Method and system for automatically analyzing placental insufficiency in slices of ultrasound images of a curved morphology

Abstract

The present invention provides a system and method for automatically analyzing placental insufficiency in a curved morphological ultrasound image slice of a placental anatomical part. The method includes: acquiring, by an ultrasound system, an ultrasound volume of the placental anatomical part, the ultrasound volume including color Doppler information. The method includes: extracting, by at least one processor of the ultrasound system, a morphological ultrasound image slice at a certain distance below the inner surface of the placental anatomical part. The morphological ultrasound image slice is curved in all three dimensions and includes the color Doppler information. The method includes: analyzing, by the at least one processor, the color Doppler information of the morphological ultrasound image slice to generate perfusion data information. The method includes: causing, by at least one processor, a display system to present the morphological ultrasound image slice together with the perfusion data information.

Description

Technical Field

[0001] Some embodiments relate to ultrasound imaging. More specifically, some embodiments relate to a method and system for automatically analyzing placental insufficiency by extracting a topographic ultrasound image slice that is curved in all three dimensions and includes color Doppler information, which is automatically analyzed to generate perfusion data information that is displayed at a display system together with the topographic slice. Background Art

[0002] Ultrasound imaging is a medical imaging technique used to image organs and soft tissues in the human body. Ultrasound imaging uses real-time, non-invasive high-frequency sound waves to produce a series of two-dimensional (2D) images and / or three-dimensional (3D) images.

[0003] In the field of obstetric ultrasound, several structures within the uterine cavity are of particular interest, such as the fetus, umbilical cord, placenta, etc. Different volume rendering modes are applied to illustrate these structures of the uterine cavity. Intrauterine growth restriction (IUGR) occurs when the fetus is not as large as expected for a specific gestational age. Placental infarction is the death of cells in part of the placenta due to interruption of its blood supply. Placental infarction can have serious effects on the fetus, such as IUGR and vascular abnormalities. Placental infarction is difficult to diagnose with ultrasound because the placenta has an irregular shape in all directions. Typically, due to the irregular shape of the placenta, multiple planar slices are extracted from the ultrasound volume to visualize the perfusion state of the placenta beneath its surface, which is time-consuming and difficult for users lacking analysis experience.

[0004] By comparing such systems with some aspects of the present disclosure set forth in the remainder of this application with reference to the accompanying drawings, further limitations and disadvantages of conventional and traditional methods will become apparent to those skilled in the art. Summary of the Invention

[0005] There is provided a system and / or method for automatically analyzing placental insufficiency in a curved topographic ultrasound image slice, substantially as shown and / or described in conjunction with at least one of the accompanying drawings and more fully set forth in the claims.

[0006] These and other advantages, aspects, and novel features of the present disclosure, as well as details of its illustrated embodiments, will be more fully understood from the following description and the drawings. Brief Description of the Drawings

[0007] Figure 1 is a block diagram of an exemplary ultrasound system according to various embodiments, the ultrasound system being operable to automatically analyze placental insufficiency in a curved topographic ultrasound image slice.

[0008] Figure 2Screenshot of an exemplary A-plane ultrasound image view of a placenta according to various embodiments, the view having a tool that can be manipulated to set the curvature of a topographical ultrasound image slice.

[0009] Figure 3 Screenshot of an exemplary B-plane ultrasound image view of a placenta according to various embodiments, the view having a tool that can be manipulated to set the curvature of a topographical ultrasound image slice.

[0010] Figure 4 Display diagram of an exemplary ultrasound slice that is curved in all three dimensions through an exemplary placenta model according to various embodiments.

[0011] Figure 5 Screenshot of an exemplary volume rendering of a curved ultrasound image slice according to various embodiments, the curved ultrasound image slice having a segmented perfusion region larger than a predetermined size threshold.

[0012] Figure 6 Screenshot of an exemplary volume rendering of a curved ultrasound image slice with a grid, each grid cell including a blood flow velocity measurement result.

[0013] Figure 7 Screenshot of an exemplary volume rendering of a curved ultrasound image slice with a grid, each grid cell including a perfusion area ratio measurement result, wherein grid cells with perfusion area ratio measurement results below a predetermined threshold are highlighted, and wherein a group of spatially adjacent highlighted cells are identified.

[0014] Figure 8 Flowchart showing exemplary steps for automatically analyzing placental insufficiency in a curved topographical ultrasound image slice according to various embodiments. Detailed Description

[0015] Certain embodiments are found in a method and system for automatically analyzing placental insufficiency in a curved topographical ultrasound image slice. Aspects of the present disclosure have the following technical effects: automatically extracting a topographical ultrasound image slice that maintains a certain distance below an anatomical structure such as the inner surface of the placenta. Various embodiments have the following technical effects: providing a tool that can be manipulated to manually define the curvature of the topographical ultrasound image slice to be extracted. Certain embodiments have the following technical effects: providing a volume rendering of a topographical ultrasound image slice that is curved in all three dimensions. Aspects of the present disclosure have the following technical effects: automatically generating perfusion data information by analyzing the color Doppler information of the curved topographical ultrasound image slice. Certain embodiments have the following technical effects: presenting the curved topographical ultrasound image slice together with the automatically generated perfusion data information.

[0016] The foregoing Summary of the Invention and the following Detailed Description of certain embodiments will be better understood when read in conjunction with the accompanying drawings. To the extent that the figures in the drawings illustrate diagrams of functional blocks of various embodiments, these functional blocks do not necessarily represent divisions between hardware circuits. Thus, for example, one or more functional blocks (e.g., a processor or a memory) may be implemented in a single piece of hardware (e.g., a general-purpose signal processor or a random access memory block, a hard disk, etc.) or in multiple pieces of hardware. Similarly, a program may be an independent program, may be included as a subroutine in an operating system, may be a function in an installed software package, etc. It should be understood that the various embodiments are not limited to the arrangements and instrumentalities shown in the drawings. It should also be understood that embodiments may be combined, or other embodiments may be utilized, and structural, logical, and electrical changes may be made without departing from the scope of the various embodiments. Accordingly, the following detailed description should not be taken in a limiting sense, and the scope of the present disclosure is defined by the appended claims and their equivalents.

[0017] As used herein, an element or step recited in the singular and preceded by the word "a" or "an" should be understood as not excluding a plurality of the recited elements or steps, unless expressly stated to the contrary. Further, references to "exemplary embodiments", "various embodiments", "certain embodiments", "representative embodiments", etc. are not to be construed as precluding the existence of additional embodiments that also incorporate the recited features. Moreover, unless expressly stated to the contrary, an embodiment that "comprises", "includes", or "has" one or more elements having a particular property may include additional elements that do not have that property.

[0018] Additionally, as used herein, the term "image" broadly refers to both visual images and data representing visual images. However, many embodiments generate (or are configured to generate) at least one visual image. Further, as used herein, the phrase "image" is used to refer to ultrasound modes such as B-mode (2D mode), M-mode, three-dimensional (3D) mode, CF mode, PW Doppler, CW Doppler, contrast-enhanced ultrasound (CEUS), and / or sub-modes of B-mode and / or CF such as harmonic imaging, shear wave elastography (SWEI), strain elastography, TVI, PDI, B-flow, MVI, UGAP, and in some cases also MM, CM, TVD, where the "image" and / or "plane" includes a single beam or multiple beams.

[0019] Moreover, as used herein, the term processor or processing unit refers to any type of processing unit that can perform the required computations needed for the various embodiments, such as a single-core or multi-core: CPU, accelerated processing unit (APU), graphics processing unit (GPU), DSP, FPGA, ASIC, or combinations thereof.

