Patent Publication Number: US-2022237798-A1

Title: Method and system for automatically estimating a hepatorenal index from ultrasound images

Description:
FIELD 
     Certain embodiments relate to ultrasound imaging. More specifically, certain embodiments relate to a method and system for automatically estimating a hepatorenal index (HRI) from ultrasound images. 
     BACKGROUND 
     Ultrasound imaging is a medical imaging technique for imaging organs and soft tissues in a human body. Ultrasound imaging uses real time, non-invasive high frequency sound waves to produce two-dimensional (2D), three-dimensional (3D), and/or four-dimensional (4D) (i.e., real-time/continuous 3D images) images. 
     Ultrasound imaging is a valuable, non-invasive tool for diagnosing various medical conditions, such as non-alcoholic fatty liver disease (NAFLD). NAFLD is one of the most common causes of liver disease in the United States with 30-40% of adults in the United States having NAFLD. Liver with steatosis (i.e., fat content of more than 5%) may be visualized in ultrasound as being brighter than the neighboring kidney. Hepatorenal index (HRI), defined as the ratio of mean sample value of the liver to renal cortex, is a biomarker used in clinical practice for steatosis grading of NAFLD and is a simple and efficient way of identifying liver steatosis. However, clinicians have high subjectivity in estimating HRI due to lack of guidelines and consensus in the community. In addition, identifying liver and kidney region for calculating HRI is subjective to clinician&#39;s skill level, size of the region, anatomical view, depth, ultrasound machine, type of input data, and selection/avoidance of certain anatomical parts. For example, a smaller region of interest (ROI) size often used by clinicians makes HRI very susceptible to variations in kidney anatomy. 
     Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of such systems with some aspects of the present disclosure as set forth in the remainder of the present application with reference to the drawings. 
     BRIEF SUMMARY 
     A system and/or method is provided for automatically estimating a hepatorenal index (HRI) from ultrasound images, substantially as shown in and/or described in connection with at least one of the figures, as set forth more completely in the claims. 
     These and other advantages, aspects and novel features of the present disclosure, as well as details of an illustrated embodiment thereof, will be more fully understood from the following description and drawings. 
    
    
     
       BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS 
         FIG. 1  is a block diagram of an exemplary ultrasound system that is operable to automatically estimate a hepatorenal index (HRI) from ultrasound images, in accordance with various embodiments. 
         FIG. 2  is an exemplary display of a Morison&#39;s pouch ultrasound image view having an anatomical segmentation of a liver and renal cortex, in accordance with various embodiments. 
         FIG. 3  is an exemplary display of the segmented Morison&#39;s pouch ultrasound image view of  FIG. 2  having identified valid samples, in accordance with various embodiments. 
         FIG. 4  is an exemplary display of the segmented Morison&#39;s pouch ultrasound image view having identified valid samples of  FIG. 3  with automatically positioned liver and renal cortex regions of interest for HRI calculation, in accordance with various embodiments. 
         FIG. 5  is a flow chart illustrating exemplary steps that may be utilized for automatically estimating an HRI from ultrasound images, in accordance with exemplary embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Certain embodiments may be found in a method and system for automatically estimating a hepatorenal index (HRI) from ultrasound images. Various embodiments have the technical effect of automatically calculating HRI from ultrasound images of Morison&#39;s pouch view. Certain embodiments have the technical effect of automatically identifying liver and kidney anatomy in the ultrasound image views. Aspects of the present disclosure provide the technical effect of identifying valid samples in the liver and kidney region that may be used for HRI calculation. Various embodiments have the technical effect of identifying an appropriate anatomical view for HRI calculation, calculating an HRI score, and providing a confidence score for each HRI calculation. Certain embodiments have the technical effect of automatically positioning a liver region of interest and a renal cortex region of interest based on identified valid samples. Aspects of the present disclosure have the technical effect of an automated HRI calculation that is objective, user independent, accurate, and optimizes the clinical protocol. 
     The foregoing summary, as well as the following detailed description of certain embodiments will be better understood when read in conjunction with the appended drawings. To the extent that the figures illustrate diagrams of the functional blocks of various embodiments, the functional blocks are not necessarily indicative of the division between hardware circuitry. Thus, for example, one or more of the functional blocks (e.g., processors or memories) may be implemented in a single piece of hardware (e.g., a general-purpose signal processor or a block of random access memory, hard disk, or the like) or multiple pieces of hardware. Similarly, the programs may be stand-alone programs, may be incorporated as subroutines in an operating system, may be functions in an installed software package, and the like. It should be understood that the various embodiments are not limited to the arrangements and instrumentality shown in the drawings. It should also be understood that the embodiments may be combined, or that other embodiments may be utilized and that structural, logical and electrical changes may be made without departing from the scope of the various embodiments. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present disclosure is defined by the appended claims and their equivalents. 
     As used herein, an element or step recited in the singular and preceded with the word “a” or “an” should be understood as not excluding plural of said elements or steps, unless such exclusion is explicitly stated. Furthermore, references to “an exemplary embodiment,” “various embodiments,” “certain embodiments,” “a representative embodiment,” and the like are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. Moreover, unless explicitly stated to the contrary, embodiments “comprising,” “including,” or “having” an element or a plurality of elements having a particular property may include additional elements not having that property. 
     Also as used herein, the term “image” broadly refers to both viewable images and data representing a viewable image. However, many embodiments generate (or are configured to generate) at least one viewable image. In addition, as used herein, the phrase “image” is used to refer to an ultrasound mode such as B-mode (2D mode), M-mode, three-dimensional (3D) mode, CF-mode, PW Doppler, CW Doppler, MGD, and/or sub-modes of B-mode and/or CF such as Shear Wave Elasticity Imaging (SWEI), TVI, Angio, B-flow, BMI, BMI_Angio, and in some cases also MM, CM, TVD where the “image” and/or “plane” includes a single beam or multiple beams. 
     Furthermore, the term processor or processing unit, as used herein, refers to any type of processing unit that can carry out the required calculations needed for the various embodiments, such as single or multi-core: CPU, Accelerated Processing Unit (APU), Graphics Board, DSP, FPGA, ASIC or a combination thereof. 
     It should be noted that various embodiments described herein that generate or form images may include processing for forming images that in some embodiments includes beamforming and in other embodiments does not include beamforming. For example, an image can be formed without beamforming, such as by multiplying the matrix of demodulated data by a matrix of coefficients so that the product is the image, and wherein the process does not form any “beams”. Also, forming of images may be performed using channel combinations that may originate from more than one transmit event (e.g., synthetic aperture techniques). 
