaging apparatus according to claim 23, wherein the computer system is further configured to provide a notification to the operator in relation to subset of ultrasound images having the lowest uncertainty.

25. An ultrasound imaging apparatus according to claim 24, wherein the computer system is further configured to automatically select the subset of ultrasound images having the lowest uncertainty.

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