Patent Description:
The following detailed description refers to the accompanying drawings. The same reference numbers in different drawings identify the same or similar elements.

The measurement of the volume of the prostate gland is important for clinical decisions. For example, for male patients with Lower Urinary Tract Symptoms (LUTS) secondary to Benign Prostatic Hypertrophy (BPH), the prostate size may be important to predict response to medical therapy, risk for urinary retention, and/or need for future surgery. The Digital Rectal Exam (DRE) has been the mainstay of a standard physical exam for men above the age of <NUM> for evaluating the prostate for size, surface texture, firmness, and tenderness. However, the estimation of prostate size is qualitative and subjective, and different examiners may reach different conclusions. Furthermore, DRE does not provide an accurate method for evaluating the change in the size of the prostate over time. For example, studies show that an increase in prostate volume of only <NUM> milliliters (mL) is associated with a higher risk of developing acute urinary retention that may require surgical intervention and it may be very difficult to detect a change of such magnitude with DRE.

An imaging modality used for prostate assessment is Transrectal Ultrasound (TRUS). In TRUS, an ultrasound probe is inserted into a patient's rectum, which may be uncomfortable for the patient and inconvenient and/or inefficient for the examiner. Furthermore, to calculate the volume of a patient's prostate from ultrasound images, the examiner may need to obtain ultrasound images in multiple planes, measure the size of the prostate in each image, and use an ellipsoid calculation (e.g., height x length x width x π/<NUM>, etc.) to determine the volume of the prostate. Such manual calculations may be cumbersome and inefficient, and may result in an inaccurate determination of the volume of the prostate. For example, ellipsoid calculations assume that the prostate shape is a perfect ellipsoid, which may not be an accurate assumption.

Imaging with other modalities, such as Magnetic Resonant Imaging (MRI) or Computed Tomography (CT), may generate sectional slices of the prostate that may be used for volumetric contouring to generate a more accurate volume measurement of the prostate. However, these modalities are more expensive, time-consuming, and inconvenient to the patient in comparison to ultrasound. Furthermore, CT scans expose the patient to ionizing radiation.

Implementations described herein relate to an automated prostate analysis system that uses transabdominal ultrasound to capture images of the patient's prostate and that automatically calculates the size, volume, and/or shape of the patient's prostate based on the captured ultrasound images. Transabdominal ultrasound imaging may be more comfortable for the patient and easier to perform for the examiner in comparison to TRUS. Furthermore, automatic calculation of the size and volume of the patient's prostate from the captured transabdominal ultrasound images may be more efficient and more accurate than a manual calculation and/or a calculation based on an ellipsoid formula.

Thus, as described herein, a system includes a controller unit that includes a user interface and is in communication with an ultrasound probe for capturing ultrasound images. The controller unit is configured to obtain an ultrasound image of the patient's lower abdominal area, identify an anatomical landmark in the obtained ultrasound image, and aim the ultrasound probe to transabdominally image the patient's prostate using the identified anatomical landmark as a reference point. Aiming the ultrasound probe may include redirecting the energy of the ultrasound probe in the direction of the patient's prostate. The controller unit may determine an aiming zone or area for the ultrasound probe using the identified anatomical landmark, which may include an identified back wall of the bladder, side wall of the bladder (and/or any other wall of the bladder), the pubic bone, a wall of the rectum, the prostate itself, and/or any other type of identified anatomical landmark in the obtained ultrasound image. Identifying the anatomical landmark may include performing a segmentation process on the obtained ultrasound image using a machine learning model trained to identify one or more different types of anatomical landmarks. In some implementations, aiming the ultrasound probe may include obtaining information from at least one of a position sensor or a pressure sensor located in the ultrasound probe.

In some implementations, the patient's bladder area may be imaged first, the obtained image of the bladder area may be used to aim the ultrasound probe, and the aimed ultrasound probe may be used to image the patient's prostate area. In other implementations, the patient's bladder area and prostate area may be imaged simultaneously, the obtained image of the combined bladder and prostate area may be used to aim the ultrasound probe, and the aimed ultrasound probe may be used to obtain a higher quality image, a more detailed image, and/or a different type of image of the patient's prostate area. In yet other implementations, the patient's prostate area may be imaged directly, the obtained image of the patient's prostate area may be used to aim the ultrasound probe, and the aimed ultrasound probe may be used to obtain a higher quality image, a more detailed image, and/or a different type of image of the patient's prostate area. In yet other implementations, the ultrasound probe may be aimed at the patient's prostate area without acquiring an ultrasound image to aid in aiming the ultrasound probe.

The controller unit is further configured to obtain one or more transabdominal images of the patient's prostate using the aimed ultrasound probe and perform a segmentation process on the obtained transabdominal images of the patient's prostate using a trained machine learning model to identify a boundary of the prostate in the transabdominal images of the patient's prostate. The identified boundary of the prostate may be used to determine a size, volume, and/or shape of the patient's prostate. The determined size, volume, and/or shape of the patient's prostate may then be used to generate an analysis and/or recommendation (e.g., a recommendation for a medical intervention, etc.) based on the determined size, volume, and/or shape of the patient's prostate. Furthermore, the determined size, volume, and/or shape of the patient's prostate may be used to generate a risk prediction based on the determined size, volume, and/or shape of the patient's prostate. Additionally, the determined size, volume, and/or shape of the patient's prostate may be used to predict a change in the size of the patient's prostate in the future (e.g., over an upcoming number of years, etc.), to predict an outcome of a medical prostate reduction treatment, and/or to predict other aspects relating to the patient's prostate.

The analysis and/or recommendation may be based on the determined size, volume, and/or shape, or may be based on the determined size, volume, and/or shape combined with a clinically derived value. As an example, the recommendation may be based on a ratio of a Prostate-Specific Antigen (PSA) value, determined for the patient via a blood test, and the determined volume of the patient's prostate. As another example, a PSA/volume value may be weighted by the patient's age to generate a recommendation. As yet another example, a recommendation may be based on a calculated intravesical prostatic protrusion (IPP) value. An IPP value may measure how much a patient's prostate protrudes into the patient's bladder. A high IPP value may indicate difficulty in voiding urine, for example.

In some implementations, the risk prediction and/or clinical recommendation may be generated using a machine learning model trained to output a quantitative clinical grading value for the patient's prostate based on the determined size, volume, and/or shape. Furthermore, the size, volume, and/or shape of the patient's prostate may be compared to a previously determined size, volume, and/or shape obtained during a previous visit to determine a change in the size, volume, and/or shape of the patient's prostate. The determined change may be used to generate the risk prediction and/or clinical recommendation. Additionally, the same machine learning model, or a different machine learning model, may be used to determine whether the patient's prostate includes a lesion or an area of concern. The machine learning model may generate a likelihood that the patient's prostate includes a lesion or an area of concern and/or may perform segmentation to generate a boundary around an identified lesion or area of concern.

Moreover, the determined size, volume, and/or shape of the patient's prostate may be used to generate a recommendation to improve measurement of the determined size, volume, and/or shape of the patient's prostate. The recommendation may include at least one of a recommendation to adjust a position or angle of the ultrasound probe, a recommendation to the patient to drink water or another fluid, or a recommendation to address a detected artefact or interference in an ultrasound image, to obtain a better image of the prostate.

In some implementations, the ultrasound system may include a dual focus ultrasound transducer. The dual focus ultrasound transducer may include an outer element driven at a first frequency and an inner element driven at a second frequency that is higher than the first frequency. For example, the second frequency may correspond to a harmonic of the first frequency. The use of multiple frequencies may result in an extended depth of field, which results in better focus when imaging structures deeper in a patient's body, such as transabdominal imaging of the patient's prostate. Thus, obtaining the transabdominal images of the patient's prostate using the aimed ultrasound probe may include driving the dual focus ultrasound transducer in the ultrasound probe. Driving the dual focus ultrasound transducer may include generating an ultrasound signal that includes a fundamental frequency and at least one harmonic of the fundamental frequency.

In some implementations, obtaining the transabdominal images of the patient's prostate using the aimed ultrasound probe may include obtaining a set of three-dimensional (3D) ultrasound scan images of the patient's prostate. Furthermore, determining the size, volume, and/or shape of the prostate may include identifying a 3D boundary surface of the patient's prostate in the obtained 3D ultrasound scan images using a trained machine learning model.