     In various embodiments, ultrasound processing to form images is performed, for example, including ultrasound beamforming, such as receive beamforming, in software, firmware, hardware, or a combination thereof. One implementation of an ultrasound system having a software beamformer architecture formed in accordance with various embodiments is illustrated in  FIG. 1 . 
       FIG. 1  is a block diagram of an exemplary ultrasound system  100  that is operable to automatically estimate a hepatorenal index (HRI) from ultrasound images, in accordance with various embodiments. Referring to  FIG. 1 , there is shown an ultrasound system  100  and a training system  200 . The ultrasound system  100  comprises a transmitter  102 , an ultrasound probe  104 , a transmit beamformer  110 , a receiver  118 , a receive beamformer  120 , A/D converters  122 , a RF processor  124 , a 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 . 
     The transmitter  102  may comprise suitable logic, circuitry, interfaces and/or code that may be operable to drive an ultrasound probe  104 . The ultrasound probe  104  may comprise a two dimensional (2D) array of piezoelectric elements. The ultrasound probe  104  may comprise a group of transmit transducer elements  106  and a group of receive transducer elements  108 , that normally constitute the same elements. In certain embodiment, the ultrasound probe  104  may be operable to acquire ultrasound image data covering at least a substantial portion of an anatomy, such as a liver and a kidney, or any suitable anatomical structure(s). 
     The transmit beamformer  110  may comprise suitable logic, circuitry, interfaces and/or code that may be operable to control the transmitter  102  which, through a transmit sub-aperture beamformer  114 , drives the group of transmit transducer elements  106  to emit ultrasonic transmit signals into a region of interest (e.g., human, animal, underground cavity, physical structure and the like). The transmitted ultrasonic signals may be back-scattered from structures in the object of interest, like blood cells or tissue, to produce echoes. The echoes are received by the receive transducer elements  108 . 
     The group of receive transducer elements  108  in the ultrasound probe  104  may be operable to convert the received echoes into analog signals, undergo sub-aperture beamforming by a receive sub-aperture beamformer  116  and are then communicated to a receiver  118 . The receiver  118  may comprise suitable logic, circuitry, interfaces and/or code that may be operable to receive the signals from the receive sub-aperture beamformer  116 . The analog signals may be communicated to one or more of the plurality of A/D converters  122 . 
     The plurality of A/D converters  122  may comprise suitable logic, circuitry, interfaces and/or code that may be operable to convert the analog signals from the receiver  118  to corresponding digital signals. The plurality of A/D converters  122  are disposed between the receiver  118  and the RF processor  124 . Notwithstanding, the disclosure is not limited in this regard. Accordingly, in some embodiments, the plurality of A/D converters  122  may be integrated within the receiver  118 . 
     The RF processor  124  may comprise suitable logic, circuitry, interfaces and/or code that may be operable to demodulate the digital signals output by the plurality of A/D converters  122 . In accordance with an embodiment, the RF processor  124  may comprise a complex demodulator (not shown) that is operable to demodulate the digital signals to form I/Q data pairs that are representative of the corresponding echo signals. The RF or I/Q signal data may then be communicated to an RF/IQ buffer  126 . The RF/IQ buffer  126  may comprise suitable logic, circuitry, interfaces and/or code that may be operable to provide temporary storage of the RF or I/Q signal data, which is generated by the RF processor  124 . 
     The receive beamformer  120  may comprise suitable logic, circuitry, interfaces and/or code that may be operable to perform digital beamforming processing to, for example, sum the delayed channel signals received from RF processor  124  via the RF/IQ buffer  126  and output a beam summed signal. The resulting processed information may be the beam summed signal that is output from the receive beamformer  120  and communicated to the signal processor  132 . In accordance with 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  comprises a plurality of receive beamformers  120 . 
     The user input device  130  may be utilized to input patient data, scan parameters, settings, select protocols and/or templates, select an examination type, select a desired ultrasound image view, select valid sample identification algorithms, reposition automatically-placed regions of interest, and the like. In an exemplary embodiment, the user input device  130  may be operable to configure, manage and/or control operation of one or more components and/or modules in the ultrasound system  100 . In this regard, the user input device  130  may be operable to configure, manage and/or control operation of the transmitter  102 , the ultrasound probe  104 , the transmit beamformer  110 , the receiver  118 , the receive beamformer  120 , the RF processor  124 , the RF/IQ buffer  126 , the user input device  130 , the signal processor  132 , the image buffer  136 , the display system  134 , and/or the archive  138 . The user input device  130  may include button(s), rotary encoder(s), a touchscreen, a touch pad, a trackball, motion tracking, voice recognition, a mousing device, keyboard, camera and/or any other device capable of receiving a user directive. In certain embodiments, one or more of the user input devices  130  may be integrated into other components, such as the display system  134 , for example. As an example, user input device  130  may include a touchscreen display. 
     The signal processor  132  may comprise suitable logic, circuitry, interfaces and/or code that may be operable to process ultrasound scan data (i.e., summed IQ signal) for generating ultrasound images for presentation on a display system  134 . The signal processor  132  is operable to perform one or more processing operations according to a plurality of selectable ultrasound modalities on the acquired ultrasound scan data. In an exemplary embodiment, the signal processor  132  may be operable to perform display processing and/or control processing, among other things. Acquired ultrasound scan data may be processed in real-time during a scanning session as the echo signals are received. Additionally or alternatively, the ultrasound scan data may be stored temporarily in the RF/IQ buffer  126  during a scanning session and processed in less than real-time in a live or off-line operation. In various embodiments, the processed image data can be presented at the display system  134  and/or may be stored at the archive  138 . The archive  138  may be a local archive, a Picture Archiving and Communication System (PACS), an enterprise archive (EA), a vendor-neutral archive (VNA), or any suitable device for storing images and related information. 
     The signal processor  132  may be one or more central processing units, microprocessors, microcontrollers, and/or the like. The signal processor  132  may be an integrated component, or may be distributed across various locations, for example. In an exemplary embodiment, the signal processor  132  may comprise an image analysis processor  140 , a segmentation processor  150 , a sample identification processor  160 , a region of interest (ROI) positioning processor  170 , and a hepatorenal index (HRI) processor  180 . The signal processor  132  may be capable of receiving input information from a user input device  130  and/or archive  138 , receiving image data, generating an output displayable by a display system  134 , and manipulating the output in response to input information from a user input device  130 , among other things. The signal processor  132 , including the image analysis processor  140 , the segmentation processor  150 , the sample identification processor  160 , the region of interest (ROI) positioning processor  170 , and the hepatorenal index (HRI) processor  180 , may be capable of executing any of the method(s) and/or set(s) of instructions discussed herein in accordance with the various embodiments, for example. 
