Patent Description:
In current clinical practice, fetal weight is typically estimated using a combination of 2D measurements, extracted from 2D (= two-dimensional) ultrasound images. The measurements are performed on 2D standard clinical planes. Fetal weight estimation is very important in the management of the pregnancy but is often under or over estimated.

Where the fetus is too small or too large, errors in fetal weight estimation during pregnancy may lead to inadequate care given to the infant during pregnancy or after birth. Depending on the actual weight of the fetus, the balance between relative coefficients used in the current models will affect the precision of the estimated fetal weight. For example, in the case of fetal weights over <NUM>, formulae that depend more on the abdominal circumference and femur length in the calculation provide more accurate predictions of birth weight.

Current equations used for fetal weight estimation are based on statistical regressions and estimations based on population analysis. The number of different equations is very high, which leads to various possible estimates according to the selected model and introduces uncertainty in which model to apply.

In order to improve accuracy, it is known to use fractional limb volume as an additional parameter in fetal weight equations, such as thigh and arm fractional volumes. However, there remain inaccuracies in the fetal weight estimation.

It is also known to use 3D (=three-dimensional) images to perform fetal weight estimation such as disclosed in <CIT> and <CIT>. <CIT> discloses the use of multiple 3D ultrasound measurements in order to make fetal weight estimation more robust. A head image, abdominal image and femur image are each segmented and the fetal weight estimation is based on the resulting segmentations. However, these measurements may be difficult to obtain, as the fetus does not necessarily fit in one 3D acquisition. This is especially the case for the torso of the fetus after <NUM> weeks. It is then usually too large to fit in one single imaging volume.

A partial torso covering will lead to an under estimation of the torso volume and thus of the torso weight. Thus, it is necessary to obtain an estimation of the fraction of the torso that is missing in the 3D acquisition.

There is therefore a need for a practical method and system that is able to estimate accurately a torso volume from a partial 3D torso acquisition, to enable more accurate fetal weight estimation from a 3D fetus image.

According to an aspect of the invention, there is provided a method for performing fetal weight estimation, the method comprising:.

The 2D and 3D images are preferably ultrasound images. The 3D image for example covers only a portion of the torso and hence only a portion of the spine, whereas the 2D image covers more of the spine, and preferably the entire spine of the fetus.

The method estimates a missing portion of the torso from the 3D image. The 3D shape of the missing portion of the torso may be estimated, or there may simply be an estimation of the missing volume as a fraction i.e. the percentage volume of the torso that is missing from the 3D image.

The full torso 3D volume may be considered to be extrapolated from the portion of the volume that is present in the 3D image. However, instead of extrapolating based only on the information present in the 3D image itself, a spine segmentation from the 2D image, which covers more and preferably all of the spine, enables the extrapolation to be more accurate. In particular, the 3D volume that is present is extrapolated with knowledge of more (and preferably all) of the spine shape. For example, a torso shape model may be fitted not only to the 3D image data that is present, but also to the additional spine portions obtained from the 2D sagittal image. Thus, the torso shape model may be applied more accurately and give more accurate volume and weight estimations than when applied to the 3D image alone.

The method for example involves creating the first and second segmentations using a Deep Learning model. The first and second segmentations may for example be obtained using landmark detection. Such landmark detection may use a pose estimation network or an object detection network.

Registering the first and second segmentations for example comprises matching spinal features, in particular vertebrae, between the first and second segmentations. As well as matching spinal features, the registering may also or alternatively comprise matching additional non-spinal landmarks, such as the heart, stomach etc..

Creating the torso segmentation may also be implemented using a Deep Learning model.

The method may comprise estimating a missing portion of the torso by fitting an ellipsoidal torso shape model to the torso segmentation.

The method for example comprises displaying the torso segmentation and the missing portion to a user. This allows the user to assess if the extrapolation looks correct. The method may then further comprise receiving user input based on the displayed torso segmentation and missing portion to instruct adjustment of the shape of the missing portion. Thus, the user can make adjustments to the extrapolation, and the volume and weight estimations will then be adjusted accordingly.

The fetal weight estimation is for example based on a global homogeneous tissue density.

The invention also provides a computer program product comprising computer program code which is adapted, when said computer program is run on a computer, to implement the method defined above.