Additionally, obtaining the transabdominal images of the patient's prostate may include obtaining one or more different types of ultrasound images, such as, for example, B-mode images (e.g., fundamental, harmonic and/or compounded, etc.), probability mode (P-mode) images, Doppler mode ultrasound images (e.g., Power Doppler, Continuous Wave Doppler, Pulsed Wave Doppler, etc.), motion mode (M-mode) ultrasound images, elastography ultrasound images, and/or any other type of imaging modality that uses ultrasound. A P-mode ultrasound image may correspond to an ultrasound image (e.g., a B-mode ultrasound image, etc.) in which each particular pixel is mapped to a probability indicating whether that particular pixel is within or part of a target organ/structure.

A machine learning model, as described herein, may include a computer program trained using a training set of images, and/or other types of input, to identify a particular feature in an image, to classify the image, or an identified feature in the image, into a particular class from a set of classes, and/or to output a numerical and/or categorical value for a particular parameter. In some implementations, a machine learning model may include a deep learning artificial neural network (DNN), such as a convolutional neural network (CNN). A CNN may be trained to develop multiple convolution matrices, also known as kernels, to identify features in ultrasound images, and/or optical images, including, for example, intensity transitions, shapes, texture information, etc., to identify a particular tissue structure and/or pathology in an image. The CNN, and/or another type of machine learning model, may be trained to perform classification, segmentation, and/or to output one or more values (e.g., a volume value, a size value, a numerical and/or categorical value representing a value on a clinical grading scale, etc.).

A CNN may include multiple layers of nodes, including an input layer, one or more convolution layers, one or more output computing layers, one or more pooling layers, and an output layer. A convolution layer may perform a convolution operation on the output of a set of nodes in the input layer associated with pixels within an area defined by the receptive field of the convolution layer. A pooling layer reduces a set of outputs from adjacent nodes in a convolution layer to reduce the dimensionality of a feature map generated by the convolutional layer. An output computing layer may generate an output for each node in a pooling layer based on an output function, such as, for example, a rectifier activation function. The output layer may include a fully connected layer that generates an output that identifies a feature of interest, that classifies an input into a particular class from a set of classes, and/or that outputs a numerical and/or categorical value for a parameter of interest.

A CNN may be trained using supervised learning, in which a set of images that has been labeled to identify a feature of interest, classified in a particular class from a set of classes, and/or to output a numerical and/or categorical value for a parameter of interest. For example, to train a CNN to perform segmentation to identify an implant capsule, a set of ultrasound images in which the implant capsule has been labeled may be used to train the CNN.

In other implementations, a different type of machine learning model may be used, such as, for example, a linear classifier, a naive Bayesian classifier, a kernel density estimation classifier, a decision tree classifier, a support vector machine classifier, a maximum entropy classifier, and/or another type of classifier. Furthermore, in other implementations, a machine learning model may be trained using unsupervised learning, in which input images have not been labeled with a predetermined classification. Moreover, in some implementations, the ultrasound system may include a "manual caliper" option to manually adjust the segmentation output of a machine learning model. For example, a user may be able to adjust the boundary of a prostate identified by a machine learning model, using a graphical user interface (GUI), keypad or keyboard inputs, etc. Additionally, the manual caliper option may enable a user to perform a manual segmentation without machine learning by defining a boundary of an organ, tissue, or area using the GUI or keypad/keyboard input. As an example, the user may draw the boundary or define a set of points that may be used to calculate the boundary. A user may select to use the manual caliper option to define the boundary when, for example, the machine learning model is determined by the user to be performing unsatisfactorily to determine the boundary. In some implementations, the user may manually select a set of seed points and the machine learning model may use the selected set of seed points together with the ultrasound image data to determine the boundary.

Although implementations described herein refer to scanning a prostate, in other implementations, other body areas, organs, joints, and/or vessels may be scanned. For example, implementations described herein may be used to assess the size, volume, and/or shape of an organ, tissue, or structure in another part of a patient's body that may be imaged using transabdominal ultrasound, such as, for example, the bladder, the kidneys, the colon, the small intestine, the pancreas, the ovaries, the uterus, and/or another organ, tissue, or structure. For example, an ultrasound system may include a bladder mode to scan and assess the size, volume, and/or shape of a patient's bladder, and a prostate mode to scan and assess the size, volume, and/or shape of the patient's prostate, and/or other modes to scan other body parts.

<FIG> is a diagram illustrating an exemplary ultrasound system <NUM> according to an implementation described herein. As shown in <FIG>, ultrasound system <NUM> may include an ultrasound probe <NUM>, a controller unit <NUM>, and a cable <NUM>.

Ultrasound probe <NUM> may house one or more ultrasound transducers configured to generate ultrasound energy at a particular frequency and/or pulse repetition rate and to receive reflected ultrasound energy (e.g., ultrasound echoes) and convert the reflected ultrasound energy into electrical signals. For example, in some implementations, ultrasound probe <NUM> may be configured to transmit ultrasound signals with the center frequency in a range that extends from approximately two megahertz (MHz) to approximately <NUM> or more. In other implementations, ultrasound probe <NUM> may be configured to transmit ultrasound signals in a different range. Furthermore, ultrasound probe <NUM> may house one or more motors for controlling the movement of the ultrasound transducer.

Ultrasound probe <NUM> may include a handle <NUM>, a trigger <NUM>, and a dome <NUM> (also referred to as a "nose"). A user (e.g., a medical practitioner, etc.) may hold ultrasound probe <NUM> via handle <NUM> and press trigger <NUM> to activate one or more ultrasound transceivers and transducers located in dome <NUM> to transmit ultrasound signals toward a patient's region of interest (e.g., a particular body organ, a body joint, a blood vessel, etc.). For example, probe <NUM> may be positioned on a pelvic area of a patient and over the patient's bladder and/or prostate.

Handle <NUM> enables a user to move probe <NUM> relative to a patient's region of interest. Activation of trigger <NUM> initiates an ultrasound scan of a selected anatomical portion while dome <NUM> is in contact with a surface portion of a patient's body when the patient's region of interest is scanned. Dome <NUM> may enclose one or more ultrasound transducers and may be formed from a material that provides an appropriate acoustical impedance match to the anatomical portion and/or permits ultrasound energy to be properly focused as it is projected into the anatomical portion. Dome <NUM> may also include transceiver circuitry that includes a transmitter and a receiver to transmit and receive ultrasound signals. Probe <NUM> may communicate with controller unit <NUM> via a wired connection, such as via cable <NUM>. In other implementations, probe <NUM> may communicate with controller unit <NUM> via a wireless connection (e.g., Bluetooth, WiFi, etc.). In some implementations, probe <NUM> may not include dome <NUM>.

Controller unit <NUM> may house and include one or more processors or processing logic configured to process reflected ultrasound energy that is received by probe <NUM> to produce an image of the scanned anatomical region. Furthermore, controller unit <NUM> may include display <NUM> to enable a user to view images from an ultrasound scan, and/or to enable operational interaction with respect to the user during operation of probe <NUM>. For example, display <NUM> may include an output display/screen, such as a liquid crystal display (LCD), light emitting diode (LED) based display, touchscreen, and/or another type of display that provides text and/or image data to a user.

For example, display <NUM> may provide instructions to an operator for positioning probe <NUM> relative to a selected anatomical portion of a patient. Alternatively, ultrasound probe <NUM> may include a small display (e.g., in handle <NUM>) that provides instructions for positioning ultrasound probe <NUM>. Display <NUM> may also display two-dimensional or three-dimensional images of the selected anatomical region. In some implementations, display <NUM> may include a GUI that allows the user to select various features associated with an ultrasound scan. For example, display <NUM> may include selection items (e.g., buttons, dropdown menu items, checkboxes, etc.) to select particular transmission frequencies, to perform a particular action upon a captured ultrasound image (e.g., organ detection, fluid detection, tissue boundary detection, volume measurement, etc.), and/or other types of selections available to the user.

Additionally, display <NUM> may include selection items to select particular types of ultrasound images to be obtained, such as B-mode images (e.g., fundamental, harmonic and/or compounded, etc.), P-mode images, Doppler ultrasound images, M-mode images, elastography, and/or other types of ultrasound images. Moreover, display <NUM> may include selection items to select an aiming mode for probe <NUM> and/or to initiate a three-dimensional (3D) scan after probe <NUM> has been successfully positioned with respect to the patient's region of interest.