     The ultrasound system  100  may be operable to continuously acquire ultrasound scan data at a frame rate that is suitable for the imaging situation in question. Typical frame rates range from 20-120 but may be lower or higher. The acquired ultrasound scan data may be displayed on the display system  134  at a display-rate that can be the same as the frame rate, or slower or faster. An image buffer  136  is included for storing processed frames of acquired ultrasound scan data that are not scheduled to be displayed immediately. Preferably, the image buffer  136  is of sufficient capacity to store at least several minutes&#39; worth of frames of ultrasound scan data. The frames of ultrasound scan data are stored in a manner to facilitate retrieval thereof according to its order or time of acquisition. The image buffer  136  may be embodied as any known data storage medium. 
     The signal processor  132  may include an image analysis processor  140  that comprises suitable logic, circuitry, interfaces and/or code that may be operable to analyze acquired ultrasound image data to determine whether a desired ultrasound image view has been obtained. For example, the image analysis processor  140  may analyze ultrasound image data acquired by an ultrasound probe  104  to determine whether a desired view, such as a Morison&#39;s pouch ultrasound image view or any suitable ultrasound image view of the liver and kidney, has been obtained. The image analysis processor  140  may direct the signal processor  132  to freeze the view presented at the display system  134  once the desired image view is obtained. The view may be stored at archive  138  and/or any suitable data storage medium. The image analysis processor  140  may include, for example, artificial intelligence image analysis algorithms, one or more deep neural networks (e.g., a convolutional neural network such as u-net) and/or may utilize any suitable image analysis techniques or machine learning processing functionality configured to determine whether a desired view has been obtained. Additionally and/or alternatively, the artificial intelligence image analysis techniques or machine learning processing functionality configured to provide the image analysis techniques may be provided by a different processor or distributed across multiple processors at the ultrasound system  100  and/or a remote processor communicatively coupled to the ultrasound system  100 . In various embodiments, the image analysis processor  140  may comprise suitable logic, circuitry, interfaces and/or code that may be operable to provide a quality metric associated with the obtained view. For example, the image analysis processor  140  may analyze the obtained ultrasound image view as a whole, regions of the obtained ultrasound image view, the obtained ultrasound image view segmented by the segmentation processor  150 , or the like to provide a quality metric associated with the obtained view. The image analysis processor  140  may be configured to cause the display system  134  to present the quality metric with the obtained ultrasound image view. For example, the quality metric may be a score (e.g., 1, 2, 3, 4, 5), grade (e.g., A, B, C, D, F), rating (e.g., Excellent, Good, Fair, Poor), color-coding (e.g., green, yellow, red), or the like of the obtained ultrasound image view as a whole and/or for each region of the obtained ultrasound image view. The quality metric may assist a user in determining whether to proceed with the obtained view or to acquire additional ultrasound image data. The image analysis processor  140  may store the quality metric at archive and/or any suitable data storage medium. 
     The signal processor  132  may include a segmentation processor  150  that comprises suitable logic, circuitry, interfaces and/or code that may be operable to segment flow image frames and B-mode frames. The segmentation processor  150  may be used to identify a liver and a renal cortex of a kidney in the obtained ultrasound image view, such as the Morison&#39;s pouch view. In this regard, the segmentation processor  150  may include, for example, artificial intelligence image analysis algorithms, one or more deep neural networks (e.g., a convolutional neural network such as u-net) and/or may utilize any suitable form of artificial intelligence image analysis techniques or machine learning processing functionality configured to provide automated segmentation functionality. Additionally and/or alternatively, the artificial intelligence image analysis techniques or machine learning processing functionality configured to provide the automated segmentation may be provided by a different processor or distributed across multiple processors at the ultrasound system  100  and/or a remote processor communicatively coupled to the ultrasound system  100 . For example, the image segmentation functionality may be provided as a deep neural network that may be made up of, for example, an input layer, an output layer, and one or more hidden layers in between the input and output layers. Each of the layers may be made up of a plurality of processing nodes that may be referred to as neurons. For example, the image segmentation functionality may include an input layer having a neuron for each sample or a group of samples from an obtained ultrasound image view of the liver and a kidney. The output layer may have a neuron corresponding to a plurality of pre-defined anatomical structures, such as a liver, a renal cortex, or any suitable anatomical structure. Each neuron of each layer may perform a processing function and pass the processed ultrasound image information to one of a plurality of neurons of a downstream layer for further processing. As an example, neurons of a first layer may learn to recognize edges of structure in the obtained ultrasound image. The neurons of a second layer may learn to recognize shapes based on the detected edges from the first layer. The neurons of a third layer may learn positions of the recognized shapes relative to landmarks in the obtained ultrasound image. The processing performed by the deep neural network may identify anatomical structures and the location of the structures in the obtained ultrasound image with a high degree of probability. 
     In an exemplary embodiment, the segmentation processor  150  may be configured to store the image segmentation information at archive  138  and/or any suitable storage medium. The segmentation processor  150  may be configured to cause the display system  134  to present the image segmentation information with the obtained ultrasound image. The image segmentation information may be provided to the image analysis processor  140  for providing a quality metric associated with the obtained ultrasound image view as discussed above. The image segmentation information may be provided to the sample identification processor  160  for identifying valid samples in the liver and renal cortex portions of the obtained ultrasound image, as described below. 
       FIG. 2  is an exemplary display  200  of a Morison&#39;s pouch ultrasound image view  202  having an anatomical segmentation  210 ,  220  of a liver  204  and renal cortex  208 , in accordance with various embodiments. Referring to  FIG. 2 , a display  200  includes an obtained ultrasound image view  202 , such as a Morison&#39;s pouch ultrasound image view  202  having a liver  204  and a kidney  206 . The segmentation processor  150  may perform image segmentation  210 ,  220  on the obtained ultrasound image view  202  to identify a liver  204  and a renal cortex  208  of a kidney  206 . The obtained ultrasound image view  202  having the segmented  210  liver  204  and segmented  220  renal cortex  208  of the kidney  206  may be displayed  200  at the display system  134 , provided to the image analysis processor  140 , provided to the sample identification processor  160 , and/or stored at archive  138  and/or any suitable data storage medium. 