[0020] It should be noted that the various embodiments described herein for generating or forming an image may include a process for forming an image, which in some embodiments includes beamforming and in other embodiments does not include beamforming. For example, an image may be formed without beamforming, such as by multiplying a matrix of demodulated data by a coefficient matrix such that the product is an image and wherein the process does not form any "beams". Additionally, the formation of an image may be performed using channel combinations (e.g., synthetic aperture techniques) that may originate from more than one transmit event.

[0021] In various embodiments, for example, ultrasonic processing is performed in software, firmware, hardware, or a combination thereof to form an image, including ultrasonic beamforming, such as receive beamforming. A specific implementation of an ultrasonic system having a software beamformer architecture formed in accordance with various embodiments is shown in Figure 1 below.

[0022] Figure 1 is a block diagram of an exemplary ultrasonic system 100 according to various embodiments, which is operable to automatically analyze placental insufficiency in a curved morphology ultrasonic image slice. Referring to Figure 1 , the ultrasonic system 100 and the training system 200 are shown. The ultrasonic system 100 includes a transmitter 102, an ultrasonic probe 104, a transmit beamformer 110, a receiver 118, a receive beamformer 120, an A / D converter 122, an RF processor 124, an RF / IQ buffer 126, a user input device 130, a signal processor 132, an image buffer 136, a display system 134, and an archive 138.

[0023] The transmitter 102 may include suitable logic, circuitry, interfaces, and / or code operable to drive the ultrasonic probe 104. The ultrasonic probe 104 may include a two-dimensional (2D) piezoelectric element array. The ultrasonic probe 104 may include a set of transmit transducer elements 106 and a set of receive transducer elements 108 that typically constitute the same elements. In certain embodiments, the ultrasonic probe 104 may be operable to acquire ultrasonic image data that covers at least most of an anatomical structure (such as the placenta, fetus, heart, blood vessels, or any suitable anatomical structure).

[0024] The transmit beamformer 110 may include suitable logic, circuitry, interfaces, and / or code operable to control the transmitter 102, which drives the set of transmit transducer elements 106 through a transmit sub-aperture beamformer 114 to transmit ultrasonic transmit signals into an area of interest (e.g., a person, an animal, an underground cavity, a physical structure, etc.). The transmitted ultrasonic signals may be backscattered from structures (such as blood cells or tissues) in the object of interest to generate echoes. The echoes are received by the receive transducer elements 108.

[0025] This set of receiving transducer elements 108 in the ultrasound probe 104 may be operable to convert received echoes into analog signals, perform sub-aperture beamforming through the receive sub-aperture beamformer 116, and then transmit them to the receiver 118. The receiver 118 may include suitable logic, circuitry, interfaces, and / or code that is operable to receive signals from the receive sub-aperture beamformer 116. The analog signal may be transmitted to one or more of the plurality of A / D converters 122.

[0026] The plurality of A / D converters 122 may include suitable logic, circuitry, interfaces, and / or code that is operable to convert the analog signal from the receiver 118 into a corresponding digital signal. The plurality of A / D converters 122 are disposed between the receiver 118 and the RF processor 124. However, the present disclosure is not limited in this regard. Thus, in some embodiments, the plurality of A / D converters 122 may be integrated within the receiver 118.

[0027] The RF processor 124 may include suitable logic, circuitry, interfaces, and / or code that is operable to demodulate the digital signal output by the plurality of A / D converters 122. According to one embodiment, the RF processor 124 may include a complex demodulator (not shown) that is operable to demodulate the digital signal to form an I / Q data pair representative of the corresponding echo signal. The RF or I / Q signal data may then be transmitted to the RF / IQ buffer 126. The RF / IQ buffer 126 may include suitable logic, circuitry, interfaces, and / or code that is operable to provide temporary storage of the RF or I / Q signal data generated by the RF processor 124.

[0028] The receive beamformer 120 may include suitable logic, circuitry, interfaces, and / or code that is operable to perform digital beamforming processing to, for example, sum the delayed channel signals received from the RF processor 124 via the RF / IQ buffer 126 and output a beam sum signal. The resulting processed information may be the beam sum signal that is output from the receive beamformer 120 and transmitted to the signal processor 132. According to some embodiments, the receiver 118, the plurality of A / D converters 122, the RF processor 124, and the beamformer 120 may be integrated into a single beamformer, which may be digital. In various embodiments, the ultrasound system 100 includes a plurality of receive beamformers 120.

[0029] The user input device 130 can be used to input patient data, scan parameters, make settings, select protocols and / or templates, select measurement results, manipulate tools for defining the curvature of an ultrasound slice, and the like. In an exemplary embodiment, the user input device 130 can be operable to configure, manage, and / or control the operation of one or more components and / or modules in the ultrasound system 100. In this regard, the user input device 130 can be operable to configure, manage, and / or control the operation of the transmitter 102, ultrasound probe 104, transmit beamformer 110, receiver 118, receive beamformer 120, RF processor 124, RF / IQ buffer 126, user input device 130, signal processor 132, image buffer 136, display system 134, and / or archive 138. The user input device 130 can include one or more buttons, one or more rotary encoders, a touch screen, motion tracking, voice recognition, a mouse device, a keyboard, a camera, and / or any other device capable of receiving user instructions. In a particular embodiment, for example, one or more of the user input devices in the user input device 130 can be integrated into other components (such as the display system 134 or the ultrasound probe 104). As an example, the user input device 130 can include a touch screen display.

[0030] The signal processor 132 can include suitable logic, circuitry, interfaces, and / or code that can be operable to process ultrasound scan data (i.e., the summed IQ signals) to generate an ultrasound image for presentation on the display system 134. The signal processor 132 can be operable to perform one or more processing operations on the acquired ultrasound scan data according to a plurality of selectable ultrasound modalities. In an exemplary embodiment, the signal processor 132 can be used to perform display processing and / or control processing, etc. As echo signals are received, the acquired ultrasound scan data can be processed in real time during a scan session. Additionally or alternatively, the ultrasound scan data can be temporarily stored in the RF / IQ buffer 126 during a scan session and processed in a less real-time manner in an online operation or an offline operation. In various embodiments, the processed image data can be presented at the display system 134 and / or can be stored at the archive 138. The archive 138 can be a local archive, a picture archiving and communication system (PACS), or any suitable device for storing images and associated information.

[0031] The signal processor 132 can be one or more central processing units, microprocessors, and / or microcontrollers, etc. For example, the signal processor 132 can be an integrated component or can be distributed at various locations. In an exemplary embodiment, the signal processor 132 can include a slice extraction processor 140 and a perfusion analysis processor 150. The signal processor 132 may be capable of receiving input information from the user input device 130 and / or the archive 138, generating output that can be displayed by the display system 134, and manipulating the output in response to the input information from the user input device 130, etc. The signal processor 132, the slice extraction processor 140, and the perfusion analysis processor 150 may be capable of performing, for example, any method and / or instruction set discussed herein according to various embodiments.

[0032] The ultrasound system 100 can be operated to continuously acquire ultrasound scan data at a frame rate suitable for the imaging situation under consideration. Typical frame rates are in the range of 20 to 120, but can be lower or higher. The acquired ultrasound scan data can be displayed on the display system 134 at a display rate that is the same as, slower than, or faster than the frame rate. An image buffer 136 is included for storing processed frames of the acquired ultrasound scan data that are not scheduled for immediate display. Preferably, the image buffer 136 has sufficient capacity to store frame values of ultrasound scan data for at least several minutes. The frames of the ultrasound scan data are stored in a manner that is easily retrievable according to their acquisition order or time. The image buffer 136 can be embodied as any known data storage medium.