The invention also provides an ultrasound imaging system comprising:.

The processor is for example adapted to register the first and second segmentations by matching vertebrae between the first and second segmentations. The missing portion of the torso is for example estimated by fitting an ellipsoidal torso shape model to the torso segmentation.

It should be understood that the detailed description and specific examples, while indicating exemplary embodiments of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention. These and other features, aspects, and advantages of the present invention will become better understood from the following description, appended claims, and accompanying drawings.

The invention provides a method and system for estimating the weight of a fetus. The fetal weight estimation is made by segmenting the fetal spine from a 2D sagittal image of the spine and segmenting the fetal spine from a 3D image of the torso. The two segmentations are registered, and the torso is segmented from the 3D image. If the 3D image is missing a portion of the torso, the missing portion of the torso is estimated based on the registered spine segmentations and the torso segmentation. A complete torso volume can then be estimated by extrapolating from the part of the torso present in the 3D image, and an accurate fetus weight estimation may be made. Thus, the use of a 2D sagittal image covering a larger volume of the torso than a 3D image enables increased accuracy in deriving a fetal volume and weight estimation from the 3D image.

The general operation of an exemplary ultrasound system will first be described, with reference to <FIG>. The system produces ultrasound images, and these are then processed in the manner explained further below in order to implement the method of the invention.

The system comprises an array transducer probe <NUM> which has a transducer array <NUM> for transmitting ultrasound waves and receiving echo information. The transducer array <NUM> may comprise capacitive micromachined ultrasonic transducers (CMUT); piezoelectric transducers, formed of materials such as lead zirconate titanate (PZT) or polyvinylidene difluoride (PVDF); or any other suitable transducer technology. In this example, the transducer array <NUM> is a two-dimensional array of transducers <NUM> capable of scanning either a 2D plane or a three-dimensional volume of a region of interest.

This invention in particular has both 2D imaging functionality and 3D imaging functionality.

Optionally, the transducer array <NUM> is coupled to a micro-beamformer <NUM> which controls reception of signals by the transducer elements. Micro-beamformers are capable of at least partial beamforming of the signals received by sub-arrays.

The system includes a transmit/receive (T/R) switch <NUM>, which the micro-beamformer <NUM> can be coupled to and which switches the array between transmission and reception modes, and protects the main beamformer <NUM> from high energy transmit signals in the case where a micro-beamformer is not used and the transducer array is operated directly by the main system beamformer. The transmission of ultrasound beams from the transducer array <NUM> is directed by a transducer controller <NUM> coupled to the micro-beamformer by the T/R switch <NUM> and a main transmission beamformer (not shown), which can receive input from the user's operation of the user interface or control panel <NUM>. The controller <NUM> can include transmission circuitry arranged to drive the transducer elements of the array <NUM> (either directly or via a micro-beamformer) during the transmission mode.

In a typical line-by-line imaging sequence, the beamforming system within the probe may operate as follows. During transmission, the beamformer (which may be the micro-beamformer or the main system beamformer depending upon the implementation) activates the transducer array, or a sub-aperture of the transducer array. The sub-aperture may be a one-dimensional line of transducers or a two-dimensional patch of transducers within the larger array. In transmit mode, the focusing and steering of the ultrasound beam generated by the array, or a sub-aperture of the array, are controlled as described below.

The shifted sub-aperture is then activated, and the process repeated until all of the transducer elements of the transducer array have been activated.

For each line (or sub-aperture), the total received signal, used to form an associated line of the final ultrasound image, will be a sum of the voltage signals measured by the transducer elements of the given sub-aperture during the receive period. The resulting line signals, following the beamforming process below, are typically referred to as radio-frequency (RF) data. Each line signal (RF data set) generated by the various sub-apertures then undergoes additional processing to generate the lines of the final ultrasound image. The change in amplitude of the line signal with time will contribute to the change in brightness of the ultrasound image with depth, wherein a high amplitude peak will correspond to a bright pixel (or collection of pixels) in the final image. A peak appearing near the beginning of the line signal will represent an echo from a shallow structure, whereas peaks appearing progressively later in the line signal will represent echoes from structures at increasing depths within the subject.