     The signal processor  132  may include a sample identification processor  160  that comprises suitable logic, circuitry, interfaces and/or code that may be operable to identify valid samples in the liver  204  and renal cortex  208  in the obtained ultrasound image view  202  by excluding invalid samples. For example, the sample identification processor  160  may identify valid samples based on exclusion of invalid samples from the liver  204  and renal cortex  208  in the obtained ultrasound image view  202 . The sample identification processor  160  may exclude samples from the segmented  210  liver  204  of the obtained ultrasound image view  202  by applying artificial intelligence algorithms and/or any suitable image analysis technique to exclude samples inside large ducts, vessels, masses, and cysts in the liver  204 . The sample identification processor  160  may apply artificial intelligence algorithms and/or any suitable image analysis technique to exclude samples in the segmented  210  liver  204  having artifacts and/or in artifact prone regions. The sample identification processor  160  may apply artificial intelligence algorithms and/or any suitable image analysis technique to exclude samples in the segmented  210  liver  204  within a threshold distance from a boundary of the liver  204 . The sample identification processor  160  may apply artificial intelligence algorithms and/or any suitable image analysis technique to identify valid samples in the segmented  210  liver  204  in only homogenous regions of the segmented  210  liver  204 . The sample identification processor  160  may apply artificial intelligence algorithms and/or any suitable image analysis technique to exclude samples in the segmented  210  liver  204  having sample values above an upper threshold or below a lower threshold. The sample identification processor  160  may apply artificial intelligence algorithms and/or any suitable image analysis technique to exclude samples in the segmented  210  liver  204  that are greater than a threshold distance from the kidney  206 . 
     The sample identification processor  160  may apply artificial intelligence algorithms and/or any suitable image analysis technique to exclude samples inside masses, cysts, collecting system, and external renal tissue in the segmented  210  renal cortex  208 . The sample identification processor  160  may apply artificial intelligence algorithms and/or any suitable image analysis technique to exclude samples in the segmented  210  renal cortex  208  having artifacts and/or in artifact prone regions. The sample identification processor  160  may apply artificial intelligence algorithms and/or any suitable image analysis technique to exclude samples in the segmented  210  renal cortex  208  within a threshold distance from a boundary of the kidney  206 . The sample identification processor  160  may apply artificial intelligence algorithms and/or any suitable image analysis technique to identify valid samples in the segmented  210  renal cortex  208  in only homogenous regions of the segmented  210  renal cortex  208 . The sample identification processor  160  may apply artificial intelligence algorithms and/or any suitable image analysis technique to exclude samples in the segmented  210  renal cortex  208  having sample values above an upper threshold or below a lower threshold. The sample identification processor  160  may apply artificial intelligence algorithms and/or any suitable image analysis technique to exclude samples in the segmented  210  renal cortex  208  that are greater than a threshold distance from the liver  204 . 
     In various embodiments, the sample identification processor  160  may be configured to identify the valid samples with highlighting, shading, labeling, and/or any suitable visual indication. The obtained ultrasound image view  202  having the visual indication of the identified samples may be displayed at the display system  134 . Additionally and/or alternatively, the sample identification processor  160  may provide the obtained ultrasound image view  202  having the identified valid samples (with or without a visual indication) to the ROI positioning processor  170 . The sample identification processor  160  may additionally and/or alternatively store the obtained ultrasound image view  202  having the identified valid samples (with or without a visual indication) to archive  138  and/or any suitable data storage medium. 
       FIG. 3  is an exemplary display  300  of the segmented Morison&#39;s pouch ultrasound image view  202  of  FIG. 2  having identified valid samples  230 ,  240 , in accordance with various embodiments. Referring to  FIG. 3 , a display  300  includes the segmented ultrasound image view  202  of  FIG. 2  depicting a Morison&#39;s pouch ultrasound image view  202  having a liver  204  and a kidney  206 . The sample identification processor  160  may apply sample identification algorithms on the segmented ultrasound image view  202  to identify valid samples of the liver  204  and the renal cortex  208  of the kidney  206 . The sample identification processor  160  may be configured to identify the valid samples with highlighting, shading, labeling, and/or any suitable visual indication  230 ,  240 . The segmented ultrasound image view  202  having the visual indication  230  of the identified valid samples of the liver  204  and the visual indication  240  of the identified valid samples of the renal cortex  208  may be displayed  300  at the display system  134 . The samples  232 ,  242  of the segmented liver  204  and renal cortex  208  excluded by the sample identification processor  160  may not include a visual indication or may be provided with a different visual indication from the valid samples  230 ,  240 . The segmented ultrasound image view  202  having the identified valid samples  230  of the liver  204  and the identified valid samples  240  of the renal cortex  208  of the kidney  206  may be displayed  300  at the display system  134 , provided to the ROI positioning processor  170 , and/or stored at archive  138  and/or any suitable data storage medium. 
     The signal processor  132  may include a region of interest (ROI) positioning processor  170  that comprises suitable logic, circuitry, interfaces and/or code that may be operable to automatically position a liver region of interest and a renal cortex region of interest in the obtained ultrasound image view  202  based on the identified valid samples  230 ,  240  and at least one criterion. For example, the ROI positioning processor  170  may be configured to apply algorithms defined by the at least one criterion to the valid samples  230 ,  240  of the obtained ultrasound image view  202  to identify a densest valid sample region in the renal cortex  208  of the kidney  206  and the densest valid sample region in the liver  204 . The ROI positioning processor  170  may determine the densest valid sample region of the liver  204  based at least in part on one or more of a shortest distance to a center line of the transducer data, a shortest distance to the densest valid sample region of the renal cortex  208 , the densest valid sample region of the liver  204  at a same image depth as the densest valid sample region of the renal cortex  208 , and/or the densest valid sample region of the liver  204  at a same geometrical depth (e.g., if using a curved array transducer) as the densest valid sample region of the renal cortex  208 . The ROI positioning processor  170  may be configured to provide visual indications of the liver region of interest and the renal cortex region of interest in the obtained ultrasound image view  202 . The visual indications of the regions of interest may include highlighting, shading, labeling, overlaid shapes, and/or any suitable visual indications. The obtained ultrasound image view  202  having the visual indications of the liver region of interest and the renal cortex region of interest may be displayed at the display system  134 . Additionally and/or alternatively, the ROI positioning processor  170  may provide the obtained ultrasound image view  202  having the positioned liver region of interest and renal cortex region of interest (with or without visual indications) to the HRI processor  180 . The ROI positioning processor  170  may additionally and/or alternatively store the obtained ultrasound image view  202  having the positioned liver region of interest and renal cortex region of interest (with or without visual indications) to archive  138  and/or any suitable data storage medium. 