[0033] The signal processor 132 may include a slice extraction processor 140 that includes suitable logic, circuitry, interfaces, and / or code that may be operable to extract from an ultrasound volume including color Doppler information of a placental anatomical portion, a morphological slice that is curved in all three dimensions and has color Doppler information. The slice extraction processor 140 may be configured to provide tools that are presented at the display system 134 together with the ultrasound planes (e.g., A-plane, B-plane, and / or C-plane) of the volume and that are manipulable to manually select the curvature of the morphological slice to be extracted from the ultrasound volume. For example, the slice extraction processor 140 may present curvature line tools overlaid on the A-plane and B-plane of the ultrasound volume. The curvature line tools may be dragged and / or otherwise manipulated to set the morphological slice curvature. The slice extraction processor 140 may be configured to extract and draw a morphological ultrasound slice that is curved in all three dimensions based on the manipulation positioning of the curvature line tools and the defined slice thickness. Additionally and / or optionally, the slice extraction processor 140 may include image analysis algorithms, artificial intelligence algorithms, one or more deep neural networks (e.g., convolutional neural networks), and / or may utilize any suitable form of image analysis techniques or machine learning processing functions configured to identify the inner surface of the placental anatomical portion depicted in the ultrasound volume. The slice extraction processor 140 may be configured to automatically extract from the ultrasound volume a morphological ultrasound slice that is curved in all three dimensions and that is maintained at a specified distance or depth (e.g., 5 millimeters or any suitable distance / depth) below the identified inner surface of the placental anatomical portion depicted in the ultrasound volume. The slice extraction processor 140 may be configured to extract and draw a morphological ultrasound slice that is curved in all three dimensions based on the identified inner surface, the specified distance, and the defined slice thickness (e.g., between 1 millimeter and 5 millimeters). The slice extraction processor 140 may be configured to provide the drawn curved morphological ultrasound image slice to the perfusion analysis processor 150. Additionally and / or optionally, the slice extraction processor 140 may present the drawn curved morphological ultrasound image slice at the display system 134 and / or store the drawn curved morphological ultrasound image slice in the archive 138 and / or any suitable data storage medium.

[0034] The slice extraction processor 140 may include suitable logic, circuitry, interfaces, and / or code that may be operable to automatically identify the inner surface of the depicted placental anatomy portion in the ultrasound volume. In various embodiments, the slice extraction processor 140 may be provided as a deep neural network, which may consist of, for example, an input layer, an output layer, and one or more hidden layers between the input layer and the output layer. Each layer may be composed of a plurality of processing nodes, which may be referred to as neurons. For example, the slice extraction processor 140 may include an input layer having neurons for each pixel or group of pixels in the ultrasound volume from the placental anatomy portion. The output layer may have neurons corresponding to the inner surface of the placental anatomy portion and / or any suitable anatomical structure. Each neuron in each layer may perform a processing function and pass the processed ultrasound image information to one of the plurality of neurons in the downstream layer for further processing. For example, the neurons in the first layer may learn to identify the structural edges in the ultrasound image data. The neurons in the second layer may learn to identify the shape based on the detected edges from the first layer. The neurons in the third layer may learn the position of the identified shape relative to the landmarks in the ultrasound image data. The processing performed by the slice extraction processor 140 deep neural network (e.g., convolutional neural network) may highly probably identify the inner surface of the depicted placental anatomy portion in the acquired ultrasound volume. The distance below the inner surface of the placental anatomy portion may be a default distance and / or a distance selectable by the ultrasound operator via the user input device 130. The thickness of the curved topography ultrasound image slice to be extracted may be a default thickness and / or a thickness selectable by the ultrasound operator via the user input device 130. In various embodiments, the thickness may be in the selectable range of 1 millimeter to 5 millimeters. The slice extraction processor 140 may be configured to extract and draw a curved topography ultrasound slice in all three dimensions based on the identified inner surface, the specified distance, and the defined slice thickness.

[0035] Figure 2 is a screenshot 300 of an exemplary A-plane ultrasound image view 310 of a placenta according to various embodiments, the view having a tool 320 that can be manipulated to set the curvature of the topography ultrasound image slice. Figure 3 is a screenshot of an exemplary B-plane ultrasound image view 410 of a placenta according to various embodiments, the view having a tool 420 that can be manipulated to set the curvature of the topography ultrasound image slice. Refer to Figure 2 and Figure 3, showing screenshots 300, 400 of an exemplary A-plane ultrasound image view 310 and a B-plane ultrasound image view 410. The A-plane ultrasound image view 310 and the B-plane ultrasound image view 410 are overlaid with manipulable tools 320, 420 configured to select the curvature of the morphological ultrasound slice to be extracted. The tools 320, 420 can be lines overlaid on the image views 310, 410 that can be selected and dragged to change the position of the tools 320, 420 and the curvature of the tools 320, 420. For example, an ultrasound operator can operate the user input device 130 to select points along the tools 320, 420 and drag the tools 320, 420 to adjust the curvature of the tools 320, 420. The curvature of the manipulable tool 320 in the A-plane ultrasound image view 310 and the curvature of the manipulable tool 420 in the B-plane ultrasound image view 410 can be combined by the slice extraction processor 140 to determine the curvature of the morphological ultrasound image slice to be extracted in all three dimensions. The A-plane ultrasound image view 310 and the B-plane ultrasound image view 410 may also include indication information of the thicknesses 330, 430 of the morphological ultrasound image slices to be extracted. The slice thickness can be determined based on the thickness of the placenta volume. The slice thickness can be a default thickness, a user-selected thickness, and / or a slice thickness automatically determined by the slice extraction processor 140. For example, the slice extraction processor 140 can be configured to automatically set the slice thickness based on the thickness of the placenta volume such that only placental tissue is covered in the extracted slice. The indication information of the slice thicknesses 330, 430 can be selected or updated by the ultrasound operator, via the user input device, by moving the indication information 330, 430, entering an updated slice thickness, or based on any suitable user selection. The indication information of the slice thicknesses 330, 430 is configured to move synchronously with the change of the manipulable tools 320, 420.

[0036] Figure 4 is a display diagram 500 of an exemplary ultrasound slice 510 curved in all three dimensions through an exemplary model of the placenta 520. Refer to Figure 4, showing a model of an exemplary placenta 520 having an ultrasound slice 520 that is curved in all three dimensions through the placenta model 510. The curvature of the ultrasound slice 520 can be automatically selected by the slice extraction processor 140 based on the identification of the inner surface of the placenta, a specific distance below the inner surface of the placenta, and the defined slice thickness. The defined slice thickness can be a default thickness, a user-selected thickness, and / or a slice thickness automatically determined by the slice extraction processor 140. For example, the slice extraction processor 140 can be configured to automatically set the slice thickness based on the thickness of the volume model of the placenta 520 such that only placental tissue is encompassed within the extracted curved ultrasound image slice 510. Additionally and / or optionally, the curvature of the ultrasound slice 520 can be manually selected by the ultrasound operator via the user input device 130 by interacting with the manipulable tools 320, 420 as shown in Figure 2 and Figure 3 .

[0037] Referring again to Figure 1 , the signal processor 132 can include a perfusion analysis processor 150 that includes suitable logic, circuitry, interfaces, and / or code that may be operable to analyze the color Doppler information of the morphological ultrasound image slice to generate perfusion data information. The perfusion data information can include: segmented perfusion regions larger than a predetermined size threshold; an indication of whether the count number of segmented perfusion regions exceeds a predetermined minimum; blood flow velocity measurements (e.g., peak absolute velocity, mean absolute velocity, ratio of peak positive velocity to peak negative velocity, and ratio of mean positive velocity to mean negative velocity, etc.) in the grid cells of a grid overlaid on the morphological ultrasound image slice; perfusion area ratio measurements (e.g., ratio of perfusion area to non-perfusion area) in the grid cells of a grid overlaid on the morphological ultrasound image slice; highlighting of low perfusion regions; and / or any suitable indication information of low perfusion regions in the morphological ultrasound image slice.