<FIG> is a diagram illustrating an exemplary environment <NUM> for ultrasound system <NUM> according to an implementation described herein. Environment <NUM> illustrates the operation of ultrasound system <NUM> with respect to a patient <NUM>. As shown in <FIG>, patient <NUM> may be positioned so that a patient's region of interest may be scanned. For example, assume the region of interest corresponds to the patient's bladder <NUM> and/or prostate <NUM>. To scan prostate <NUM>, ultrasound probe <NUM> may be positioned against a surface portion of patient <NUM> that is proximate to the anatomical portion to be scanned. The user may apply acoustic gel <NUM> (or gel pads) to the skin of patient <NUM> over the area of prostate <NUM> to provide an acoustical impedance match when dome <NUM> is placed against the skin.

The user may select an aiming mode via controller unit <NUM> (e.g., by selecting an aiming mode button, menu item, etc., on display <NUM>, by speaking a voice command, etc.). Alternatively, an aiming mode may be selected automatically when controller unit <NUM> detects motion of ultrasound probe <NUM> or ultrasound probe <NUM> contacts acoustic gel <NUM> or the skin of patient <NUM> (e.g., via an accelerometer and/or gyroscope inside ultrasound probe <NUM>). Ultrasound probe <NUM> may transmit ultrasound signals <NUM> through bladder <NUM> and prostate <NUM> and may receive reflected ultrasound signals. The reflected ultrasound signals may be processed into images that are displayed on display <NUM>.

Although <FIG> and <FIG> show exemplary components of ultrasound system <NUM>, in other implementations, ultrasound system <NUM> may include fewer components, different components, additional components, or differently arranged components than depicted in <FIG> and <FIG>. Additionally or alternatively, one or more components of ultrasound system <NUM> may perform one or more tasks described as being performed by one or more other components of ultrasound system <NUM>.

For example, in other embodiments, ultrasound probe <NUM> may correspond to a self-contained device that includes a microprocessor housed within ultrasound probe <NUM>, configured to operably control the one or more ultrasound transducers, and to process the reflected ultrasound energy to generate ultrasound images. Accordingly, a display on ultrasound probe <NUM> may be used to display the generated images and/or to view other information associated with the operation of ultrasound probe <NUM>. In yet other implementations, ultrasound probe <NUM> may be coupled to a general-purpose computer, such as a laptop, tablet, smart phone, and/or a desktop computer (via a wired or wireless connection) that includes software that at least partially controls the operation of ultrasound probe <NUM> and/or that includes software to process information received from ultrasound probe <NUM> to generate ultrasound images.

<FIG> is a diagram of a first exemplary implementation of ultrasound probe <NUM> according to an implementation described herein. As shown in <FIG>, ultrasound probe <NUM> may include a single transducer element coupled to two rotational motors. In this implementation, ultrasound probe <NUM> may include a base <NUM> connected to a dome <NUM>, a theta motor <NUM>, a spindle <NUM>, a phi motor <NUM>, and a transducer bucket <NUM> with a transducer <NUM>. Theta motor <NUM>, phi motor <NUM>, and/or transducer <NUM> may include wired or wireless electrical connections that electrically connect theta motor <NUM>, phi motor <NUM>, and/or transducer <NUM> to controller unit <NUM>.

Dome <NUM> may enclose transducer bucket <NUM> and may be formed from a material that provides an appropriate acoustical impedance match to the anatomical portion and/or permits ultrasound energy to be properly focused as it is projected into the anatomical portion. Base <NUM> may house theta motor <NUM> and provide structural support to ultrasound probe <NUM>. Base <NUM> may connect to dome <NUM> and may form a seal with dome <NUM> to protect the components of ultrasound probe <NUM> from the external environment. Theta motor <NUM> may rotate spindle <NUM> with respect to base <NUM> in a longitudinal direction with respect to transducer <NUM>, by rotating around a vertical axis referred to herein as a theta (θ) rotational plane <NUM>. Spindle <NUM> may terminate in a shaft <NUM> and phi motor <NUM> may be mounted onto shaft <NUM>. Phi motor <NUM> may rotate around an axis orthogonal to the theta rotational plane <NUM> around a horizontal axis referred to herein as a phi (ϕ) rotational plane <NUM>. Transducer bucket <NUM> may be mounted to phi motor <NUM> and may move with phi motor <NUM>.

Transducer <NUM> may be mounted to transducer bucket <NUM>. Transducer <NUM> may include a piezoelectric transducer, a capacitive transducer, a micro-electromechanical system (MEMS) transducer, and/or another type of ultrasound transducer. Transducer <NUM>, along with transceiver circuitry associated with transducer <NUM>, may convert electrical signals to ultrasound signals at a particular ultrasound frequency or range of ultrasound frequencies, may receive reflected ultrasound signals (e.g., echoes, etc.), and may convert the received ultrasound signals to electrical signals. Transducer <NUM> may transmit and receive ultrasound signals in a signal direction <NUM> that is substantially perpendicular to the surface of transducer <NUM>.

Signal direction <NUM> may be controlled by the movement of phi motor <NUM> and the orientation of phi motor may be controlled by theta motor <NUM>. For example, phi motor <NUM> may rotate back and forth across an angle that is less than <NUM> degrees to generate ultrasound image data for a particular plane and theta motor <NUM> may rotate to particular positions to obtain ultrasound image data for different planes.

In a 3D scan mode, theta motor <NUM> may cycle through a set of planes one or more times to obtain a full 3D scan of an area of interest. In each particular plane of the set of planes, phi motor <NUM> may rotate to obtain ultrasound image data for the particular plane. The movement of theta motor <NUM> and phi motor <NUM> may be interlaced in the 3D scan motor. For example, the movement of phi motor <NUM> in a first direction may be followed by a movement of theta motor <NUM> from a first plane to a second plane, followed by the movement of phi motor <NUM> in a second direction opposite to the first direction, followed by movement of theta motor <NUM> from the second plane to a third plane, etc. Such interlaced movement may enable ultrasound probe <NUM> to obtain smooth continuous volume scanning as well as improving the rate at which the scan data is obtained.

Additionally, as shown in <FIG>, ultrasound probe <NUM> may include a position sensor <NUM> and a pressure sensor <NUM>. Position sensor <NUM> may include an accelerometer, gyroscope, and/or magnetometer configured to measure changes in the position of ultrasound probe <NUM>. Pressure sensor <NUM> may include a piezoelectric pressure sensor, a capacitive pressure sensor, an inductive pressure sensor, a strain gauge pressure sensor, a thermal pressure sensor, a MEMS sensor, and/or another type of sensor to sense pressure applied against ultrasound probe <NUM>. Position sensor <NUM> and/or pressure sensor <NUM> may be used to help an operator accurately position ultrasound probe <NUM> during aiming and/or scanning by providing feedback and/or instructions to the operator. While position sensor <NUM> and pressure sensor <NUM> are shown in particular locations on ultrasound probe <NUM> in <FIG>, in other implementations, position sensor <NUM> and/or pressure sensor <NUM> may located anywhere on ultrasound probe <NUM>.

<FIG> is a diagram of a second exemplary implementation of ultrasound probe <NUM> according to an implementation described herein. As shown in <FIG>, ultrasound probe <NUM> may include a one-dimensional (1D) array of transducer elements coupled to a rotation motor. In this implementation, ultrasound probe <NUM> may include a base <NUM> connected to dome <NUM>, a theta motor <NUM>, a spindle <NUM>, and a transducer bucket <NUM> with a 1D transducer array <NUM>. Theta motor <NUM> and/or 1D transducer array <NUM> may include wired or wireless electrical connections that electrically connect theta motor <NUM> and/or 1D transducer array <NUM> to controller unit <NUM>.