       FIG. 4  is an exemplary display  400  of the segmented Morison&#39;s pouch ultrasound image view  202  having identified valid samples  230 ,  240  of  FIG. 3  with automatically positioned liver and renal cortex regions of interest  250 ,  260  for HRI calculation, in accordance with various embodiments. Referring to  FIG. 4 , a display  400  includes the segmented ultrasound image view  202  of  FIG. 3  depicting a Morison&#39;s pouch ultrasound image view  202  having a liver  204  and a kidney  206  with identified valid samples  230 ,  240 . The ROI positioning processor  170  may automatically position a liver region of interest  250  and a renal cortex region of interest  260  in the obtained ultrasound image view  202  based on the identified valid samples  230 ,  240  and at least one criterion. The ROI positioning processor  170  may be configured to identify the liver and renal cortex regions of interest  250 ,  260  with highlighting, shading, labeling, overlaid shapes, and/or any suitable visual indication. The segmented ultrasound image view  202  having the automatically positioned liver and renal cortex regions of interest  250 ,  260  may be displayed  400  at the display system  134 , provided to the HRI processor  180 , and/or stored at archive  138  and/or any suitable data storage medium. In various embodiments, a clinician may re-position the automatically positioned liver and/or renal cortex regions of interest  250 ,  260  via the user input device  130 . 
     The signal processor  132  may include a hepatorenal index (HRI) processor  180  that comprises suitable logic, circuitry, interfaces and/or code that may be operable to determine an HRI based on the liver and renal cortex regions of interest  250 ,  260  in the segmented ultrasound image view  202 . For example, the HRI processor  180  may determine the HRI by calculating a ratio of the liver data in the liver region of interest  250  to the renal cortex data in the renal cortex region of interest  260 . The liver data and renal cortex data may comprise the beamformed data, in-phase and quadrature (I/Q) data, post-processed data, or grayscale values. The HRI processor  180  may be configured to cause the display system  134  to present the HRI value. In certain embodiments, the HRI processor  180  may be configured to determine a quality score corresponding with the determined HRI value. For example, the HRI processor  180  may apply a quality score algorithm based on a standard deviation from multiple regions of interest in the liver  204  and renal cortex  208  of the segmented ultrasound image view  202 . The HRI processor  180  may be configured to cause the display system  134  to present the quality score with the HRI value. 
     The display system  134  may be any device capable of communicating visual information to a user. For example, a display system  134  may include a liquid crystal display, a light emitting diode display, and/or any suitable display or displays. The display system  134  can be operable to display information from the signal processor  132  and/or archive  138 , such as ultrasound images  202  with and/or without image quality metrics, segmentation information  210 ,  220 , valid  230 ,  240  and/or invalid  232 ,  242  sample information, regions of interest  250 ,  260 , HRI values, HRI quality scores, and/or any suitable information. 
     The archive  138  may be one or more computer-readable memories integrated with the ultrasound system  100  and/or communicatively coupled (e.g., over a network) to the ultrasound system  100 , such as a Picture Archiving and Communication System (PACS), an enterprise archive (EA), a vendor-neutral archive (VNA), a server, a hard disk, floppy disk, CD, CD-ROM, DVD, compact storage, flash memory, random access memory, read-only memory, electrically erasable and programmable read-only memory and/or any suitable memory. The archive  138  may include databases, libraries, sets of information, or other storage accessed by and/or incorporated with the signal processor  132 , for example. The archive  138  may be able to store data temporarily or permanently, for example. The archive  138  may be capable of storing medical image data, data generated by the signal processor  132 , and/or instructions readable by the signal processor  132 , among other things. In various embodiments, the archive  138  stores ultrasound images  202 , image quality metrics generated by the image analysis processor  140 , instructions for analyzing the ultrasound images  202  and generating the image quality metrics, segmentation information generated by the segmentation processor  150 , instructions for performing image segmentation, valid and/or invalid sample information  230 ,  232 ,  240 ,  242  generated by the sample identification processor  160 , instructions for identifying valid samples  230 ,  240 , region of interest information  250 ,  260  generated by the ROI positioning processor  170 , instructions for automatically positioning regions of interest  250 ,  260 , HRI values generated by the HRI processor  180 , instructions for determining HRI values, HRI quality scores generated by the HRI processor  180 , and/or instructions for generating HRI quality scores, among other things. 
     Components of the ultrasound system  100  may be implemented in software, hardware, firmware, and/or the like. The various components of the ultrasound system  100  may be communicatively linked. Components of the ultrasound system  100  may be implemented separately and/or integrated in various forms. For example, the display system  134  and the user input device  130  may be integrated as a touchscreen display. 
     Still referring to  FIG. 1 , the training system  200  may comprise a training engine  210  and a training database  220 . The training engine  160  may comprise suitable logic, circuitry, interfaces and/or code that may be operable to train the neurons of the deep neural network(s) (e.g., artificial intelligence model(s)) inferenced (i.e., deployed) by the image analysis processor  140 , segmentation processor  150 , sample identification processor  160 , ROI positioning processor  170 , and/or HRI processor  180 . For example, the artificial intelligence model inferenced by the image analysis processor  140  may be trained to automatically identify an ultrasound image view (e.g., a Morison&#39;s pouch ultrasound image view  202 ). The artificial intelligence model inferenced by the segmentation processor  150  may be trained to automatically segment an obtained ultrasound image view to identify anatomies (e.g., a liver  204  and a renal cortex  208  of a kidney  206 ). As an example, the training engine  210  may train the deep neural networks deployed by the image analysis processor  140  and/or segmentation processor  150  using database(s)  220  of classified Morison&#39;s pouch ultrasound image views of a liver  204  and a renal cortex  208  of a kidney  206 . The ultrasound images may include ultrasound images of a particular anatomical feature, such as Morison&#39;s pouch ultrasound image views  202  having a liver  204  and a renal cortex  208  of a kidney  206 , or any suitable ultrasound images and features. 
     In various embodiments, the databases  220  of training images may be a Picture Archiving and Communication System (PACS), or any suitable data storage medium. In certain embodiments, the training engine  210  and/or training image databases  220  may be remote system(s) communicatively coupled via a wired or wireless connection to the ultrasound system  100  as shown in  FIG. 1 . Additionally and/or alternatively, components or all of the training system  200  may be integrated with the ultrasound system  100  in various forms. 