[0038] The perfusion analysis processor 150 may include image analysis algorithms, artificial intelligence algorithms, one or more deep neural networks (e.g., convolutional neural networks), and / or may utilize any suitable form of image analysis techniques or machine learning processing functions configured to segment perfusion regions in topographic ultrasound image slices. In various embodiments, the perfusion analysis processor 150 may be configured to segment only perfusion regions larger than a predetermined size threshold. In an exemplary embodiment, the perfusion analysis processor 150 may be provided as a deep neural network, which may be composed of, for example, an input layer, an output layer, and one or more hidden layers between the input layer and the output layer. Each layer may be composed of a plurality of processing nodes, which may be referred to as neurons. For example, the perfusion analysis processor 150 may include an input layer having neurons for each pixel or group of pixels in a topographic ultrasound image slice from a placental anatomical portion with color Doppler information. The output layer may have neurons corresponding to the segmented perfusion regions of the placental anatomical portion. Each neuron in each layer may perform a processing function and transfer the processed ultrasound image information to one of the plurality of neurons in the downstream layer for further processing. For example, the neurons in the first layer may learn to identify structural edges in the ultrasound image data. The neurons in the second layer may learn to identify shapes based on the detected edges from the first layer. The neurons in the third layer may learn the position of the identified shapes relative to landmarks in the ultrasound image data. The processing performed by the perfusion analysis processor 150 deep neural network (e.g., convolutional neural network) may segment the perfusion regions of the placental anatomical portion depicted in the topographic ultrasound image slice with a high probability.

[0039] The perfusion analysis processor 150 may be configured to cause the display system 134 to present a rendering of a curved topographic ultrasound image slice with segmented perfusion regions. In various embodiments, the segmented perfusion regions may include only perfusion regions larger than a predetermined size threshold. The segmented perfusion regions may be shown by depicting a contour, highlighting, and / or any suitable indication information for the segmented perfusion regions. In various embodiments, the perfusion analysis processor 150 may be configured to count the segmented perfusion regions presented on the curved topographic ultrasound image slice. The perfusion analysis processor 150 may be configured to provide an alert if the counted number of segmented perfusion regions does not exceed a predetermined minimum number. For example, the alert may be an icon, a text message, and / or any suitable indication information of low perfusion in the curved topographic ultrasound image slice. The perfusion analysis processor 150 may store the rendered curved topographic ultrasound image slice with segmented perfusion regions in the archive 138 and / or any suitable data storage medium.

[0040] Figure 5Screenshot 600 of an exemplary volume rendering of a curved ultrasound image slice 610 in which a segmented perfusion region 620 is greater than a predetermined size threshold, according to various embodiments. Refer to Figure 5 , screenshot 600 includes a volume rendering of curved ultrasound image slice 610 having color Doppler information representing perfusion region 620. The rendering of curved ultrasound image slice 610 includes segmented perfusion region 620 that is greater than a predetermined size threshold. The rendering of curved ultrasound image slice 610 is shown with a contour line of segmented perfusion region 620. In various embodiments, segmented perfusion region 620 may be identified by delineating a contour, highlighting, and / or any suitable indication information. In an exemplary embodiment, perfusion analysis processor 150 may be configured to count segmented perfusion regions 620 presented on curved morphology ultrasound image slice 610. Perfusion analysis processor 150 may be configured to provide an alert if the counted number of segmented perfusion regions 620 does not exceed a predetermined minimum number such as ten (10) or any suitable minimum number. For example, screenshot 600 of the volume rendering of curved ultrasound image slice 610 includes eleven (11) segmented perfusion regions 620, which is greater than the predetermined minimum number of ten (10) segmented perfusion regions 620. However, if the rendering of curved ultrasound image slice 610 includes fewer than ten (10) segmented perfusion regions 620, perfusion analysis processor 150 may be configured to provide an alert such as an icon, a text message, and / or any suitable indication of low perfusion in curved morphology ultrasound image slice 610.

[0041] Refer again to Figure 1 , perfusion analysis processor 150 may be configured to cause display system 134 to present a rendering of a curved morphology ultrasound image slice having a grid overlaid on the rendered curved morphology ultrasound image slice. Perfusion analysis processor 150 may be configured to segment perfusion regions in the rendered curved morphology ultrasound image slice and automatically analyze color Doppler information associated with the segmented perfusion regions in the rendered curved morphology ultrasound image slice to provide a blood flow velocity measurement for each cell in the grid. The blood flow velocity measurement may be a peak absolute velocity, an average absolute velocity, a ratio of peak positive velocity to peak negative velocity, a ratio of average positive velocity to average negative velocity, and / or any suitable blood flow velocity measurement. Perfusion analysis processor 150 may store the rendered curved morphology ultrasound image slice having the grid and the blood flow velocity measurement associated with each grid cell in archive 138 and / or any suitable data storage medium.

[0042] Figure 6Screenshot 700 of an exemplary volume rendering of a curved ultrasound image slice 710 with a grid 730, where each grid cell includes a blood flow velocity measurement result 740. Refer to Figure 6 , screenshot 700 of the curved topography ultrasound image slice 710 has color Doppler information representing a perfusion region 720. The curved topography ultrasound image slice 710 overlaps with the grid 730 that forms grid cells. The perfusion analysis processor 150 can be configured to segment the perfusion region 720 and perform a blood flow velocity measurement 740 on the perfusion region 720 in each grid cell based on the color Doppler information. The perfusion analysis processor 150 can be configured to present the blood flow velocity measurement result 740 for each grid cell in the corresponding grid cell. For example, as Figure 6 shown, the peak absolute velocity measurement result 740 is presented in each grid cell of the grid 730, which is superimposed on the rendering of the curved ultrasound image slice 710. In various embodiments, the blood flow velocity measurement result can additionally and / or optionally include the average absolute velocity, the ratio of the peak positive velocity to the peak negative velocity, the ratio of the average positive velocity to the average negative velocity, and / or any suitable blood flow velocity measurement result.

[0043] Refer again to Figure 1, the perfusion analysis processor 150 can be configured to cause the display system 134 to present a rendering of a curved morphology ultrasound image slice having a grid overlaid on the rendered curved morphology ultrasound image slice. The perfusion analysis processor 150 can be configured to segment perfusion regions in the rendered curved morphology ultrasound image slice and automatically analyze color Doppler information associated with the segmented perfusion regions in the rendered curved morphology ultrasound image slice to provide area ratio measurements for each cell in the grid. The area ratio measurements can be determined by calculating the percentage of grid cells that include perfusion. The perfusion analysis processor 150 can be configured to highlight grid cells having an area ratio less than an area ratio threshold. For example, the perfusion analysis processor 150 can highlight grid cells having an area ratio below 15%. The perfusion analysis processor 150 can be configured to determine overall perfusion insufficiency based on the number of highlighted grid cells. The perfusion analysis processor 150 can be configured to delineate, label, highlight, and / or otherwise provide an indicator of local dysfunction by identifying a group of spatially adjacent grid cells having an area ratio below the threshold. As an example, to provide visual indication information of a local area of low perfusion when rendering a curved morphology ultrasound image slice, the perfusion analysis processor 150 can provide a box that surrounds a group of highlighted grid cells that are spatially adjacent and have an area ratio below the threshold. The perfusion analysis processor 150 can store the rendered curved morphology ultrasound image slice having the grid, the perfusion area ratio measurements associated with each grid cell, the highlighting, and / or the low perfusion identifier at the archive 138 and / or any suitable data storage medium.