Base <NUM> may house theta motor <NUM> and provide structural support to ultrasound probe <NUM>. Base <NUM> may connect to dome <NUM> and may form a seal with dome <NUM> to protect the components of ultrasound probe <NUM> from the external environment. Theta motor <NUM> may rotate spindle <NUM> with respect to base <NUM> in longitudinal direction with respect to 1D transducer array <NUM> by rotating around theta rotational plane <NUM>. Spindle <NUM> may terminate in transducer bucket <NUM>. 1D transducer array <NUM> may be mounted to transducer bucket <NUM>. 1D transducer array <NUM> may include a curved 1D array of piezoelectric transducers, capacitive transducers, MEMS transducers, and/or other types of ultrasound transducers. 1D transducer array <NUM> may convert electrical signals to ultrasound signals at a particular ultrasound frequency or range of ultrasound frequencies, may receive reflected ultrasound signals (e.g., echoes, etc.), and may convert the received ultrasound signals to electrical signals. Each element, or a group of elements, of 1D transducer array <NUM> may transmit and receive ultrasound signals in a particular direction of a set of directions, illustrated as item <NUM> in <FIG>. In other implementations, 1D transducer array <NUM> may be controlled to electronically tilt an ultrasound beam in a particular direction. Thus, together, the elements of 1D transducer array <NUM> may generate ultrasound image data for a particular plane. In a 3D scan mode, theta motor <NUM> may cycle through a set of planes one or more times to obtain a full 3D scan of an area of interest. In each particular plane of the set of planes, 1D transducer array <NUM> may obtain ultrasound image data for the particular plane. In some implementations, ultrasound probe <NUM> may not include theta motor <NUM> and/or dome <NUM>. For example, ultrasound probe <NUM> may correspond to a hand-held probe that is moved manually by a user to different positions. Additionally, as shown in <FIG>, ultrasound probe <NUM> may include position sensor <NUM> and pressure sensor <NUM>.

<FIG> is a diagram of a third exemplary ultrasound probe <NUM> according to an implementation described herein. While in second exemplary ultrasound probe <NUM> of <FIG>, 1D transducer array <NUM> rotates around spindle <NUM> in a longitudinal direction with respect to 1D transducer array <NUM>, in third exemplary ultrasound probe <NUM> of <FIG>, 1D transducer array <NUM> rotates around spindle <NUM> in a transverse direction with respect to 1D transducer array <NUM>, causing 1D transducer array <NUM> to move back and forth in a fan-like motion. As shown in <FIG>, ultrasound probe <NUM> may include 1D transducer array <NUM> mounted to transducer bucket <NUM> with base <NUM>, theta motor <NUM>, and spindle <NUM> mounted in a horizontal direction with respect to 1D transducer array <NUM> and positioned perpendicularly to the center of set of directions <NUM>. Thus, theta motor <NUM> may rotate spindle <NUM> with respect to base <NUM> in a transverse direction with respect to 1D transducer array <NUM> by rotating around theta rotational plane <NUM>, causing 1D transducer array <NUM> to move back and forth in a fan-like motion. Additionally, as shown in <FIG>, ultrasound probe <NUM> may include position sensor <NUM> and pressure sensor <NUM>.

<FIG> is a diagram of a fourth exemplary ultrasound probe <NUM> according to an implementation described herein. As shown in <FIG>, ultrasound probe <NUM> may include a two-dimensional (2D) array of transducer elements. In this implementation, ultrasound probe <NUM> may include a base <NUM>, a spindle <NUM>, and a transducer bucket <NUM> with a 2D transducer array <NUM>. 2D transducer array <NUM> may include wired or wireless electrical connections that electrically connects 2D transducer array <NUM> to controller unit <NUM>.

Base <NUM> may provide structural support to ultrasound probe <NUM> and secure spindle <NUM>. Spindle <NUM> may terminate in transducer bucket <NUM>. 2D transducer array <NUM> may be mounted to transducer bucket <NUM>. 2D transducer array <NUM> may include a 2D array of piezoelectric transducers, capacitive transducers, MEMS transducers, and/or other types of ultrasound transducers. 2D transducer array <NUM> may convert electrical signals to ultrasound signals at a particular ultrasound frequency or range of ultrasound frequencies, may receive reflected ultrasound signals (e.g., echoes, etc.), and may convert the received ultrasound signals to electrical signals. Each element of 2D transducer array <NUM> may transmit and receive ultrasound signals in a particular direction of a set of directions, illustrated as item <NUM> in <FIG>. Thus, together, the elements of 2D transducer array <NUM> may generate ultrasound image data for multiple planes to generate a 3D ultrasound scan. In other words, 2D transducer array <NUM> may be controlled to tilt an ultrasound beam electronically in a particular direction. In a 3D scan mode, 2D transducer array <NUM> may cycle through sets of 1D sets of transducer elements one or more times to obtain a full 3D scan of an area of interest. Alternatively, multiple sets of 1D sets of transducer elements, or even all of the transducer elements, of 2D transducer array <NUM> may be activated substantially simultaneously to obtain a full 3D scan of the area of interest. Additionally, as shown in <FIG>, ultrasound probe <NUM> may include position sensor <NUM> and pressure sensor <NUM>.

Although <FIG>, <FIG>, <FIG>, and <FIG> show exemplary components of ultrasound probe <NUM>, in other implementations, ultrasound probe <NUM> may include fewer components, different components, additional components, or differently arranged components than depicted in <FIG>, <FIG>, <FIG>, and <FIG>. Additionally or alternatively, one or more components of ultrasound probe <NUM> may perform one or more tasks described as being performed by one or more other components of ultrasound probe <NUM>.

<FIG> are diagrams of a first view <NUM> and a second view <NUM>, respectively, of a dual element transducer <NUM> that may be included in any of the probes shown in <FIG>. As shown in first view <NUM> of <FIG>, corresponding to a plane perpendicular to transmitted and received ultrasound signals (e.g., perpendicular to signal directions <NUM>, <NUM>, or <NUM>), transducer <NUM> may include an outer transducer element <NUM> and an inner transducer element <NUM>. Outer transducer element <NUM> may be coupled to an outer transducer pulser/receiver <NUM> and inner transducer element <NUM> may be coupled to an inner transducer pulser/receiver <NUM>. Outer transducer pulser/receiver <NUM> may drive outer transducer element <NUM> to pulse at a first frequency and inner transducer pulser/receiver <NUM> may drive inner transducer element <NUM> to pulse at a second frequency. As an example, the second frequency may be a harmonic of the first frequency (e.g., a first harmonic, a second harmonic, etc.). As another example, the transmission frequencies of outer transducer element <NUM> and an inner transducer element <NUM> may be offset by a particular difference. For example, outer transducer element <NUM> may transmit ultrasound signals at a frequency of <NUM> + Δ MHz and inner transducer element <NUM> may transmit ultrasound signals at a frequency of <NUM> - Δ MHz, where Δ is the selected frequency offset. Furthermore, outer transducer pulser/receiver <NUM> and inner transducer pulser/receiver <NUM> may detect and receive ultrasound signal echoes received by outer transducer element <NUM> and inner transducer element <NUM>, respectively.

In some implementations, outer transducer element <NUM> may transmit ultrasound signals at a lower frequency and lower bandwidth and may be used for a spatially long acoustic radiation force pulse. Inner transducer element <NUM> may transmit ultrasound signals at a higher frequency and higher bandwidth, and may be used for the tracking of induced shear waves. As an example, outer transducer element <NUM> may transmit ultrasound pulses at a frequency of f<NUM> and inner transducer element <NUM> may receive an echo from the first harmonic with a resonance at a frequency of <NUM>*f<NUM>.

In some implementations, the transducer frequencies may be optimized for differential harmonics. For example, outer transducer element <NUM> may transmit ultrasound pulses at a frequency of f<NUM>, inner transducer element <NUM> may transmit ultrasound pulses at a frequency of f<NUM>, outer transducer element <NUM> may measure returned echo signals at frequency f<NUM>-f<NUM>, and inner transducer element <NUM> may measure returned echo signals at frequency <NUM>*f<NUM>.

Second view <NUM> of <FIG> illustrates a side view of dual element transducer <NUM> with respect to first view <NUM>. As shown in <FIG>, in some implementations, outer transducer element <NUM> and inner transducer element <NUM> may form a concave parabolic surface in a plane perpendicular to the direction of transmitted and received signals. The concave parabolic surface, together with the difference in the transmitted frequencies, may result in an extended depth of field <NUM> based on a combination of an inner transducer depth of field <NUM> and an outer transducer depth of field <NUM>. Thus, the design of dual element transducer <NUM> may be optimized to extend the depth of field of the transmitted ultrasound signals, while maintaining uniformity in the axial and lateral beam dimensions, based on the dimensions, shape, and relative geometric focus offset of outer transducer element <NUM> and inner transducer element <NUM>, and based on the selected frequencies transmitted and measured by each of outer transducer element <NUM> and inner transducer element <NUM>. For example, the depth of focus of dual element transducer <NUM> may be extended to at least <NUM> centimeters (cm).

<FIG> is a diagram illustrating exemplary components of a device <NUM> according to an implementation described herein. Controller unit <NUM>, display <NUM>, and/or ultrasound probe <NUM> may each include one or more devices <NUM>. As shown in <FIG>, device <NUM> may include a bus <NUM>, a processor <NUM>, a memory <NUM>, an input device <NUM>, an output device <NUM>, and a communication interface <NUM>.