In addition, upon receiving the echo signals from within the subject, it is possible to perform the inverse of the above-described process in order to perform receive focusing. In other words, the incoming signals may be received by the transducer elements and subject to an electronic time delay before being passed into the system for signal processing. The simplest example of this is referred to as delay-and-sum beamforming. It is possible to dynamically adjust the receive focusing of the transducer array as a function of time.

In the reception channel, partially beamformed signals are produced from the channel data by the micro-beamformer <NUM> and are then passed to a main receive beamformer <NUM> where the partially beamformed signals from individual patches of transducers are combined into a fully beamformed signal, referred to as radio frequency (RF) data.

The beamformers for transmission and for reception may be implemented in different hardware and can have different functions.

The RF signals may then be coupled to a B-mode (i.e. brightness mode, or 2D imaging mode) processor <NUM> and a Doppler processor <NUM>. The B-mode processor <NUM> performs amplitude detection on the received ultrasound signal for the imaging of structures in the body, such as organ tissue and blood vessels. In the case of line-by-line imaging, each line (beam) is represented by an associated RF signal, the amplitude of which is used to generate a brightness value to be assigned to a pixel in the B-mode image. B-mode images of such structures may be formed in the harmonic or fundamental image mode, or a combination of both as described in <CIT>) and <CIT>) The Doppler processor <NUM> processes temporally distinct signals arising from tissue movement and blood flow for the detection of moving substances, such as the flow of blood cells in the image field.

The structural and motion signals produced by the B-mode and Doppler processors are coupled to a scan converter <NUM> and a multi-planar reformatter <NUM>. The scan converter <NUM> arranges the echo signals in the spatial relationship from which they were received in a desired image format. In other words, the scan converter acts to convert the RF data from a cylindrical coordinate system to a Cartesian coordinate system appropriate for displaying an ultrasound image on an image display <NUM>. In the case of B-mode imaging, the brightness of pixel at a given coordinate is proportional to the amplitude of the RF signal received from that location. For instance, the scan converter may arrange the echo signal into a two-dimensional (2D) sector-shaped format, or a pyramidal three-dimensional (3D) image. The scan converter can overlay a B-mode structural image with colors corresponding to motion at points in the image field, where the Doppler-estimated velocities to produce a given color. The combined B-mode structural image and color Doppler image depicts the motion of tissue and blood flow within the structural image field. The multi-planar reformatter will convert echoes that are received from points in a common plane in a volumetric region of the body into an ultrasound image of that plane, as described in <CIT>. A volume renderer <NUM> converts the echo signals of a 3D data set into a projected 3D image as viewed from a given reference point as described in <CIT>.

The 2D or 3D images are coupled from the scan converter <NUM>, multi-planar reformatter <NUM>, and volume renderer <NUM> to an image processor <NUM> for further enhancement, buffering and temporary storage for display on an image display <NUM>. The imaging processor may be adapted to remove certain imaging artifacts from the final ultrasound image, such as: acoustic shadowing, for example caused by a strong attenuator or refraction; posterior enhancement, for example caused by a weak attenuator; reverberation artifacts, for example where highly reflective tissue interfaces are located in close proximity; and so on. In addition, the image processor may be adapted to handle certain speckle reduction functions, in order to improve the contrast of the final ultrasound image.

In addition to being used for imaging, the blood flow values produced by the Doppler processor <NUM> and tissue structure information produced by the B-mode processor <NUM> are coupled to a quantification processor <NUM>. The quantification processor produces measures of different flow conditions such as the volume rate of blood flow in addition to structural measurements such as the sizes of organs and gestational age. The quantification processor may receive input from the user control panel <NUM>, such as the point in the anatomy of an image where a measurement is to be made.

Output data from the quantification processor is coupled to a graphics processor <NUM> for the reproduction of measurement graphics and values with the image on the display <NUM>, and for audio output from the display device <NUM>. The graphics processor <NUM> can also generate graphic overlays for display with the ultrasound images. These graphic overlays can contain standard identifying information such as patient name, date and time of the image, imaging parameters, and the like. For these purposes the graphics processor receives input from the user interface <NUM>, such as patient name. The user interface is also coupled to the transmit controller <NUM> to control the generation of ultrasound signals from the transducer array <NUM> and hence the images produced by the transducer array and the ultrasound system. The transmit control function of the controller <NUM> is only one of the functions performed. The controller <NUM> also takes account of the mode of operation (given by the user) and the corresponding required transmitter configuration and band-pass configuration in the receiver analog to digital converter. The controller <NUM> can be a state machine with fixed states.