[0044] Figure 7 is a screenshot 800 of an exemplary volume rendering of a curved ultrasound image slice 810 having a grid 830, each grid cell including a perfusion area ratio measurement 840, where grid cells having a perfusion area ratio measurement 840 below a predetermined threshold are highlighted 850, and where a group of spatially adjacent highlighted cells 850 are identified 860. Refer to Figure 7 , the screenshot 800 of the curved morphology ultrasound image slice 810 has color Doppler information representing a perfusion region 820. The curved morphology ultrasound image slice 810 overlaps with a grid 830 that forms grid cells. The perfusion analysis processor 150 can be configured to segment the perfusion region 820 and perform an area ratio measurement of the perfusion region 820 in each grid cell based on the color Doppler information. The perfusion analysis processor 150 can be configured to present the area ratio measurement 840 for each grid cell in the corresponding grid cell. For example, as Figure 7As shown, the area ratio measurement result 840 is presented in each grid cell of the grid 830, which is superimposed on the drawing of the curved ultrasound image slice 810. The perfusion analysis processor 150 can be configured to highlight 850 the grid cells where the area ratio measurement result 840 is lower than an area ratio threshold, such as lower than 15%. The perfusion analysis processor 150 can be configured to identify 860 local perfusion insufficiency by delineating the contour, adding labels, highlighting, and / or otherwise identifying a group of spatially adjacent highlighted grid cells where the area ratio measurement result is lower than the threshold, so as to provide visual indication information 860 of the low perfusion local area when drawing the curved topography ultrasound image slice 810.

[0045] Referring again to Figure 1 , the display system 134 can be any device capable of communicating visual information to the user. For example, the display system 134 may include a liquid crystal display, a light emitting diode display, and / or any suitable one or more displays. The display system 134 is operable to present: the ultrasound image planes 310, 410; the manipulable tools 320, 420; the indication information of the slice thickness 330, 430; the drawn curved topography ultrasound image slices 520, 610, 710, 810 with color Doppler information in all three dimensions; the perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860; and / or any suitable information.

[0046] Archive 138 can be one or more computer-readable memories integrated with and / or communicatively coupled (e.g., via a network) to ultrasound system 100, such as an image archiving and communication system (PACS), a server, a hard disk, a floppy disk, a CD, a CD-ROM, a DVD, a compact memory, a flash memory, a random access memory, a read-only memory, an electrically erasable and programmable read-only memory, and / or any suitable memory. Archive 138 can include, for example, a database, a library, an information set, or other memory accessed by and / or in conjunction with signal processor 132. For example, archive 138 is capable of storing data temporarily or permanently. Archive 138 may be capable of storing medical image data, data generated by signal processor 132, and / or instructions readable by signal processor 132, etc. In various embodiments, archive 138 stores: an ultrasound volume; ultrasound image planes 310, 410; instructions for providing manipulable tools 320, 420; instructions for providing indication information for slice thicknesses 330, 430; instructions for extracting morphological ultrasound image slices 520, 610, 710, 810 that are curved in all three dimensions and have color Doppler information; morphological ultrasound image slices 520, 610, 710, 810 that are curved in all three dimensions and have color Doppler information; instructions for generating perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860; and / or, for example, perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860.

[0047] The components of ultrasound system 100 can be implemented in software, hardware, firmware, etc. The various components of ultrasound system 100 can be communicatively connected. The components of ultrasound system 100 can be implemented separately and / or integrated in various forms. For example, display system 134 and user input device 130 can be integrated as a touchscreen display.

[0048] Still referring to Figure 1, the training system 200 may include a training engine 210 and a training database 220. The training engine 160 may include suitable logic, circuitry, interfaces, and / or code that may be operable to train the neurons of a deep neural network (e.g., an artificial intelligence model) inferred (i.e., deployed) by the slice extraction processor 140 and / or the perfusion analysis processor 150. For example, an artificial intelligence model inferred by the slice extraction processor 140 may be trained to automatically identify the inner surface of the placental anatomical part depicted in an ultrasound volume using the database 220 of classified ultrasound volumes of placental anatomical parts. As an example, the training engine 210 may train a deep neural network deployed by the slice extraction processor 140 to automatically segment the perfusion regions in a topographical ultrasound image slice using the database 220 of classified topographical ultrasound image slices having perfusion regions.

[0049] In various embodiments, the database 220 of training images may be a Picture Archiving and Communication System (PACS) or any suitable data storage medium. In a particular embodiment, the training engine 210 and / or the training image database 220 may be a remote system communicatively coupled to the ultrasound system 100 via a wired or wireless connection, as Figure 1 shown. Additionally and / or alternatively, some or all of the components of the training system 200 may be integrated with the ultrasound system 100 in various forms.

[0050] Figure 8 is a flowchart 900 showing exemplary steps 902 to 908 that may be used to automatically analyze placental insufficiency in curved topographical ultrasound image slices 520, 610, 710, 810. Referring to Figure 8 , a flowchart 900 including exemplary steps 902 to 908 is shown. Some embodiments may omit one or more steps, and / or perform the steps in an order different from the listed order, and / or combine certain steps discussed below. For example, some steps may not be performed in a particular embodiment. Also, for example, some steps may be performed in a chronological order different from the chronological order listed below, including simultaneously.

[0051] At step 902, the ultrasound system 100 acquires an ultrasound volume including color Doppler information of a placental anatomical part. For example, the ultrasound probe 104 of the ultrasound system 100 may be operated to acquire an ultrasound volume having color Doppler information of a placental anatomical part.

[0052] At step 904, the signal processor 132 of the ultrasound system 100 extracts a morphological ultrasound image slice that is curved in all three dimensions and has color Doppler information at a certain distance below the inner surface of the placental anatomical portion. For example, the slice extraction processor 140 of the signal processor 132 can be configured to provide tools 320, 420 to be presented at the display system 134 together with the ultrasound planes 310, 410 of the volume and be manipulable to manually select the curvature of the morphological slices 520, 610, 710, 810 to be extracted from the ultrasound volume. The slice extraction processor 140 can be configured to extract and draw morphological ultrasound slices 520, 610, 710, 810 that are curved in all three dimensions based on the positioning of the manipulated tools 320, 420 and the defined slice thicknesses 330, 430. As another example, the slice extraction processor 140 can include image analysis algorithms, artificial intelligence algorithms, one or more deep neural networks (e.g., convolutional neural networks), and / or can utilize any suitable form of image analysis techniques or machine learning processing functions configured to identify the inner surface of the placental anatomical portion depicted in the ultrasound volume. The slice extraction processor 140 can be configured to automatically extract from the ultrasound volume a morphological ultrasound slice that is curved in all three dimensions and that remains a specified distance or depth (e.g., 5 millimeters or any suitable distance / depth) below the identified inner surface of the placental anatomical portion depicted in the ultrasound volume. The slice extraction processor 140 can be configured to extract and draw morphological ultrasound slices that are curved in all three dimensions based on the identified inner surface, the specified distance, and the defined slice thickness (e.g., between 1 millimeter and 5 millimeters). The slice extraction processor 140 can be configured to: present the drawn curved morphological ultrasound image slices 520, 610, 710, 810 at the display system 134; provide the drawn curved morphological ultrasound image slices 520, 610, 710, 810 to the perfusion analysis processor 150 of the signal processor 132; and / or store the drawn curved morphological ultrasound image slices 520, 610, 710, 810 in the archive 138 and / or any suitable data storage medium.

[0053] At step 906, the signal processor 132 of the ultrasound system 100 can analyze the color Doppler information of the morphological ultrasound image slices 520, 610, 710, 810 to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860. For example, the perfusion analysis processor 150 of the signal processor 132 can apply an image analysis algorithm, an artificial intelligence algorithm, one or more deep neural networks (e.g., convolutional neural network), and / or any suitable form of image analysis technology or machine learning processing function to the morphological ultrasound image slices 520, 610, 710, 810 extracted in step 904 to segment the perfusion regions 620, 720, 820 in the morphological image slices 520, 610, 710, 810. The perfusion data information generated by the perfusion analysis processor 150 can include segmented perfusion regions 620, 720, 820 that are larger than a predetermined size threshold. Additionally and / or optionally, the perfusion data information can include: an indication of whether the counted number of segmented perfusion regions exceeds a predetermined minimum; blood flow velocity measurements 740 (e.g., peak absolute velocity, mean absolute velocity, ratio of peak positive velocity to peak negative velocity, and ratio of mean positive velocity to mean negative velocity, etc.) in the grid cells of the grid 730 overlaid on the morphological ultrasound image slices 520, 610, 710, 810; perfusion area ratio measurements 840 (e.g., ratio of perfusion area to non-perfusion area) in the grid cells of the grid 830 overlaid on the morphological ultrasound image slices 520, 610, 710, 810; highlighting 850, 860 of hypoperfused regions; and / or any suitable indication information of hypoperfused regions in the morphological ultrasound image slices 520, 610, 710, 810.