Bus <NUM> may include a path that permits communication among the components of device <NUM>. Processor <NUM> may include any type of single-core processor, multi-core processor, microprocessor, latch-based processor, and/or processing logic (or families of processors, microprocessors, and/or processing logics) that interprets and executes instructions. In other embodiments, processor <NUM> may include an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), and/or another type of integrated circuit or processing logic.

Memory <NUM> may include any type of dynamic storage device that may store information and/or instructions, for execution by processor <NUM>, and/or any type of nonvolatile storage device that may store information for use by processor <NUM>. For example, memory <NUM> may include a random access memory (RAM) or another type of dynamic storage device, a read-only memory (ROM) device or another type of static storage device, a content addressable memory (CAM), a magnetic and/or optical recording memory device and its corresponding drive (e.g., a hard disk drive, optical drive, etc.), and/or a removable form of memory, such as a flash memory.

Input device <NUM> may allow an operator to input information into device <NUM>. Input device <NUM> may include, for example, a keyboard, a mouse, a pen, a microphone, a remote control, an audio capture device, an image and/or video capture device, a touch-screen display, and/or another type of input device. In some embodiments, device <NUM> may be managed remotely and may not include input device <NUM>. In other words, device <NUM> may be "headless" and may not include a keyboard, for example.

Output device <NUM> may output information to an operator of device <NUM>. Output device <NUM> may include a display, a printer, a speaker, and/or another type of output device. For example, device <NUM> may include a display, which may include a liquid-crystal display (LCD) for displaying content to the customer. In some embodiments, device <NUM> may be managed remotely and may not include output device <NUM>. In other words, device <NUM> may be "headless" and may not include a display, for example.

Communication interface <NUM> may include a transceiver that enables device <NUM> to communicate with other devices and/or systems via wireless communications (e.g., radio frequency, infrared, and/or visual optics, etc.), wired communications (e.g., conductive wire, twisted pair cable, coaxial cable, transmission line, fiber optic cable, and/or waveguide, etc.), or a combination of wireless and wired communications. Communication interface <NUM> may include a transmitter that converts baseband signals to radio frequency (RF) signals and/or a receiver that converts RF signals to baseband signals. Communication interface <NUM> may be coupled to an antenna for transmitting and receiving RF signals.

Communication interface <NUM> may include a logical component that includes input and/or output ports, input and/or output systems, and/or other input and output components that facilitate the transmission of data to other devices. For example, communication interface <NUM> may include a network interface card (e.g., Ethernet card) for wired communications and/or a wireless network interface (e.g., a WiFi) card for wireless communications. Communication interface <NUM> may also include a universal serial bus (USB) port for communications over a cable, a Bluetooth™ wireless interface, a radio-frequency identification (RFID) interface, a near-field communications (NFC) wireless interface, and/or any other type of interface that converts data from one form to another form.

As will be described in detail below, device <NUM> may perform certain operations relating to capturing transabdominal ultrasound images of a patient's prostate, automated measurements of the size, volume, and/or shape of the patient's prostate, and/or recommendations based on the measurements. Device <NUM> may perform these operations in response to processor <NUM> executing software instructions contained in a computer-readable medium, such as memory <NUM>. A computer-readable medium may be defined as a non-transitory memory device. A memory device may be implemented within a single physical memory device or spread across multiple physical memory devices. The software instructions may be read into memory <NUM> from another computer-readable medium or from another device. The software instructions contained in memory <NUM> may cause processor <NUM> to perform processes described herein. Alternatively, hardwired circuitry may be used in place of, or in combination with, software instructions to implement processes described herein.

Although <FIG> shows exemplary components of device <NUM>, in other implementations, device <NUM> may include fewer components, different components, additional components, or differently arranged components than depicted in <FIG>. Additionally, or alternatively, one or more components of device <NUM> may perform one or more tasks described as being performed by one or more other components of device <NUM>.

<FIG> is a diagram illustrating exemplary functional components of controller unit <NUM>. The functional components of controller unit <NUM> may be implemented, for example, via processor <NUM> executing instructions from memory <NUM>. Alternatively, some or all of the functional components of controller unit <NUM> may be implemented via hard-wired circuitry. As shown in <FIG>, controller unit <NUM> may include an ultrasound probe controller <NUM>, an ultrasound images database (DB) <NUM>, a bladder machine learning (ML) model <NUM>, an aiming manager <NUM>, a user interface <NUM>, a prostate ML model <NUM>, a size/volume/shape calculator <NUM>, a shape analyzer <NUM>, and a recommendation manager <NUM>.

Ultrasound probe controller <NUM> may be configured to control ultrasound probe <NUM> and to collect ultrasound image data from ultrasound probe <NUM>. For example, ultrasound probe controller <NUM> may perform a 3D scan of a patient's lower abdominal area by generating ultrasound images in particular planes by controlling one or more motors and/or particular transducers of ultrasound probe <NUM>. For example, ultrasound probe controller <NUM> may control ultrasound probe <NUM> to perform a 3D scan that includes all radial, antiradial, sagittal, and transverse planes in the focus zone of ultrasound probe <NUM>. Ultrasound probe controller <NUM> may control outer transducer pulser/receiver <NUM> and inner transducer pulser/receiver <NUM> to cause outer transducer <NUM> and inner transducer <NUM> to each transmit ultrasound signals at a particular frequency or frequency range and/or to receive ultrasound signals at a particular frequency or frequency range. Ultrasound probe controller <NUM> may obtain B-mode images (e.g., fundamental, harmonic and/or compounded, etc.), P-mode images, Doppler mode ultrasound images, M-mode ultrasound images, elastography ultrasound images, and/or any other type of ultrasound images. Ultrasound probe controller <NUM> may store obtained ultrasound images in ultrasound images DB <NUM>.

Bladder ML model <NUM> may include a CNN model and/or another type of ML model trained to perform segmentation to identify the boundaries of a bladder in ultrasound images. For example, bladder ML model <NUM> may identify the back wall, side wall, and/or front wall of a bladder in a set of one or more ultrasound images. In some implementations, other types of ML models may be included that are trained to identify other types of anatomical landmarks.

Aiming manager <NUM> may determine an aiming zone for ultrasound probe <NUM> to image the patient's prostate, based on an anatomical landmark identified by bladder ML model <NUM>. For example, aiming manager <NUM> may identify an area of particular dimensions (e.g., a particular length and width) and locate the identified area a particular distance from the identified anatomical landmark, such as the identified back wall of the bladder.

Aiming manager <NUM> may then aim ultrasound probe <NUM> to image the patient's prostate based on the determined aiming zone to ensure that the ultrasound signals from ultrasound probe <NUM> reach the intended target (e.g., the patient's prostate). For example, aiming manager <NUM> may send instructions to the operator, via user interface <NUM>, to move and/or tilt ultrasound probe <NUM> in a particular direction to position ultrasound probe <NUM> to accurately image the patient's prostate. Additionally, or alternatively, aiming manager <NUM> may adjust the extended depth of field <NUM> of transducer <NUM> by selecting a first frequency, or frequency range, for outer transducer <NUM> and selecting a second frequency, or frequency range, for inner transducer <NUM> to enable the signals transmitted by ultrasound probe <NUM> to reach the required depth of field for imaging the prostate.

Aiming manager <NUM> may ensure the best possible coverage of the prostate. For example, if the prostate is located at off center at <NUM>° in the theta direction (e.g., with respect to the movement of theta motor <NUM>) and <NUM>° in the phi direction (e.g., with respect to the movement of phi motor <NUM>), the 3D ultrasound scan to determine the volume of the prostate may be performed with a shifted origin of <NUM>° in the theta direction and <NUM>° in the phi direction. Furthermore, aiming manager <NUM> may adjust the acoustic and acquisition parameters based on the estimated prostate location determined using the determined aiming zone. The adjusted parameters may include the focus position of ultrasound probe <NUM>, the scanning aperture, the scan depth, the line density, the scan plane density, and the transmission frequencies.

User interface <NUM> may generate or include a user interface (e.g., a graphical user interface) that displays ultrasound images to a user via display <NUM>, displays instructions and/or recommendations to the user, and that is configured to receive selections and/or commands from the user via a touchscreen associated with display <NUM>, via one or more control keys located on controller unit <NUM>, on ultrasound probe <NUM>, via a microphone included in controller unit <NUM>, and/or via another type of input method.