       FIG. 5  is a flow chart  500  illustrating exemplary steps  502 - 516  that may be utilized for automatically estimating an HRI from ultrasound images, in accordance with exemplary embodiments. Referring to  FIG. 5 , there is shown a flow chart  500  comprising exemplary steps  502  through  516 . Certain embodiments may omit one or more of the steps, and/or perform the steps in a different order than the order listed, and/or combine certain of the steps discussed below. For example, some steps may not be performed in certain embodiments. As a further example, certain steps may be performed in a different temporal order, including simultaneously, than listed below. 
     At step  502 , an ultrasound system  100  may acquire a sequence of ultrasound data. For example, the ultrasound probe  104  of the ultrasound system  100  may continuously acquire ultrasound image data until a desired ultrasound image view is obtained. The acquired sequence of ultrasound data may be processed and presented at a display system  134 . 
     At step  504 , a signal processor  132  of the ultrasound system  100  may determine whether a desired ultrasound image view  202  has been obtained. For example, an image analysis processor  140  of the signal processor  132  of the ultrasound system  100  may analyze the ultrasound image data acquired at step  502  to determine whether a desired ultrasound image view  202  has been obtained. The desired ultrasound image view  202  may be a Morison&#39;s pouch ultrasound image view or any suitable ultrasound image view of the liver  204  and kidney  206 . The image analysis processor  140  may direct the signal processor  132  to freeze the view presented at the display system  134  once the desired image view is obtained. The image analysis processor  140  may include, for example, artificial intelligence image analysis algorithms, one or more deep neural networks (e.g., a convolutional neural network such as u-net) and/or may utilize any suitable image analysis techniques or machine learning processing functionality configured to determine whether a desired view has been obtained. 
     At step  506 , the signal processor  132  of the ultrasound system  100  may assign a quality metric to the obtained ultrasound image view  202 . For example, the image analysis processor  140  may analyze the obtained ultrasound image view  202  as a whole, regions of the obtained ultrasound image view  202 , the obtained ultrasound image view segmented by the segmentation processor  150  at step  508 , or the like to provide a quality metric associated with the obtained ultrasound image view  202 . The image analysis processor  140  may be configured to cause the display system  134  to present the quality metric with the obtained ultrasound image view  202 . 
     At step  508 , a signal processor  132  of the ultrasound system  100  may segment  210 ,  220  a liver  204  and a renal cortex  208  in the obtained ultrasound image view  202 . For example, a segmentation processor  140  of the signal processor  132  may be configured to identify  210 ,  220  a liver  204  and a renal cortex  208  of a kidney  206  in the obtained ultrasound image view  202 , such as the Morison&#39;s pouch view. The segmentation processor  150  may include, for example, artificial intelligence image analysis algorithms, one or more deep neural networks (e.g., a convolutional neural network such as u-net) and/or may utilize any suitable form of artificial intelligence image analysis techniques or machine learning processing functionality configured to provide automated segmentation functionality. The segmentation processor  150  may be configured to cause the display system  134  to present the image segmentation information  210 ,  220  with the obtained ultrasound image  202 . 
     At step  510 , the signal processor  132  of the ultrasound system  100  may identify valid samples  230 ,  240  in the liver  204 ,  210  and the renal cortex  208 ,  220  in the obtained ultrasound image view  202  by excluding invalid samples  232 ,  242 . For example, a sample identification processor  160  of the signal processor  132  of the ultrasound system  100  may be configured to exclude samples  232  from the segmented  210  liver  204  of the obtained ultrasound image view  202  by applying artificial intelligence algorithms and/or any suitable image analysis technique to exclude samples  232  inside large ducts, vessels, masses, and cysts in the liver  204 , samples  232  in the segmented  210  liver  204  having artifacts and/or in artifact prone regions, samples  232  in the segmented  210  liver  204  within a threshold distance from a boundary of the liver  204 , samples  232  in the segmented  210  liver  204  in non-homogenous regions, samples in the segmented  210  liver  204  having sample values above an upper threshold or below a lower threshold, and/or samples in the segmented  210  liver  204  that are greater than a threshold distance from the kidney  206 . As another example, the sample identification processor  160  may apply artificial intelligence algorithms and/or any suitable image analysis technique to exclude samples  242  inside masses, cysts, collecting system, and external renal tissue in the segmented  210  renal cortex  208 , samples  242  in the segmented  210  renal cortex  208  having artifacts and/or in artifact prone regions, samples  242  in the segmented  210  renal cortex  208  within a threshold distance from a boundary of the kidney  206 , samples  242  in the segmented  210  renal cortex  208  in non-homogenous regions, samples  242  in the segmented  210  renal cortex  208  having sample values above an upper threshold or below a lower threshold, and/or samples  242  in the segmented  210  renal cortex  208  that are greater than a threshold distance from the liver  204 . The sample identification processor  160  may be configured to identify the valid samples with highlighting, shading, labeling, and/or any suitable visual indication. The obtained ultrasound image view  202  having the visual indication of the identified samples may be displayed at the display system  134 . 
     At step  512 , the signal processor  132  of the ultrasound system  100  may automatically position a liver region of interest  250  and a renal cortex region of interest  260  in the obtained ultrasound image view  202  based on the valid samples  230 ,  240  and at least one criterion. For example, a region of interest (ROI) positioning processor  170  of the signal processor  132  of the ultrasound system  100  may be configured to apply algorithms defined by the at least one criterion to the valid samples  230 ,  240  of the obtained ultrasound image view  202  to identify a densest valid sample region in the renal cortex  208  of the kidney  206  and the densest valid sample region in the liver  204 . The ROI positioning processor  170  may determine the densest valid sample region of the liver  204  based at least in part on one or more of a shortest distance to a center line of the transducer data, a shortest distance to the densest valid sample region of the renal cortex  208 , the densest valid sample region of the liver  204  at a same image depth as the densest valid sample region of the renal cortex  208 , and/or the densest valid sample region of the liver  204  at a same geometrical depth (e.g., if using a curved array transducer) as the densest valid sample region of the renal cortex  208 . The ROI positioning processor  170  may be configured to provide visual indications of the liver region of interest  250  and the renal cortex region of interest  260  in the obtained ultrasound image view  202 . The visual indications of the regions of interest may include highlighting, shading, labeling, overlaid shapes, and/or any suitable visual indications. The obtained ultrasound image view  202  having the visual indications of the liver region of interest and the renal cortex region of interest may be displayed at the display system  134 . In various embodiments, a clinician may re-position the automatically positioned liver and/or renal cortex regions of interest  250 ,  260  via the user input device  130 . 