[0054] At step 908, the signal processor 132 of the ultrasound system 100 can present the morphological ultrasound image slices 520, 610, 710, 810 together with the perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 at the display system 134. For example, the perfusion analysis processor 150 of the signal processor 132 can be configured to cause the display system 134 to present the morphological ultrasound image slices 520, 610, 710, 810 extracted in step 904 together with the perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 generated in step 906 at the display system 134.

[0055] Aspects of the present disclosure provide a method 900 and a system 100 for automatically analyzing placental insufficiency in curved morphological ultrasound image slices 520, 610, 710, 810. According to various embodiments, method 900 may include: acquiring 902, by an ultrasound system 100, an ultrasound volume of a placental anatomical portion, the ultrasound volume including color Doppler information. Method 900 may include: extracting 940, by at least one processor 132, 140 of the ultrasound system 100, curved morphological ultrasound image slices 520, 610, 710, 810 at a distance below the inner surface of the placental anatomical portion. The curved morphological ultrasound image slices 520, 610, 710, 810 are curved in all three dimensions and include color Doppler information. Method 900 may include: analyzing 906, by at least one processor 132, 150, the color Doppler information of the curved morphological ultrasound image slices 520, 610, 710, 810 to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860. Method 900 may include: causing 908, by at least one processor 132, 140, 150, a display system 134 to present the curved morphological ultrasound image slices 520, 610, 710, 810 together with the perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860.

[0056] In an exemplary embodiment, extracting 904 the topographic ultrasound image slices 520, 610, 710, 810 includes: receiving user input for manually selecting the curvature of the topographic ultrasound image slices 520, 610, 710, 810. Receiving the user input includes: presenting, by at least one processor 132, 140, manipulable tools 320, 420 that overlay each of the plurality of ultrasound image planes 310, 410. Receiving the user input includes: receiving, by at least one processor 132, 140, manipulation of the manipulable tools 320, 420 to select the curvature of the topographic ultrasound image slices 520, 610, 710, 810. In a representative embodiment, extracting 904 the topographic ultrasound image slices 520, 610, 710, 810 includes: automatically identifying, by at least one processor 132, 140, the inner surface of the placental anatomical part. Extracting 904 the topographic ultrasound image slices 520, 610, 710, 810 includes: applying, by at least one processor 132, 140, a defined distance from the inner surface of the placental anatomical part. Extracting 904 the topographic ultrasound image slices 520, 610, 710, 810 includes: applying, by at least one processor 132, 140, the defined slice thicknesses 330, 430. In various embodiments, analyzing 906 the color Doppler information of the topographic ultrasound image slices 520, 610, 710, 810 to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 includes: segmenting, by at least one processor 132, 150, perfusion regions 620 that are larger than a predetermined size threshold. Analyzing 906 the color Doppler information of the topographic ultrasound image slices 520, 610, 710, 810 to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 includes: counting, by at least one processor 132, 150, the number of segmented perfusion regions 620. Analyzing 906 the color Doppler information of the topographic ultrasound image slices 520, 610, 710, 810 to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 includes: providing an alert, by at least one processor 132, 150, when the number of segmented perfusion regions 620 is less than a predetermined minimum perfusion region threshold. In certain embodiments, analyzing 906 the color Doppler information of the topographic ultrasound image slices 520, 610, 710, 810 to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 includes: dividing, by at least one processor 132, 150, the topographic ultrasound image slices 520, 610, 710, 810 into grids 730, 830.Analyzing the color Doppler information of the morphological ultrasound image slices 520, 610, 710, 810 to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 includes: determining, by at least one processor 132, 150, the perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 for each grid cell of the grids 730, 830. In an exemplary embodiment, the perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 includes a blood flow measurement result 740, which is one or more of the following: peak absolute velocity, average absolute velocity, ratio of peak positive velocity to peak negative velocity, or ratio of average positive velocity to average negative velocity. In a representative embodiment, the perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 includes a perfusion area ratio measurement result 840. The method 900 may include one or both of the following: highlighting, by at least one processor 132, 150, each grid cell having a perfusion area ratio measurement result 840 below a predetermined perfusion area ratio measurement threshold; or, identifying, by at least one processor 132, 150, at least one set of spatially adjacent grid cells. Each of the spatially adjacent grid cells may include a perfusion area ratio measurement result 840 below a predetermined perfusion area ratio measurement threshold.

[0057] Various embodiments provide a system 100 for automatically analyzing placental insufficiency in curved morphological ultrasound image slices 520, 610, 710, 810. The system 100 may include an ultrasound system 100, at least one processor 132, 140, 150, and a display system 134. The ultrasound system 100 may be configured to acquire an ultrasound volume of a placental anatomical portion, the ultrasound volume including color Doppler information. At least one processor 132, 140 may be configured to extract the morphological ultrasound image slices 520, 610, 710, 810 at a distance below the inner surface of the placental anatomical portion. The morphological ultrasound image slices 520, 610, 710, 810 are curved in all three dimensions and include color Doppler information. At least one processor 132, 150 may be configured to analyze the color Doppler information of the morphological ultrasound image slices 520, 610, 710, 810 to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860. The display system 134 may be configured to present the morphological ultrasound image slices 520, 610, 710, 810 together with the perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860.

[0058] In representative embodiments, at least one of processors 132, 140 is configured to extract topographic ultrasound image slices 520, 610, 710, 810 by receiving user input manually selecting the curvature of the topographic ultrasound image slices 520, 610, 710, 810. At least one of processors 132, 140 is configured to present manipulable tools 320, 420 that overlay each of the plurality of ultrasound image planes 310, 410. At least one of processors 132, 140 is configured to receive manipulation of the manipulable tools 320, 420 to select the curvature of the topographic ultrasound image slices 520, 610, 710, 810. In various embodiments, at least one of processors 132, 140 is configured to extract topographic ultrasound image slices 520, 610, 710, 810 by automatically identifying the inner surface of the placental anatomical portion. At least one of processors 132, 140 is configured to extract topographic ultrasound image slices 520, 610, 710, 810 by applying a defined distance from the inner surface of the placental anatomical portion. At least one of processors 132, 140 is configured to extract topographic ultrasound image slices 520, 610, 710, 810 by applying the defined slice thicknesses 330, 430. In certain embodiments, at least one of processors 132, 150 is configured to analyze the color Doppler information of the topographic ultrasound image slices 520, 610, 710, 810 by segmenting perfusion regions 620, 720, 820 that are larger than a predetermined size threshold to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860. At least one of processors 132, 150 is configured to analyze the color Doppler information of the topographic ultrasound image slices 520, 610, 710, 810 by counting the number of segmented perfusion regions 620, 720, 820 to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860. At least one of processors 132, 150 is configured to analyze the color Doppler information of the topographic ultrasound image slices 520, 610, 710, 810 by providing an alert when the number of segmented perfusion regions 620, 720, 820 is below a predetermined minimum perfusion region threshold to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860. In an exemplary embodiment, at least one of processors 132, 150 is configured to analyze the color Doppler information of the topographic ultrasound image slices 520, 610, 710, 810 by dividing the topographic ultrasound image slices 520, 610, 710, 810 into grids 730, 830 to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860.At least one of processors 132, 150 is configured to analyze the color Doppler information of the morphological ultrasound image slices 520, 610, 710, 810 by determining perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 for each grid cell of the grids 730, 830 to generate the perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860. In a representative embodiment, the perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 includes a blood flow measurement 740 that is one or more of the following: peak absolute velocity 740, mean absolute velocity, ratio of peak positive velocity to peak negative velocity, or ratio of mean positive velocity to mean negative velocity. In various embodiments, the perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 includes a perfusion area ratio measurement 840. At least one of processors 132, 150 is configured to do one or both of the following: highlight 850 each grid cell having a perfusion area ratio measurement 840 below a predetermined perfusion area ratio measurement threshold, or identify 860 at least one group of spatially adjacent grid cells. Each of the spatially adjacent grid cells in the group of spatially adjacent grid cells has a perfusion area ratio measurement 840 below a predetermined perfusion area ratio measurement threshold.