For example, a user may select to image a particular organ, tissue, or structure (e.g., prostate), a particular type of ultrasound image to obtain, to perform a 3D ultrasound scan, select to perform an elastography ultrasound scan, select to retrieve a patient record that includes previously obtained ultrasound images, select to perform a comparison of baseline and follow-up ultrasound images, select to use a machine learning model to perform segmentation and analysis of an ultrasound image, select to identify pathologies in an ultrasound image using a machine learning model, and/or select to perform another type of function for which controller unit <NUM> has been configured.

Prostate ML model <NUM> may include a CNN model and/or another type of ML model trained to perform segmentation to identify the boundaries of a prostate in ultrasound images. In some implementations, prostate ML model <NUM> may be trained to perform segmentation to identify the boundaries of a prostate in a two-dimensional (2D) ultrasound image. Additionally, or alternatively, prostate ML model <NUM> may be trained to perform segmentation to identify a 3D surface of a prostate in a set of ultrasound images obtained during a 3D ultrasound scan. As an example, prostate ML model <NUM> may perform segmentation on individual 2D images from the set of 3D scan images to identify the boundary of the prostate in each image, and may then stitch together or otherwise combine the boundaries from the individual ultrasound images to generate a 3D surface of the prostate.

Furthermore, in some implementations, prostate ML model <NUM>, or another ML model, may be trained to classify a segmented prostate into a particular size category. Moreover, in some implementations, prostate ML model <NUM>, or another ML model, may be trained to compare a base line ultrasound image of a patient's prostate with a follow-up ultrasound image of the patient's prostate to determine how much the size, shape, and/or volume of the patient's prostate has changed over time. Additionally, in some implementations, prostate ML model <NUM>, or another ML model, may be trained to identify a particular pathology in a patient's prostate, such as a lesion or an area of concern.

In some implementations, bladder ML model <NUM> and/or prostate ML model <NUM> may be trained using ultrasound images obtained via transabdominal imaging. Additionally, or alternatively, bladder ML model <NUM> and/or prostate ML model <NUM> may be trained using a training set that includes different types of images. As an example, bladder ML model <NUM> and/or prostate ML model <NUM> may be trained using TRUS ultrasound image. As another example, bladder ML model <NUM> and/or prostate ML model <NUM> may be trained using MRI images of bladders and/or prostates. The MRI images may be obtained, for example, from a public MRI database. The obtained MRI images may include labels, such as a shape label, a size label, a medical diagnosis label, a clinically derived value label, and/or another type of label that may be used for supervised learning in training bladder ML model <NUM> and/or prostate ML model <NUM>. The labels may enable extraction of image independent features that may be used by bladder ML model <NUM> and/or prostate ML model <NUM>. For example, the labels may be used to obtain a statistical shape model of a particular type of bladder or prostate (e.g., based on images from a database of MRI bladder and/or prostate images) to facilitate training bladder ML model <NUM> and/or prostate ML model <NUM>. Furthermore, in some implementations, bladder ML model <NUM> and/or prostate ML model <NUM> may implement any combination of machine learning with algorithmic image processing techniques. Moreover, in some implementations, bladder ML model <NUM> and/or prostate ML model <NUM> may include a "manual caliper" option to "manually" adjust the segmentation output of a machine learning model. For example, a user may be presented with a user interface to enable the user to adjust the boundary of a bladder and/or the boundary of a prostate, identified by bladder ML model <NUM> or prostate ML model <NUM>, by manipulating a pointer on the display or using keypad commands to adjust the identified boundary. As another example, the user interface may enable the user to manually select a set of seed points on an image and the set of seed points may be used by bladder ML model <NUM> or prostate ML model <NUM> to perform a segmentation.

Size/volume/shape calculator <NUM> may calculate the size, volume, and/or shape of the patient's prostate based on an identified boundary, set of boundaries, and/or generated surface of the patient's prostate. As an example, size/volume/shape calculator <NUM> may calculate an area enclosed within an identified boundary in an ultrasound image to determine a cross-sectional area of the prostate in a particular plane. Additionally, or alternatively, size/volume/shape calculator <NUM> may determine the size of the prostate in a particular direction (e.g., the width, length, height, etc.). Furthermore, size/volume/shape calculator <NUM> may determine a volume of a patient's prostate based on a boundary generated using a set of ultrasound images from a 3D scan. Alternatively, size/volume/shape calculator <NUM> may add up a set of areas of different cross-sections, and use a distance between the different cross-sections to determine the thickness of each cross-section, to compute a volume for the patient's prostate.

Shape analyzer <NUM> may analyze a determined shape of the patient's prostate and/or the shape of another segmented structure, such as the patient's bladder. As an example, shape analyzer <NUM> may determine whether the determined shape of the patient's prostate falls within a normal/healthy range of shapes for a particular age group and/or whether the determined shape is indicative of a lesion. As another example, shape analyzer <NUM> may compute a IPP volume for the patient's prostate based on the determined shape of the prostate and based on the determined bladder boundary for the patient.

Recommendation manager <NUM> may generate a recommendation based on information received from prostate ML model <NUM>, size/volume/shape calculator <NUM>, and/or shape analyzer <NUM> based on a change in the size, shape, and/or volume of the patient's prostate from a previous visit based on a comparison of ultrasound scans, based on a detected lesion or area of concern, based on the determined size/volume/shape of the patient's prostate combined with a clinically derived value (e.g., a PSA value, etc.), and/or based on other obtained or generated information.

As an example, recommendation manager <NUM> may generate a recommendation for a medical intervention based on the determined size, volume, or shape of the patient's prostate, based on a PSA/volume value, and/or based on another determination. For example, recommendation manager <NUM> may recommend additional imaging, a biopsy, a resection, and/or another type of medical procedure. As another example, recommendation manager <NUM> may generate a risk prediction based on the determined size, volume, and/or shape of the patient's prostate. For example, recommendation manager <NUM> may generate a quantitative clinical grading value that indicates to what extent the patient's prostate will likely increase in size, cause urine retention and/or another type of symptom, or develop into a particular medical condition.

Additionally, recommendation manager <NUM> may generate a recommendation to improve measurement of the determined size, volume, and/or shape of the patient's prostate. The recommendation may include at least one of a recommendation to adjust a position or angle of the ultrasound probe, a recommendation to the patient to drink water or another fluid, or a recommendation to address a detected artefact or interference in an ultrasound image.

Moreover, recommendation manager <NUM> may use the determined size, volume, and/or shape of the patient's prostate to predict a change in the size of the patient's prostate in the future (e.g., over an upcoming number of years, etc.), to predict an outcome of a medical prostate reduction treatment, and/or to predict other aspects relating to the patient's prostate.

Although <FIG> shows exemplary components of controller unit <NUM>, in other implementations, controller unit <NUM> may include fewer components, different components, additional components, or differently arranged components than depicted in <FIG>. Additionally, or alternatively, one or more components of controller unit <NUM> may perform one or more tasks described as being performed by one or more other components of controller unit <NUM>. As an example, in some implementations, a single ML model may be trained to perform segmentation on both the bladder and the prostate. The single ML model may calculate the size/volume/shape of the bladder and the size/volume/shape of the prostate and/or output one or more recommendations based on the calculated size/volume/shape of the bladder and/or prostate. As another example, controller unit <NUM> may include additional functional components for characterizing other organs or tissues. For example, controller unit <NUM> may include a bladder mode to scan and assess the size, volume, and/or shape of a patient's bladder, and a prostate mode to scan and assess the size, volume, and/or shape of the patient's prostate. A user may be able to toggle between the bladder mode and the prostate mode by pressing a button, activating a selection object on a touchscreen, speaking a command, and/or performing another type of action.

<FIG> is a flowchart of a process for analyzing ultrasound images of a prostate using machine learning according to an implementation described herein. In some implementations, the process of <FIG> may be performed by ultrasound system <NUM>. In other implementations, some or all of the process of <FIG> may be performed by another device or a group of devices separate from ultrasound system <NUM>.

The process of <FIG> may include obtaining an ultrasound image of a patient's lower abdominal area using an ultrasound probe (block <NUM>), identifying an anatomical landmark in the obtained ultrasound image (block <NUM>), and aiming the ultrasound probe to transabdominally image the patient's prostate using the identified anatomical landmark as a reference point (block <NUM>). For example, an operator may select to initiate an ultrasound scan of a patient's prostate using controller unit <NUM>. The user may be instructed to place ultrasound probe <NUM> over the patient's bladder area and to initiate a scan. Ultrasound probe <NUM> may capture one or more ultrasound images of the patient's bladder.