<FIG> shows a 2D acquisition of a sagittal view of the spine. This is part of the clinical screening routine in Fetal Ultrasound. The 2D ultrasound field of view is less constrained than in 3D, which enables the user to perform a 2D acquisition where the whole spine of the fetus is visible throughout most of the pregnancy.

However, as the fetus gets bigger, it is less likely that a whole torso would fit in a 3D acquisition, leading to a partial torso volumetric imaging as shown in <FIG>.

The invention is based on the use of the information from a 2D sagittal view of the spine to infer which portion of the torso is missing in a 3D acquisition.

<FIG> shows the steps of a method <NUM> for performing fetal weight estimation.

In step <NUM> a 2D ultrasound acquisition of a sagittal view of the spine is implemented. The user is for example responsible for the acquisition of an appropriate 2D sagittal view in known manner.

In step <NUM>, a 3D ultrasound acquisition of the torso is implemented. The obtained 3D image may cover the torso only partially, as explained above.

The 2D and 3D images may be obtained from separate probes used in sequence, or from a 3D probe with 2D acquisition capability.

In step <NUM>, image processing is performed to segment the spine from the 2D sagittal view image.

In step <NUM>, image processing is performed to segment the spine from the 3D image. This is a segmentation to obtain the spine shape in 3D.

The image processing to perform spine segmentation can be performed using Deep learning methods, such as a U-Net model, or any kind of segmentation model. Landmarks can be detected along the spine, for example detecting predefined precise vertebrae. Landmark detection can be implemented using pose estimation networks in both 3D and 2D (e.g., high-resolution network (HRnet)), or object detection networks in 2D (e.g., You Only Look Once (YOLO), Fast Region Based Convolutional Neural Networks (FastRCNN)). The identification of landmarks assists in the registration of the spine between the two images, in step <NUM>.

By way of example, the spine may be automatically detected in a 3D ultrasound abdominal image by combining a morphological filter which detects elongated bright structures and a deep learning (DL) based vertebrae detector, in order to take advantage of the strengths of both methods.

A morphological filter may be used for each voxel in the abdominal image volume in a given spherical neighborhood, to compare the intensity of the voxels along a direction with the intensity of the other voxels. The filter responses are computed for various neighborhood radii and orientations and combined to obtain a global response. The global responses of neighboring voxels are aggregated to define connected components which correspond to the best filter responses.

Although some of the responses are accurately positioned on the spine using this approach, others may also be present which are outliers, that may for example be located on ribs or other elongated structures such as long bones.

The deep learning-based vertebrae detector is a 2D fully convolutional network whose input is made of 2D slices, extracted orthogonally to an image-based z-axis. The volume slicing produces a large amount of data with similar features, which is appropriate for deep learning methods. The network output is a down-sampled probability map, with values closer to <NUM> where the spine might be located. A 3D deep learning-based vertebrae detector is formed by stacking all the obtained 2D probability maps for one volume. This output heatmap is coarser than the morphological filter output, but more robustly located around the vertebrae.

By combining the deep-learning vertebrae detector and the morphological filter responses, the network output is refined and the filter responses that are outside the spine are rejected, so that a robust spine binary mask is finally obtained for the abdominal image.

This is one way to identify the spine location, but any other suitable image processing techniques may be employed for detecting the unique spine shape.

Registering the two segmentations of the spine in step <NUM> is performed based on the spine segmentations by matching (anatomical) landmarks. For example, vertebrae in both the 2D image and the 3D image are matched using the landmark (vertebrae) identification performed during the segmentation steps <NUM>, <NUM>.

The registration step can take place be between the 3D volume and the 2D image directly, e.g., taking the position of the vertebrae. Alternatively, a sagittal plane can be derived from the 3D image so that that registration then takes place between the spine segmentations in corresponding sagittal views.