     At step  514 , the signal processor  132  of the ultrasound system  100  may determine a hepatorenal index (HRI) and a quality score of the HRI. For example, an HRI processor  180  of the signal processor  132  of the ultrasound system  100  may be configured to determine an HRI based on the liver and renal cortex regions of interest  250 ,  260  in the segmented ultrasound image view  202 . The HRI processor  180  may determine the HRI by calculating a ratio of the liver data in the liver region of interest  250  to the renal cortex data in the renal cortex region of interest  260 . The liver data and renal cortex data may comprise beamformed data, in-phase and quadrature (I/Q) data, post-processed data, or grayscale values. In certain embodiments, the HRI processor  180  may be configured to determine a quality score corresponding with the determined HRI value. As an example, the HRI processor  180  may apply a quality score algorithm based on a standard deviation from multiple regions of interest in the liver  204  and renal cortex  208  of the segmented ultrasound image view  202 . 
     At step  516 , the signal processor  132  of the ultrasound system  100  may display the HRI and the quality score of the HRI. For example, the HRI processor  180  may be configured to cause the display system  134  to present the HRI value and the quality score of the HRI. 
     Aspects of the present disclosure provide automatic estimates of a hepatorenal index (HRI) from ultrasound images  202 . In accordance with various embodiments, the method  500  may comprise acquiring  502 , by an ultrasound system  100 , a sequence of ultrasound image data until a desired ultrasound image view  202  is obtained  504 . The method  500  may comprise segmenting  508 , by at least one processor  132 ,  150  of the ultrasound system  100 , a liver  204 ,  210  and a renal cortex  208 ,  220  in the obtained ultrasound image view  202 . The method  500  may comprise identifying  510 , by the at least one processor  132 ,  160 , valid samples  230 ,  240  in the liver  204 ,  210  and the renal cortex  208 ,  220  in the obtained ultrasound image view  202  by excluding invalid samples  232 ,  242 . The method  500  may comprise automatically positioning  512 , by the at least one processor  132 ,  170 , a liver region of interest  250  and a renal cortex region of interest  260  in the obtained ultrasound image view  202  based on the valid samples  230 ,  240  and at least one criterion. The method  500  may comprise determining  514 , by the at least one processor  132 ,  180 , a hepatorenal index (HRI). The method  500  may comprise causing  516 , by the at least one processor  132 ,  180 , a display system  134  to present the HRI. 
     In a representative embodiment, the method  500  may comprise determining  514 , by the at least one processor  132 ,  180 , a quality score of the HRI and causing, by the at least one processor  132 ,  180 , the display system  134  to present the quality score of the HRI with the HRI. In an exemplary embodiment, the method  500  may comprise assigning  506 , by the at least one processor  132 ,  140 , a quality metric to the obtained ultrasound image view  202  and causing  506 , by the at least one processor  132 ,  140 , the display system  134  to present the quality metric. In various embodiments, the invalid samples  232  in the liver  204 ,  210  comprise one or more of: samples inside one or more of ducts, vessels, masses, and cysts; one or both of samples depicting artifacts or samples in artifact prone regions; samples within a threshold distance to a liver boundary line; samples in non-homogenous regions inside the liver  204 ,  210 ; samples having values above an upper threshold or below a lower threshold; and samples greater than a defined distance from the kidney  206 ,  208 . In certain embodiments, the invalid samples  242  in the renal cortex  208 ,  220  comprise one or more of: samples inside one or more of masses, cysts, collecting system, and external renal tissue; one or both of samples depicting artifacts or samples in artifact prone regions; samples within a threshold distance to a kidney boundary line; samples in non-homogenous regions inside the renal cortex  208 ,  220 ; and samples having values above an upper threshold or below a lower threshold. In a representative embodiment, the at least one criterion comprises one or both of a densest valid sample region in the renal cortex  208 ,  220  and a densest valid sample region in the liver  204 ,  210 . The densest valid sample region in the liver  204 ,  210  may be identified based at least in part on one or more of: a shortest distance to a center line of transducer data; a shortest distance to the densest valid sample region in the renal cortex  208 ,  220 ; the densest valid sample region in the liver  204 ,  210  being at a same image depth as the densest valid sample region in the renal cortex  208 ,  220 ; and the densest valid sample region in the liver  204 ,  210  being at a same geometrical depth as the densest valid sample region in the renal cortex  208 ,  220 . In an exemplary embodiment, determining the HRI is based on a ratio of an average of liver data in the liver region of interest  250  and an average of renal cortex data in the renal cortex region of interest  260 . The liver data and the renal cortex data may be one of beamformed data, in-phase and quadrature (I/Q) data, post-processed data, and grayscale values. 
     Various embodiments provide an ultrasound system  100  for automatically estimating a hepatorenal index (HRI) from ultrasound images. The ultrasound system  100  may comprise an ultrasound probe  104 , at least one processor  132 ,  140 ,  150 ,  160 ,  170 ,  180  and a display system  134 . The ultrasound probe  104  may be operable to acquire a sequence of ultrasound image data until a desired ultrasound image view  202  is obtained. The at least one processor  132 ,  150  may be configured to segment a liver  204 ,  210  and a renal cortex  208 ,  220  in the obtained ultrasound image view  202 . The at least one processor  132 ,  160  may be configured to identify valid samples  230 ,  240  in the liver  204 ,  210  and the renal cortex  208 ,  220  in the obtained ultrasound image view  202  by excluding invalid samples  232 ,  242 . The at least one processor  132 ,  170  may be configured to automatically position a liver region of interest  250  and a renal cortex region of interest  260  in the obtained ultrasound image view  202  based on the valid samples  230 ,  240  and at least one criterion. The at least one processor  132 ,  180  may be configured to determine a hepatorenal index (HRI). The display system  134  may be configured to present the HRI. 