Bladder ML model <NUM> may perform segmentation to identify the back wall of a bladder and aiming manager <NUM> may determine an aiming zone for ultrasound probe <NUM> based on the identified anatomical landmark, such as, for example, the identified back wall of the bladder. User interface <NUM> may display an ultrasound image that includes the identified back wall of the bladder and the determined aiming zone. The operator may then aim ultrasound probe <NUM> to point into the determined aiming zone. Additionally, or alternatively, controller unit <NUM> may generate a set of instructions to assist the operator in aiming ultrasound probe <NUM> within the determined aiming zone. In other implementations, a different anatomical landmark may be used, such as a different wall of the bladder, a wall of the rectum, the pubic bone, the prostate itself, and/or another type of anatomical landmark in the prostate region of the patient's body. In some implementations, controller unit <NUM> may additionally automatically adjust the depth of field of ultrasound probe <NUM> based on the determined aiming zone.

Transabdominal ultrasound image(s) of the patient's prostate may be obtained using the aimed ultrasound probe (block <NUM>). For example, controller unit <NUM> may generate an indication that ultrasound probe <NUM> is properly aligned to scan the patient's prostate and the operator may initiate an ultrasound scan of the patient's prostate. For example, the operator may initiate a 3D scan of the patient's prostate and ultrasound probe <NUM> may capture ultrasound images in a set of planes (e.g., a set of radial planes around a centerline of ultrasound probe <NUM>, et. The operator may select to capture B-mode ultrasound images (e.g., fundamental, harmonic and/or compounded, etc.), P-mode images, Doppler ultrasound images, M-mode images, elastography, and/or other types of ultrasound images. As an example, Color Doppler may be used to image normal and abnormal flow of fluids (e.g., blood flow) within the prostate. As another example, elastography may be used to determine tissue stiffness, as areas of different stiffness may be associated with a pathology and may be used for prostate cancer detection and characterization.

While above example describes imaging the bladder first and using an anatomical landmark associated with the bladder (e.g., the back wall of the bladder) to aim ultrasound probe <NUM>, followed by imaging of the prostate, imaging of the bladder may not be needed to be performed first. For example, the patient's prostate and bladder area may be imaged at the same time or the patient's prostate may be imaged directly. For example, ultrasound system <NUM> may aim ultrasound probe <NUM> by identifying the prostate in a captured ultrasound image, aim ultrasound probe <NUM> to direct more ultrasound energy directly to the identified prostate, followed by a more detailed scan (e.g., a 3D scan) of the identified prostate using the aimed ultrasound probe <NUM>. Furthermore, in some implementations, ultrasound probe <NUM> may have a large field of view that does not require aiming and the patient's prostate may be scanned without aiming ultrasound probe <NUM>.

Segmentation may be performed using a trained machine learning model to identify a boundary of the prostate in the obtained ultrasound image(s) (block <NUM>). For example, prostate ML model <NUM>, and/or another trained ML model, may perform segmentation to identify the boundaries of the prostate in a set of 2D ultrasound images. Prostate ML model <NUM> may then stitch together or otherwise combine the boundaries from the individual ultrasound images to generate a 3D surface of the prostate. In some implementations, prostate ML model <NUM> may classify a segmented prostate into a particular size category (e.g., small, normal, enlarged, etc.). Additionally, prostate ML model <NUM> may compare an identified boundary of the patient's prostate with previously determined boundary of the patient's prostate during a previous visit (e.g., by retrieving ultrasound images associated with the patient's records). Moreover, in some implementations, prostate ML model <NUM>, or another ML model, may determine whether the patient's prostate includes a lesion or an area of concern based on, for example, identifying a boundary of an area within the prostate that reflected ultrasound signals differently from other areas of the prostate by at least a threshold amount. Furthermore, controller unit <NUM> may provide a user interface to enable a user to manually adjust the identified boundary of the prostate or to define a set of points for performing the segmentation process.

The size, volume, and/or shape of the patient's prostate may be determined using the identified boundary (block <NUM>) and shape analysis may be performed (bock <NUM>). For example, controller unit <NUM> may calculate an area enclosed within the identified boundary in an ultrasound image to determine a cross-sectional area of the prostate in a particular plane and may add up a set of areas of different cross-sections, and use a distance between the different cross-sections to determine the thickness of each cross-section, to compute a volume for the patient's prostate. Alternatively, controller unit <NUM> may calculate a volume within a 3D prostate boundary generated based on a collection of ultrasound images obtained during a 3D scan. Controller unit <NUM> may also determine the size and/or shape of the prostate in a particular direction (e.g., the width, length, height, etc.) and/or may determine if the shape of the prostate falls outside a range or normal shapes or is associated with a particular pathology. Furthermore, controller unit <NUM> may generate one or more clinical values based on the determined size, volume, and/or shape of the patient's prostate, such as a PSA/volume value, an IPP value, and/or another type of clinical value. As an example, controller unit <NUM> may calculate an IPP value based on the determined size, volume, or shape of the patient's prostate. As another example, controller unit <NUM> may calculate a PSA to volume ratio based on the determined size, volume, or shape of the patient's prostate combined with a clinically derived PSA level for the patient (e.g., determined via a blood test, etc.).

A recommendation for a medical intervention may be generated based on the determined size, volume, and/or shape of the patient's prostate (block <NUM>). For example, controller unit <NUM> may determine whether the size, volume, and/or shape of the patient's prostate is within a particular range for the patient's age group and/or may determine a value based on the determined size/volume/shape of the prostate combined with a clinically derived value (e.g., a PSA/volume value, etc.). Furthermore, controller unit <NUM> may compare the determined size, volume, and/or shape of the patient's prostate to a size, volume, and/or shape determined during a previous visit to calculate a change in the size, volume, and/or shape over time. Additionally, controller unit <NUM> may determine whether any lesions or areas of concern have been detected. For example, an elastography scan may identify an area of higher stiffness which may indicate a tumor. Thus, controller unit <NUM> may generate a recommendation for additional imaging, a biopsy, a resection, and/or another type of medical procedure.

A risk prediction may be generated based on the determined size, volume, and/or shape of the patient's prostate (block <NUM>). For example, controller unit <NUM> may generate a quantitative clinical grading value that indicates to what extent the patient's prostate will likely increase in size, cause urine retention and/or another type of symptom, or develop into a particular medical condition. Additionally, controller unit <NUM> may use the determined size, volume, and/or shape of the patient's prostate to predict a change in the size of the patient's prostate in the future (e.g., over an upcoming number of years, etc.), to predict an outcome of a medical prostate reduction treatment, and/or to predict other aspects relating to the patient's prostate.

A recommendation to improve measurement of the size, volume, and/or shape of the patient's prostate may be generated (block <NUM>). As an example, controller unit <NUM> may determine that the calculated size, volume, and/or shape of the patient's prostate is outside an expected range and may determine that ultrasound probe <NUM> was not positioned correctly. In response, controller unit <NUM> may generate a recommendation, presented via display <NUM>, to adjust a position or angle of the ultrasound probe. As another example, controller unit <NUM> may determine that the ultrasound signals are not travelling through the bladder and are experience too much reflection in the bladder. For example, controller unit <NUM> may determine that the patient's bladder is too empty based on a determined size of the bladder. In response, controller unit <NUM> may generate a recommendation for the patient to drink water or another fluid to increase the fluid in the bladder, which may result in better imaging of the prostate. As yet another example, controller unit <NUM> may detect an artefact or interference in an ultrasound image. For example, prostate ML model <NUM> may be trained to detect colon gas. Alternatively, controller unit <NUM> may assume colon gas is present if the size of the patient's prostate is determined to be outside an expected range due to the colon being misinterpreted as part of the prostate. In response, controller unit <NUM> may generate a recommendation to the patient to remove bowel gas.