[0059] Certain embodiments provide a non-transitory computer-readable medium having stored thereon a computer program having at least one code segment. The at least one code segment is executable by a machine to cause the ultrasound system 100 to perform step 900. Step 900 may include: receiving 902 an ultrasound volume of a placental anatomical portion, the ultrasound volume including color Doppler information. Step 900 may include: extracting 904 morphological ultrasound image slices 520, 610, 710, 810 at a distance below the inner surface of the placental anatomical portion. The morphological ultrasound image slices 520, 610, 710, 810 are curved in all three dimensions and include color Doppler information. Step 900 may include: analyzing 906 the color Doppler information of the morphological ultrasound image slices 520, 610, 710, 810 to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860. Step 900 may include: causing the display system 134 to present 908 the morphological ultrasound image slices 520, 610, 710, 810 together with the perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860.

[0060] In various embodiments, extracting 904 topographic ultrasound image slices 520, 610, 710, 810 includes: receiving user input for manually selecting the curvature of the topographic ultrasound image slices 520, 610, 710, 810. Receiving the user input includes: presenting manipulable tools 320, 420 that are overlaid on each of a plurality of ultrasound image planes 310, 410. Receiving the user input includes: receiving manipulation of the manipulable tools 320, 420 to select the curvature of the topographic ultrasound image slices 520, 610, 710, 810. In some embodiments, extracting 904 topographic ultrasound image slices 520, 610, 710, 810 includes: automatically identifying the inner surface of the placental anatomical portion. Extracting 904 topographic ultrasound image slices 520, 610, 710, 810 includes: applying a defined distance from the inner surface of the placental anatomical portion. Extracting 904 topographic ultrasound image slices 520, 610, 710, 810 includes: applying the defined slice thicknesses 330, 430. In an exemplary embodiment, analyzing 906 the color Doppler information of the topographic ultrasound image slices 520, 610, 710, 810 to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 includes: segmenting perfusion regions 620, 720, 820 that are larger than a predetermined size threshold. Analyzing 906 the color Doppler information of the topographic ultrasound image slices 520, 610, 710, 810 to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 includes: counting the number of segmented perfusion regions 620, 720, 820. Analyzing 906 the color Doppler information of the topographic ultrasound image slices 520, 610, 710, 810 to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 includes: providing an alert when the number of segmented perfusion regions 620, 720, 820 is less than a predetermined minimum perfusion region threshold. In a representative embodiment, analyzing 906 the color Doppler information of the topographic ultrasound image slices 520, 610, 710, 810 to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 includes: dividing the topographic ultrasound image slices 520, 610, 710, 810 into grids 730, 830. Analyzing 906 the color Doppler information of the topographic ultrasound image slices 520, 610, 710, 810 to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 includes: determining perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 for each grid cell of the grids 730, 830.Perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 includes blood flow measurement result 740, which is one or more of the following: peak absolute velocity 740, mean absolute velocity, ratio of peak positive velocity to peak negative velocity, or ratio of mean positive velocity to mean negative velocity. In various embodiments, analyzing 906 the color Doppler information of topographic ultrasound image slices 520, 610, 710, 810 to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 includes: dividing topographic ultrasound image slices 520, 610, 710, 810 into grids 730, 830. Analyzing 906 the color Doppler information of topographic ultrasound image slices 520, 610, 710, 810 to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 includes: determining perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 for each grid cell of grids 730, 830. The perfusion data information includes perfusion area ratio measurement result 840. Analyzing 906 the color Doppler information of topographic ultrasound image slices 520, 610, 710, 810 to generate perfusion data information 620, 720, 730, 740, 820, 830, 840, 850, 860 includes one or both of the following: highlighting 850 each grid cell whose perfusion area ratio measurement result 840 is below a predetermined perfusion area ratio measurement threshold, or identifying 860 at least one set of spatially adjacent grid cells. Each of the spatially adjacent grid cells 860 in the spatially adjacent grid cells includes a perfusion area ratio measurement result 840 that is below a predetermined perfusion area ratio measurement threshold.

[0061] As used herein, the term "circuitry" refers to physical electronic components (i.e., hardware) and any software and / or firmware ("code") that is configurable hardware, executed by hardware, and / or otherwise associated with the hardware. For example, as used herein, a particular processor and memory can include a first "circuitry" when executing one or more first codes, and a particular processor and memory can include a second "circuitry" when executing one or more second codes. As used herein, "and / or" means any one or more of the items in a list joined by "and / or". For example, "x and / or y" means any element in the three-element set {(x), (y), (x, y)}. As another example, "x, y, and / or z" means any element in the seven-element set {(x), (y), (z), (x, y), (x, z), (y, z), (x, y, z)}. As used herein, the term "exemplary" means serving as a non-limiting example, instance, or illustration. As used herein, the terms "e.g." and "for example" introduce a list of one or more non-limiting examples, instances, or illustrations. As used herein, circuitry "is operable to" and / or "is configured to" perform a function whenever the circuitry includes the necessary hardware and code (if needed) to perform the function, regardless of whether the performance of the function is disabled or not enabled by some user-configurable setting.

[0062] Other embodiments may provide a computer-readable device and / or a non-transitory computer-readable medium, and / or a machine-readable device and / or a non-transitory machine-readable medium, on which machine code and / or a computer program having at least one code segment executable by a machine and / or a computer are stored, such that the machine and / or the computer perform the steps for automatically analyzing placental insufficiency in a slice of a flexural topography ultrasound image as described herein.

[0063] Accordingly, the present disclosure may be implemented in hardware, software, or a combination of hardware and software. The present disclosure may be implemented in a centralized manner in at least one computer system or in a distributed manner in which different elements are distributed on several interconnected computer systems. Any kind of computer system or other device suitable for performing the methods described herein is appropriate.

[0064] Various embodiments may also be embedded in a computer program product that includes all the features enabling the methods described herein and is capable of performing these methods when loaded into a computer system. A computer program as used herein means any expression, in any language, code, or notation, of a set of instructions intended to cause a system having information processing capabilities to perform a particular function either directly or after performing either or both of the following: a) conversion to another language, code, or notation; and b) reproduction in a different material form.

[0065] Although the present disclosure has been described with reference to certain embodiments, those skilled in the art should understand that various changes can be made and equivalents can be substituted without departing from the scope of the present disclosure. Additionally, many modifications can be made to adapt a particular situation or material to the teachings of the present disclosure without departing from its scope. Therefore, the present disclosure is not intended to be limited to the particular embodiments disclosed, but the present disclosure will include all embodiments falling within the scope of the appended claims.

