Patent Publication Number: US-11647990-B2

Title: Implant assessment using ultrasound and optical imaging

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This patent application claims benefit of priority to U.S. Provisional Application No. 62/775,650, entitled “IMPLANT ASSESSMENT USING ULTRASOUND AND OPTICAL IMAGING” and filed on Dec. 5, 2018, which is hereby incorporated herein by reference in its entirety. 
    
    
     BACKGROUND INFORMATION 
     An ultrasound probe may generate ultrasound signals using a transducer, such as, for example, a piezoelectric transducer or a capacitive transducer, which converts electrical signals into ultrasound energy and which converts ultrasound echoes back into electrical signals. Ultrasound probes are typically used to identify a target organ or other structures in the body and/or determine features associated with the target organ/structure, such as the size of the organ/structure or the volume of fluid in the organ. For example, an ultrasound probe may be used to generate an ultrasound image of a region of interest and the ultrasound image may be processed or analyzed to identify a particular structure in the region of interest. Identifying particular structures in an ultrasound image may present various challenges. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIGS.  1 A and  2 B  are diagrams comparing a normal breast implant with a breast implant that exhibits capsular contracture according to an implementation described herein; 
         FIG.  1 C  is a diagram of a table illustrating Baker class values for breast implants according to an implementation described herein; 
         FIG.  2 A  is a diagram illustrating a first exemplary ultrasound system according to an implementation described herein; 
         FIG.  2 B  is a diagram illustrating a second exemplary ultrasound system according to an implementation described herein; 
         FIGS.  3 A- 3 D  are diagrams of first, second, third, and fourth exemplary ultrasound probes, respectively, according to implementations described herein; 
         FIGS.  4 A- 4 C  are diagrams of first, second, and third exemplary cameras, respectively, according to implementations described herein; 
         FIG.  5    is a diagram illustrating exemplary components of a device that included in one or more components of the system of  FIGS.  2 A and  2 B ; 
         FIG.  6    is a diagram illustrating exemplary functional components of the system of  FIGS.  1 A and  1 B ; 
         FIG.  7    is a flowchart of a process for implant assessment using machine learning according to an implementation described herein; 
         FIG.  8    is a flowchart of a process for characterizing an implant capsule using machine learning according to an implementation described herein; 
         FIG.  9    is a flowchart of a process for characterizing the morphology or integrity of an implant using machine learning according to an implementation described herein; 
         FIGS.  10 A,  10 B,  10 C, and  10 D  are diagrams illustrating the use of a sector grid for performing an ultrasound scan according to an implementation described herein; 
         FIG.  11    is a diagram illustrating the use of elastography for implant assessment according to an implementation described herein; 
         FIG.  12 A  is a diagram illustrating the use of a three-dimensional (3D) mesh on a depth camera image according to an implementation described herein; 
         FIG.  12 B  is a diagram illustrating a curvature plot on a depth camera image according to an implementation described herein; 
         FIGS.  13 A,  13 B, and  13 C  are diagrams illustrating segmented ultrasound scans of an implant capsule according to an implementation described herein; 
         FIGS.  14 A,  14 B, and  14 C  are diagrams illustrating the characterization of an implant capsule according to an implementation described herein; 
         FIG.  15    is a diagram illustrating generation of a 3D shape from a series of segmented ultrasound images according to an implementation described herein; 
         FIGS.  16 A and  16 B  are diagrams illustrating the characterization of capsule ruptures according to an implementation described herein; 
         FIGS.  17 A and  17 B  are diagrams illustrating the characterization of fluid collections according to an implementation described herein; 
         FIGS.  18 A and  18 B  are diagrams illustrating exemplary machine learning models according to an implementation described herein; 
         FIG.  19 A  is a diagram illustrating a first exemplary user interface according to an implementation described herein; 
         FIG.  19 B  is a diagram illustrating a second exemplary user interface according to an implementation described herein; and 
         FIG.  19 C  is a diagram illustrating a third exemplary user interface according to an implementation described herein. 
     