Other landmarks may additionally or alternatively be used, such as the heart, the stomach or the umbilical vein. Localizing any of these landmarks on both the 2D and 3D views will help in the registering of the two image acquisitions. As mentioned above, this landmark detection task can be done using pose estimation or object detection networks.

In step <NUM>, image processing is used to segment the torso from the 3D image. This can be performed using Deep learning methods, such as a 3D U-Net model. The torso segmentation is a 3D segmentation applied to the 3D image.

It is possible also to use a 2D segmentation of the torso, for instance to better refine the final volume, as it provides information on the torso shape after the registration step.

The registration between the spines in the two images, and the torso segmentation, enables an evaluation of which portion of the torso is missing from the volume captured in the 3D image, shown as regions <NUM> in <FIG>.

Thus, the spine shape captured from the 2D image, and the registration between the partial spine portion in the 3D image and the spine in the 2D image, enable the full spine shape, and hence the full torso size and shape, to be extrapolated from the 3D image of a partial volume.

An accurate torso volume and/or weight estimation can then be performed using volume calculations from the extrapolated 3D image. A complete torso volume is estimated in step <NUM> using the at least one 3D image, based on the estimation of the missing portion of the torso, and a fetus weight is estimated in step <NUM> based on the estimated complete torso volume.

The fetal weight estimation may for example be performed using a global homogeneous tissue density. In other words, the densities of all the different tissues of the fetus may be averaged, thereby generating a unique density coefficient, and multiplied by the fetal volume discerned from the extrapolated image segmentation in order to perform a fetal weight estimation with minimal computational cost. The average density of a fetus may be defined based on relevant literature.

The extrapolation for example may comprise fitting an ellipsoidal torso shape model to the torso segmentation, as illustrated in <FIG>.

<FIG> shows an abdominal image <NUM> comprising a partial view of the torso of the fetus.

Using a normal segmentation operation, the torso of the fetus is identified as indicated by the solid line outline <NUM>. However, due to the partially incomplete view of the torso, the segmentation excludes the missing information <NUM>, which would otherwise form the remaining portion of the torso of the fetus. This missing information would reduce the accuracy of the final fetal weight estimation.

The partial imaging of the torso may be compensated by way of a model torso image. The model torso image, in this case a model <NUM> with an ellipsoidal fitting, is fit to the segmented partial torso of the fetus as explained above, using both the data of the 3D image and the spine shape obtained from the 2D sagittal image, which thus extends into the missing region.

The model <NUM> may thus be applied more accurately to the segmented torso portion for example to obtain a ratio of the visible volumes and the total volume. Finally, the segmented torso is multiplied by the obtained ratio in order to compensate for the missing information.

The accuracy of the estimated fetal weight may be improved by identifying the volumes of the various tissues present within a segmented image, instead of assuming a homogeneous density.

On a graphical user interface, control points <NUM> may be added to allow for manual correction by the user, as illustrated in <FIG>. If a control point <NUM> is moved by the clinician, the border of the torso is updated accordingly.

In this case, only a first step of torso completion is performed automatically using AI or non-AI algorithms. The user is then presented with the results of this automatic torso extrapolation process and can decide to accept the results, or correct the results manually by moving come control points on the completed region.

Once a final torso segmentation is accepted, a volume and/or weight estimation can then be computed automatically based on known relations between the torso on the one hand, and volume and/or weight on the other hand.

Claim 1:
A method (<NUM>) for performing fetal weight estimation, the method comprising:
receiving (<NUM>) at least one 2D image of a sagittal view of the spine of the fetus;
receiving (<NUM>) at least one 3D image of the torso of the fetus;
segmenting (<NUM>) the spine from the at least one 2D image to create a first segmentation, and segmenting (<NUM>) the spine from the at least one 3D image to create a second segmentation;
registering (<NUM>) the first and second segmentations, wherein registering the first and second segmentations comprises matching landmarks;
segmenting (<NUM>) the torso from the at least one 3D image to create a torso segmentation;
estimating a missing portion (<NUM>) of the torso within the at least one 3D image based on the registered first and second segmentations and the torso segmentation;
estimating (<NUM>) a complete torso volume using the at least one 3D image, based on the estimation of the missing portion; and
estimating (<NUM>) a fetus weight based on the estimated complete torso volume.