<FIG> is a diagram illustrating segmentation of a bladder, identification of an aiming zone, and segmentation of a prostate according to an implementation described herein. Ultrasound image <NUM>, which may be presented on display <NUM>, may include an identified boundary <NUM> of the patient's bladder as determined through segmentation by bladder ML model <NUM>. Ultrasound image <NUM>, presented subsequently, may include an identified aiming zone <NUM> for ultrasound probe <NUM>. Aiming zone <NUM> may be determined based on the back wall of identified boundary <NUM> and/or another identified anatomical landmark, such as a side wall of the bladder, a pubic bone shadow, the prostate itself, and/or another type of anatomical landmark. Controller unit <NUM> may then optimize the settings of ultrasound probe <NUM> (e.g., by adjusting the depth of field) to capture ultrasound images in aiming zone <NUM>. Furthermore, the operator may be instructed to move/tilt ultrasound probe <NUM> to optimize image capture in aiming zone <NUM>. Ultrasound image <NUM> may be presented on display <NUM> after the patient's prostate has been imaged using aiming zone <NUM> and segmentation has been performed by prostate ML model <NUM> to identify boundary <NUM> of the patient's prostate.

<FIG> is a diagram <NUM> illustrating determination of the size of a prostate according to an implementation described herein. As shown in <FIG>, diagram <NUM> includes an ultrasound image of a patient's prostate in which the size of the prostate has been determined in two different dimensions based on segmentation performed by prostate ML model <NUM>. For example, prostate ML model <NUM> may determine a width <NUM> of the prostate and a height <NUM> of the prostate in a particular plane in which the ultrasound image has been captured.

<FIG> is a diagram <NUM> illustrating determination of the volume of a prostate from 3D ultrasound scan according to an implementation described herein. As shown in <FIG>, diagram <NUM> illustrates a generated 3D boundary surface of a patient's prostate based on a set of ultrasound images obtained during a 3D scan. A first ultrasound image <NUM> includes a boundary <NUM> of the prostate in a coronal plane, a second ultrasound image <NUM> includes a boundary <NUM> of the prostate in a sagittal plane, and a third ultrasound image <NUM> includes a boundary <NUM> of the prostate in a transverse plane.

<FIG> is a diagram <NUM> illustrating the use of elastography for prostate characterization according to an implementation described herein. As shown in <FIG>, diagram <NUM> illustrates an ultrasound image <NUM> that has been correlated with an elastography image <NUM> processed using segmentation to identify areas with different values of tissue stiffness, with different colors and/or shading values representing different tissue stiffness values. Thus, prostate ML model <NUM> may be trained to perform segmentation on elastography images to identify areas associated with a particular tissue stiffness and/or to identify a particular pathology associated with a particular tissue stiffness. Furthermore, prostate ML model <NUM> may be trained to perform histological, morphological, and/or sonographic characterization of a prostate based on B-mode brightness, elastography images, and/or Doppler ultrasound images. As an example, prostate ML model <NUM> may be trained to identify calcium deposits, adenomas, and/or other types of abnormalities. As another example, prostate ML model <NUM> may be trained to identify an outer peripheral zone and/or inner transition zone of the prostate.

<FIG> is a diagram illustrating a first exemplary user interface <NUM> according to an implementation described herein. As shown in <FIG>, user interface <NUM> may be presented on display <NUM> of controller unit <NUM> during aiming of ultrasound probe <NUM>. User interface <NUM> may present a real-time graphical representation of detected body organs/structures to aid an operator in centering ultrasound probe <NUM> for a more accurate measurement. Such a graphical representation of obtained ultrasound data may be used to center ultrasound probe <NUM>. In such a display mode, display <NUM> may include a reticle <NUM> representing the current aiming direction of ultrasound probe <NUM>, bladder projection <NUM> representing the size and position of the determined largest cross-section of the patient's bladder, and prostate projection <NUM> representing the determined the size and position of the determined largest cross-section of the patient's prostate. As the operator moves ultrasound probe <NUM> around, the positions of reticle <NUM>, bladder projection <NUM>, and prostate projection <NUM> may be updated. When the operator is satisfied with the alignment and position of ultrasound probe <NUM>, the operator may activate scan button <NUM> to perform an ultrasound scan.

<FIG> is a diagram illustrating a second exemplary user interface <NUM> according to an implementation described herein. As shown in <FIG>, user interface <NUM> may be presented on display <NUM> of controller unit <NUM> and may include a captured ultrasound image <NUM>. Ultrasound image <NUM> may include a prostate that is obscured due to gas in the colon. Controller unit <NUM> may detect the presence of colon gas as a result of poor segmentation of the prostate (e.g., an inability to identify the boundary of the prostate), as a result of detecting a boundary classified as a colon boundary, and/or using another technique. In response, controller unit <NUM> may generate a recommendation <NUM> to have the patient eliminate bowel gas.

<FIG> is a diagram illustrating a third exemplary user interface <NUM> according to an implementation described herein. As shown in <FIG>, user interface <NUM> may be presented on display <NUM> of controller unit <NUM> and may include a captured ultrasound image <NUM>. In the example of <FIG>, controller unit <NUM> may correspond to a tablet computer device communicating with ultrasound probe <NUM> using a wireless connection. Ultrasound image <NUM> may include an identified prostate boundary <NUM>. Additionally, ultrasound image <NUM> may include an identified lesion boundary <NUM>. In response, controller unit <NUM> may generate a recommendation <NUM> to perform a biopsy on the lesion.

<FIG> is a diagram illustrating a fourth exemplary user interface <NUM> according to an implementation described herein. As shown in <FIG>, user interface <NUM> may be presented in display <NUM> of controller unit <NUM> and may include a recommendation generated based on a determined clinical value. In the example of <FIG>, controller unit <NUM> may correspond to a tablet computer device communicating with ultrasound probe <NUM> using a wireless connection. User interface <NUM> may include a graphical representation <NUM> of a computed IPP value. Graphical representation <NUM> may include a determined bladder boundary <NUM>, a determined prostate boundary <NUM>, and a computed IPP value <NUM> based on the determined bladder boundary <NUM> and prostate boundary <NUM>. User interface <NUM> may further include an analysis <NUM> based on the computed IPP value <NUM>. For example, analysis <NUM> may indicate that a bladder output obstruction (BOO) is likely based on the computed IPP value <NUM>.

In the preceding specification, various preferred embodiments have been described with reference to the accompanying drawings. It will, however, be evident that various modifications and changes may be made thereto, and additional embodiments may be implemented, without departing from the broader scope of the invention as set forth in the claims that follow. The specification and drawings are accordingly to be regarded in an illustrative rather than restrictive sense.

For example, while a series of blocks have been described with respect to <FIG>, the order of the blocks may be modified in other implementations. Further, non-dependent blocks may be performed in parallel.

It will be apparent that systems and/or methods, as described above, may be implemented in many different forms of software, firmware, and hardware in the implementations illustrated in the figures. The actual software code or specialized control hardware used to implement these systems and methods is not limiting of the embodiments. Thus, the operation and behavior of the systems and methods were described without reference to the specific software code--it being understood that software and control hardware can be designed to implement the systems and methods based on the description herein.

Further, certain portions, described above, may be implemented as a component that performs one or more functions. A component, as used herein, may include hardware, such as a processor, an ASIC, or a FPGA, or a combination of hardware and software (e.g., a processor executing software).

It should be emphasized that the terms "comprises" / "comprising" when used in this specification are taken to specify the presence of stated features, integers, steps or components but does not preclude the presence or addition of one or more other features, integers, steps, components or groups thereof.

The term "logic," as used herein, may refer to a combination of one or more processors configured to execute instructions stored in one or more memory devices, may refer to hardwired circuitry, and/or may refer to a combination thereof. Furthermore, a logic may be included in a single device or may be distributed across multiple, and possibly remote, devices.

For the purposes of describing and defining the present invention, it is additionally noted that the term "substantially" is utilized herein to represent the inherent degree of uncertainty that may be attributed to any quantitative comparison, value, measurement, or other representation. The term "substantially" is also utilized herein to represent the degree by which a quantitative representation may vary from a stated reference without resulting in a change in the basic function of the subject matter at issue.

Claim 1:
A method performed by a computing device, the method comprising:
obtaining (<NUM>) an ultrasound image of a patient's bladder area;
identifying (<NUM>), by the computing device, an anatomical landmark in the ultrasound image;
determining, by the computing device, an aiming zone to image the patient's prostate based on the identified anatomical landmark;
aiming (<NUM>), by the computing device, the ultrasound probe to transabdominally image the patient's prostate based on the determined aiming zone;
obtaining (<NUM>), by the computing device, a transabdominal image of the patient's prostate using the aimed ultrasound probe; and
performing (<NUM>), by the computing device, a segmentation process on the obtained transabdominal image of the patient's prostate using a machine learning model to identify a boundary of the prostate in the transabdominal image of the patient's prostate.