IP Library › Granted Patent US 12,465,321
Granted Patent B2
US 12,465,321 · App. 18/019,110 · Granted Nov 11, 2025

Ultrasound method

Inventors: Sally Collins (Oxford, GB); Yi Yin (Oxford, GB); Alys Clark (Grafton, NZ); Padraig Looney (Oxford, GB)
Assignee: OXFORD UNIVERSITY INNOVATION LIMITED
A61B8/0833A61B8/0891
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Quick Facts
Patent No.
US 12,465,321
App. No.
18/019,110
Granted
Nov 11, 2025
Kind
B2
Abstract

A computer-implemented method of automatically identifying a reference vessel in an ultrasound image, the method comprising: segmenting the image to identify an organ; locating a predetermined anatomical landmark specific to the organ; identifying appropriate vasculature for the organ; and selecting a reference vessel from the vasculature that has a size within a predetermined size range and a location with a predetermined distance range from the predetermined interface.

Claims (38)

1 . The computer-implemented method of automatically identifying a reference vessel in an ultrasound image, the method comprising:

segmenting the image to identify an organ;

locating a predetermined anatomical landmark specific to the organ;

identifying appropriate vasculature for the organ; and

selecting a reference vessel from the vasculature that has a size within a predetermined size range and a location with a predetermined distance range from the predetermined anatomical landmark.

2 . The method according to claim 1 wherein the predetermined size range is greater than 3 mm in diameter.

3 . The method according to claim 1 wherein segmenting comprises using a trained fully convolutional neural network.

4 . The method according to claim 1 wherein locating comprises using a multi-class transfer learning model.

5 . The method according to claim 4 wherein the multi-class transfer learning model comprises two independent pathways, a first pathway having parameters initialised using a segmentation model and a second pathway having parameters initialised using variance scaling.

6 . The method according to claim 1 wherein identifying comprises using an automated multi-seed region growing based process.

7 . The method according to claim 6 wherein identifying further comprises using a 3D medial axis based thinning process on the output of the growing based process.

8 . The method according to claim 1 wherein the organ is the placenta and preferably the anatomical landmark is the utero-placental interface.

9 . The method according to claim 8 wherein the predetermined distance range is from about 0.5 cm to about 1.5 cm.

10 . The computer-implemented method of mapping blood flow in a power Doppler image of an organ, the method comprising:

identifying a reference vessel in the power Doppler image using the method of claim 1 ;

determining a reference power Doppler value from the reference vessel; and

scaling power Doppler values in the power Doppler image based on the reference power Doppler value to obtain a standardised image of blood flow.

11 . The method according to claim 10 wherein determining comprises fitting a model vessel profile to the reference vessel and determining a standardization point not falling within the high shear vessel margins.

12 . The method according to claim 11 wherein fitting a model vessel profile comprises detecting a local maximum intensity point and using a region growing method to obtain an initial vessel region where all the interior intensities are greater than a threshold.

13 . The method according to claim 12 further comprising applying an iterative gradient descent technique is applied to minimize a cost function.

14 . A computer-implemented method of determining fractional moving blood volume in an organ, the method comprising:

obtaining a power Doppler image of the organ;

mapping blood flow in the power Doppler image to obtain a standardised image of blood flow using the method of claim 10 ; and

determining fractional moving blood volume using the standardised image of blood flow.

15 . A computer-implemented method of calculating a risk of adverse pregnancy outcomes comprising:

determining fractional moving blood volume in the placenta of a subject according to the method of claim 14 ;

measuring placental volume of the subject; and

calculating a risk score based at least in part on the fractional moving blood volume and the placental volume.

16 . A non-transitory computer readable medium comprising instructions that, when executed by a computer system, instruct the computer system to perform a method of automatically identifying a reference vessel in an ultrasound image, the method comprising:

segmenting image to identify an organ;

locating a predetermined anatomical landmark specific to the organ;

identifying appropriate vasculature for the organ; and

selecting a reference vessel from the vasculature that has a size within a predetermined size range and a location with a predetermined distance range from the determined anatomical landmark.

17 . An ultrasound scanner including a computer control system configured to perform a method of automatically identifying a reference vessel in an ultrasound image, the method comprising:

segmenting the image to identify an organ;

locating a predetermined anatomical landmark specific to the organ;

identifying appropriate vasculature for the organ; and

selecting a reference vessel from the vasculature that has a size within a predetermined size range and a location with a predetermined distance range from the predetermined anatomical landmark.

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 2, 2025
From: AUCKLAND UNISERVICES LIMITED
To: OXFORD UNIVERSITY INNOVATION LIMITED
Reel/Frame 070708/0026 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 14, 2023
From: CLARK, ALYS
To: AUCKLAND UNISERVICES LIMITED
Reel/Frame 063321/0670 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 14, 2023
From: COLLINS, SALLY; YIN, YI; LOONEY, PADRAIG
To: OXFORD UNIVERSITY INNOVATION LIMITED
Reel/Frame 063334/0281 →
Continuity (1)
Related Publication 20230277156A1 · Sep 7, 2023
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