     In an exemplary embodiment, the at least one processor  132 ,  180  may be configured to determine a quality score of the HRI and cause the display system  134  to present the quality score of the HRI with the HRI. In various embodiments, the at least one processor  132 ,  140  may be configured to assign a quality metric to the obtained ultrasound image view  202  and cause the display system  134  to present the quality metric. In certain embodiments, the invalid samples  232  in the liver  204 ,  210  comprise one or more of: samples inside one or more of ducts, vessels, masses, and cysts; one or both of samples depicting artifacts or samples in artifact prone regions; samples within a threshold distance to a liver boundary line; samples in non-homogenous regions inside the liver  204 ,  210 ; samples having values above an upper threshold or below a lower threshold; and samples greater than a defined distance from the kidney  206 ,  208 . In a representative embodiment, the invalid samples  242  in the renal cortex  208 ,  220  comprise one or more of: samples inside one or more of masses, cysts, collecting system, and external renal tissue; one or both of samples depicting artifacts or samples in artifact prone regions; samples within a threshold distance to a kidney boundary line; samples in non-homogenous regions inside the renal cortex; and samples having values above an upper threshold or below a lower threshold. In an exemplary embodiment, the at least one criterion may comprise one or both of a densest valid sample region in the renal cortex  208 ,  220  and a densest valid sample region in the liver  204 ,  210 . The densest valid sample region in the liver  204 ,  210  may be identified based at least in part on one or more of: a shortest distance to a center line of transducer data; a shortest distance to the densest valid sample region in the renal cortex  208 ,  220 ; the densest valid sample region in the liver  204 ,  210  being at a same image depth as the densest valid sample region in the renal cortex  208 ,  220 ; and the densest valid sample region in the liver  204 ,  210  being at a same geometrical depth as the densest valid sample region in the renal cortex  208 ,  220 . In various embodiments, the at least one processor  132 ,  180  configured to determine the HRI based on a ratio of an average of liver data in the liver region of interest  250  and an average of renal cortex data in the renal cortex region of interest  260 . The liver data and the renal cortex data may be one of beamformed data, in-phase and quadrature (I/Q) data, post-processed data, and grayscale values. 
     Certain embodiments provide a non-transitory computer readable medium having stored thereon, a computer program having at least one code section. The at least one code section is executable by a machine for causing an ultrasound system to perform steps  500 . The steps  500  may comprise receiving  502  a sequence of ultrasound image data until a desired ultrasound image view  202  is received  504 . The steps  500  may comprise segmenting  508  a liver  204 ,  210  and a renal cortex  208 ,  220  in the received ultrasound image view  202 . The steps  500  may comprise identifying  510  valid samples  230 ,  240  in the liver  204 ,  210  and the renal cortex  208 ,  220  in the received ultrasound image view  202  by excluding invalid samples  232 ,  242 . The steps  500  may comprise automatically positioning  512  a liver region of interest  250  and a renal cortex region of interest  260  in the received ultrasound image view  202  based on the valid samples  230 ,  240  and at least one criterion. The steps  500  may comprise determining  514  a hepatorenal index (HRI). The steps  500  may comprise causing  516  a display system  134  to present the HRI. 
     In various embodiments, the steps  500  may comprise assigning  506  a quality metric to the received ultrasound image view  202  and causing the display system  134  to present the quality metric. The steps  500  may comprise determining  514  a quality score of the HRI and causing  516  the display system  134  to present the quality score of the HRI with the HRI. In certain embodiments, the invalid samples  232  in the liver  204 ,  210  may comprise one or more of: samples inside one or more of ducts, vessels, masses, and cysts; one or both of samples depicting artifacts or samples in artifact prone regions; samples within a threshold distance to a liver boundary line; samples in non-homogenous regions inside the liver  204 ,  210 ; samples having values above an upper threshold or below a lower threshold; and samples greater than a defined distance from the kidney  206 ,  208 . In a representative embodiment, the invalid samples  242  in the renal cortex  208 ,  220  may comprise one or more of: samples inside one or more of masses, cysts, collecting system, and external renal tissue; one or both of samples depicting artifacts or samples in artifact prone regions; samples within a threshold distance to a kidney boundary line; samples in non-homogenous regions inside the renal cortex  208 ,  220 ; and samples having values above an upper threshold or below a lower threshold. 
     In an exemplary embodiment, the at least one criterion may comprise one or both of a densest valid sample region in the renal cortex  208 ,  220  and a densest valid sample region in the liver  204 ,  210 . The densest valid sample region in the liver  204 ,  210  may be identified based at least in part on one or more of: a shortest distance to a center line of transducer data; a shortest distance to the densest valid sample region in the renal cortex  208 ,  220 ; the densest valid sample region in the liver  204 ,  210  being at a same image depth as the densest valid sample region in the renal cortex  208 ,  220 ; and the densest valid sample region in the liver  204 ,  210  being at a same geometrical depth as the densest valid sample region in the renal cortex  208 ,  220 . In various embodiments, determining  514  the HRI may be based on a ratio of an average of liver data in the liver region of interest  250  and an average of renal cortex data in the renal cortex region of interest  260 . The liver data and the renal cortex data may be one of beamformed data, in-phase and quadrature (I/Q) data, post-processed data, and grayscale values. 
     As utilized herein the term “circuitry” refers to physical electronic components (i.e. hardware) and any software and/or firmware (“code”) which may configure the hardware, be executed by the hardware, and or otherwise be associated with the hardware. As used herein, for example, a particular processor and memory may comprise a first “circuit” when executing a first one or more lines of code and may comprise a second “circuit” when executing a second one or more lines of code. As utilized herein, “and/or” means any one or more of the items in the list joined by “and/or”. As an example, “x and/or y” means any element of the three-element set {(x), (y), (x, y)}. As another example, “x, y, and/or z” means any element of the seven-element set {(x), (y), (z), (x, y), (x, z), (y, z), (x, y, z)}. As utilized herein, the term “exemplary” means serving as a non-limiting example, instance, or illustration. As utilized herein, the terms “e.g.,” and “for example” set off lists of one or more non-limiting examples, instances, or illustrations. As utilized herein, circuitry is “operable” or “configured” to perform a function whenever the circuitry comprises the necessary hardware and code (if any is necessary) to perform the function, regardless of whether performance of the function is disabled, or not enabled, by some user-configurable setting. 
     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, having stored thereon, a machine code and/or a computer program having at least one code section executable by a machine and/or a computer, thereby causing the machine and/or computer to perform the steps as described herein for automatically estimating a hepatorenal index (HRI) from ultrasound images. 
     Accordingly, the present disclosure may be realized in hardware, software, or a combination of hardware and software. The present disclosure may be realized in a centralized fashion in at least one computer system, or in a distributed fashion where different elements are spread across several interconnected computer systems. Any kind of computer system or other apparatus adapted for carrying out the methods described herein is suited. 
     Various embodiments may also be embedded in a computer program product, which comprises all the features enabling the implementation of the methods described herein, and which when loaded in a computer system is able to carry out these methods. Computer program in the present context means any expression, in any language, code or notation, of a set of instructions intended to cause a system having an information processing capability to perform a particular function either directly or after either or both of the following: a) conversion to another language, code or notation; b) reproduction in a different material form. 
     While the present disclosure has been described with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the scope of the present disclosure. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departing from its scope. Therefore, it is intended that the present disclosure not be limited to the particular embodiment disclosed, but that the present disclosure will include all embodiments falling within the scope of the appended claims.