Claims

1. An imaging method, the imaging method comprising: Acquiring, by an ultrasound system, an ultrasound volume of an anatomical portion of a placenta, the ultrasound volume including color Doppler information; Extracting, by at least one processor of the ultrasound system, a morphological ultrasound image slice at a distance below an inner surface of the anatomical portion of the placenta, wherein the morphological ultrasound image slice is curved in all three dimensions and includes the color Doppler information; Analyzing, by the at least one processor, the color Doppler information of the morphological ultrasound image slice to generate perfusion data information; And Causing, by the at least one processor, a display system to present the morphological ultrasound image slice together with the perfusion data information.

2. The method according to claim 1, wherein: The extracting the morphological ultrasound image slice includes: receiving user input for manually selecting a curvature of the morphological ultrasound image slice; and The receiving the user input includes: Presenting, by the at least one processor, a manipulable tool that is overlaid on each of a plurality of ultrasound image planes; and Receiving, by the at least one processor, manipulation of the manipulable tool to select the curvature of the morphological ultrasound image slice.

3. The method according to claim 1, wherein the extracting the morphological ultrasound image slice includes: Automatically identifying, by the at least one processor, the inner surface of the anatomical portion of the placenta; Applying, by the at least one processor, a defined distance from the inner surface of the anatomical portion of the placenta; And Applying, by the at least one processor, a defined slice thickness.

4. The method according to claim 1, wherein the analyzing the color Doppler information of the morphological ultrasound image slice to generate perfusion data information includes: Segmenting, by the at least one processor, perfusion regions greater than a predetermined size threshold; Counting, by the at least one processor, the number of the segmented perfusion regions; And Providing, by the at least one processor, an alert when the number of the segmented perfusion regions is less than a predetermined minimum perfusion region threshold.

5. The method according to claim 1, wherein the analyzing the color Doppler information of the morphological ultrasound image slice to generate perfusion data information includes: Dividing, by the at least one processor, the morphological ultrasound image slice into a grid; And Determining, by the at least one processor, the perfusion data information for each grid cell of the grid.

6. The method according to claim 5, wherein the perfusion data information includes a blood flow measurement result, and the blood flow measurement result is one or more of the following: Peak absolute velocity, Average absolute velocity, Ratio of peak positive velocity to peak negative velocity, or Ratio of average positive velocity to average negative velocity.

7. The method according to claim 5, wherein the perfusion data information includes a perfusion area ratio measurement result, and includes one or both of the following: Highlighting, by the at least one processor, each grid cell for which the perfusion area ratio measurement result is below a predetermined perfusion area ratio measurement threshold, or At least one set of spatially adjacent grid cells is identified by the at least one processor, wherein each of the spatially adjacent grid cells of the spatially adjacent grid cells includes the perfusion area ratio measurement result below the predetermined perfusion area ratio measurement threshold.

8. An ultrasound system, the ultrasound system including at least one processor; wherein the ultrasound system is configured to acquire an ultrasound volume of a placental anatomical portion, the ultrasound volume including color Doppler information; the at least one processor is configured to: Extract a slice of the morphological ultrasound image at a certain distance below the inner surface of the anatomical portion of the placenta, wherein the slice of the morphological ultrasound image is curved in all three dimensions and includes the color Doppler information; and analyze the color Doppler information of the morphological ultrasound image slice to generate perfusion data information; and a display system configured to present the morphological ultrasound image slice together with the perfusion data information.

9. The system according to claim 8, wherein: the at least one processor is configured to extract the morphological ultrasound image slice by receiving a user input manually selecting the curvature of the morphological ultrasound image slice; and the at least one processor is configured to: present a manipulable tool that covers each of a plurality of ultrasound image planes; and receive manipulation of the manipulable tool to select the curvature of the morphological ultrasound image slice.

10. The system according to claim 8, wherein the at least one processor is configured to extract the morphological ultrasound image slice by the following steps: automatically identify the inner surface of the placental anatomical portion; apply a defined distance from the inner surface of the placental anatomical portion; and apply a defined slice thickness.

11. The system according to claim 8, wherein the at least one processor is configured to analyze the color Doppler information of the morphological ultrasound image slice to generate perfusion data information by the following steps: segment perfusion regions larger than a predetermined size threshold; count the number of the segmented perfusion regions; and provide an alarm when the number of the segmented perfusion regions is less than a predetermined minimum perfusion region threshold.

12. The system according to claim 8, wherein the at least one processor is configured to analyze the color Doppler information of the morphological ultrasound image slice to generate perfusion data information by the following steps: divide the morphological ultrasound image slice into a grid; and determine the perfusion data information for each grid cell of the grid.

13. The system according to claim 12, wherein the perfusion data information includes a blood flow measurement result, and the blood flow measurement result is one or more of the following: peak absolute velocity, average absolute velocity, ratio of peak positive velocity to peak negative velocity, or ratio of average positive velocity to average negative velocity.

14. The system according to claim 12, wherein the perfusion data information includes a perfusion area ratio measurement result, and wherein the at least one processor is configured to one or both of the following: highlight each grid cell with a perfusion area ratio measurement result below a predetermined perfusion area ratio measurement threshold, or Identify at least one set of spatially adjacent grid cells, where each of the spatially adjacent grid cells in the set of spatially adjacent grid cells includes a measurement result of the perfusion area ratio that is below the predetermined perfusion area ratio measurement threshold.

15. A non-transitory computer-readable medium having stored thereon a computer program, the computer program having at least one code segment that is executable by a machine to cause an ultrasound system to perform steps including: Receive an ultrasound volume of a placental anatomical portion, the ultrasound volume including color Doppler information; Extract a morphological ultrasound image slice at a distance below the inner surface of the placental anatomical portion, where the morphological ultrasound image slice is curved in all three dimensions and includes the color Doppler information; Analyze the color Doppler information of the morphological ultrasound image slice to generate perfusion data information; And Cause a display system to present the morphological ultrasound image slice together with the perfusion data information.

16. The non-transitory computer-readable medium according to claim 15, wherein: The extracting the morphological ultrasound image slice includes: receiving user input manually selecting the curvature of the morphological ultrasound image slice; and The receiving the user input includes: Presenting a manipulable tool that overlays each of a plurality of ultrasound image planes; and Receiving manipulation of the manipulable tool to select the curvature of the morphological ultrasound image slice.

17. The non-transitory computer-readable medium according to claim 15, wherein the extracting the morphological ultrasound image slice includes: Automatically identifying the inner surface of the placental anatomical portion; Applying a defined distance from the inner surface of the placental anatomical portion; And Applying a defined slice thickness.

18. The non-transitory computer-readable medium according to claim 15, wherein the analyzing the color Doppler information of the morphological ultrasound image slice to generate perfusion data information includes: Segmenting perfusion regions larger than a predetermined size threshold; Counting the number of the segmented perfusion regions; And Providing an alert when the number of the segmented perfusion regions is less than a predetermined minimum perfusion region threshold.

19. The non-transitory computer-readable medium according to claim 15, wherein the analyzing the color Doppler information of the morphological ultrasound image slice to generate perfusion data information includes: Dividing the morphological ultrasound image slice into a grid; And Determining the perfusion data information for each grid cell of the grid, where the perfusion data information includes a blood flow measurement result that is one or more of: Peak absolute velocity, Average absolute velocity, Ratio of peak positive velocity to peak negative velocity, or Ratio of average positive velocity to average negative velocity.

20. The non-transitory computer-readable medium according to claim 15, wherein the analyzing the color Doppler information of the morphological ultrasound image slice to generate perfusion data information includes: Dividing the morphological ultrasound image slice into a grid; Determine the perfusion data information for each grid cell of the grid, where the perfusion data information includes a measurement result of the perfusion area ratio; and one or both of the following: Highlight each grid cell whose measurement result of the perfusion area ratio is lower than a predetermined perfusion area ratio measurement threshold, or Identify at least one set of spatially adjacent grid cells, where each of the spatially adjacent grid cells in the set of spatially adjacent grid cells includes a measurement result of the perfusion area ratio that is lower than the predetermined perfusion area ratio measurement threshold.

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