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     The following detailed description refers to the accompanying drawings. The same reference numbers in different drawings identify the same or similar elements. 
     Hundreds of thousands of breast augmentation surgeries are performed each year after mastectomies, for corrections of congenital defects, or for cosmetic reasons. A breast augmentation surgery may include the placement of a breast implant into a patient&#39;s breast. For example, a breast implant may include a saline implant, a silicone implant, a cohesive gel implant (also referred to as a “gummy bear” implant), or a fat transfer implant. A patient may experience complications after breast implant surgery. 
     One complication that may result from breast implant surgery is a rupture of the implant capsule. A rupture may result in spilling of implant contents, such as, for example, silicone, into surrounding tissue area (referred to as extracapsular rupture), or the spilling of implant contents from one compartment of the implant into another compartment (referred to as intracapsular rupture). Another complication that may result from breast implant surgery is capsular contracture. While ruptures may occur infrequently, some studies indicate that capsular contracture may occur in as many as 20% of patients. 
       FIG.  1 A  is a diagram  101  of side views comparing a normal breast implant  110  to a breast implant with capsular contracture  120 . As shown in  FIG.  1 A , capsular contracture  120  results in contracting the shape of the breast implant, resulting in a deformation of the breast. The capsular contracture may be caused by tissue accumulation  130  at, or near, the implant membrane of the breast implant. Tissue accumulation  130  may result from a thickening of the fibrous capsule that grows around the implant membrane, which may result from excessive fibrotic foreign body reaction to the implant by fibroblasts, macrophages, and lymphocytes. Additionally, or alternatively, a biofilm may form on the implant membrane from bacterial growth, which may cause an exaggerated inflammatory response. The implant membrane, together with the tissue accumulation  130 , may be referred to as an implant capsule. 
       FIG.  1 B  is a diagram  102  of frontal views comparing a patient  140  with a normal breast implant during an initial visit to obtain baseline images and with the patient  140  experiencing capsular contracture during a follow-up visit. Patient  140  may have a breast implant  142  with a normal implant capsule during the initial visit, as shown in corresponding B-mode (i.e., 2-dimensional brightness mode) ultrasound image  144  with segmentation performed to identify the boundaries of the implant capsule. Ultrasound image  144  may include an implant capsule  146  that has a normal thickness and does not exhibit radial folds. During the follow-up visit, patient  140  may have a breast implant  152  that has experienced capsular contracture, as shown in corresponding B-mode ultrasound image  154  with segmentation performed to identify the boundaries of the implant capsule. Ultrasound image  154  may include a thickened implant capsule  156 . Furthermore, implant capsule  156  may exhibit radial folds due to the capsular contracture, giving implant capsule  156  a wavy appearance. 
     Furthermore, as shown in  FIG.  1 B , the capsular contracture of breast implant  152  may cause visible deformation of the breast of patient  140 . For example, if the capsular contracture is severe toward the bottom of the breast, breast implant  152  may be pushed upward. Similarly, if the capsular contracture is severe on the medial side of the breast, breast implant  152  may be pushed laterally (not shown in  FIG.  1 B ). Thus, capsular contractures of breast implants may result in implant deformation, a thickening of the implant capsule, and/or an increased number of radial folds in the implant capsule. The deformation may be detected through visual inspection and the thickening of the implant capsule and/or the increased number of radial folds may be detected via ultrasound images of the implant capsule. 
     Capsular contractures of breast implants may differ in severity. The severity of capsular contracture may be measured using a Baker class value.  FIG.  1 C  is a diagram of a table  160  illustrating Baker class values for breast implants according to an implementation described herein. As shown in  FIG.  1 C , table  160  includes a Baker class value column  162 , a firmness/pain column  164 , an implant palpability column  166 , and an implant visibility column  168 . Baker class value column  162  identifies a particular Baker class. Firmness/pain column  164  identifies a corresponding firmness and/or pain description associated with the particular Baker class. Implant palpability column  166  identifies a corresponding palpation description associated with the particular Baker class. Implant visibility column  168  identifies a corresponding visibility description associated with the particular Baker class. Thus, a clinical evaluation by a health care provider may help identify a Baker class associated with a patient&#39;s breast implant. 
     The Baker class may be used to select a medical intervention for the patient. For example, Baker class values of 1 and 2 may be recommended for non-surgical options, such as massage, vitamin E supplementations, steroid injections, or drugs, such as leukotriene pathway inhibitors, while Baker class values of 3 and 4 may be recommended for surgical intervention, such as capsulotomy, capsulectomy, autologous fat transfer, or implant replacement. Thus, an accurate determination of the Baker class may be important in selecting the most appropriate medical intervention. 
     However, the clinical evaluation by itself may not be sufficient to confidently determine the Baker class or determine an appropriate medical intervention. Furthermore, other types of quantitative clinical grading may offer more information about the type of medical intervention that may be appropriate. Therefore, medical imaging may be used in addition to a visual and palpatory assessment by a health care provider. For example, Magnetic Resonance Imaging (MRI) may be recommended to assess the status of the breast implant. However, MRI imaging may be costly and require technical expertise to interpret. 
     Implementations described herein relate to implant assessment using ultrasound and optical imaging. A system may include a controller unit that includes a user interface and is in communication with an ultrasound probe for capturing ultrasound images and a camera for capturing optical images. The controller unit may be configured to obtain baseline ultrasound images, captured using the ultrasound probe, of a patient&#39;s breast area and to obtain baseline optical images, captured using the camera, of the patient&#39;s breast area. The controller unit may be further configured to obtain, to a later time, such as during a subsequent patient follow-up visit, follow-up ultrasound images using the ultrasound probe and follow-up optical images using the camera. 
     The controller unit may be further configured to use one or more machine learning models to compare the baseline ultrasound images with the follow-up ultrasound images and to compare the baseline optical images with the follow-up optical images, to detect a change in a morphology or integrity of the patient&#39;s breast area based on the comparisons, and to generate a recommendation for a medical intervention based on the detected change. 
     In some implementations, the controller unit may be further configured to obtain baseline three-dimensional (3D) scan images of the patient&#39;s breast area using a depth camera, to obtain follow-up 3D scan images of the patient&#39;s breast area using the depth camera, to use the one or more machine learning models to compare the baseline 3D scan images with the follow-up 3D scan images, and to further detect the change in the morphology or integrity of the patient&#39;s breast area based on the comparison of the baseline 3D scan images with the follow-up 3D scan images. 
     In some implementations, the controller unit may be further configured to obtain baseline elastography images of the patient&#39;s breast area using the ultrasound probe, to obtain follow-up elastography images of the patient&#39;s breast area using the ultrasound probe, to use the one or more machine learning models to compare the baseline elastography images with the follow-up elastography images, and to further detect a change in the morphology, integrity, and/or stiffness of the patient&#39;s breast area based on the comparison of the baseline elastography images with the follow-up elastography images. 
     In some implementations, the controller unit may be further configured to obtain baseline thermal camera images of the patient&#39;s breast area using a thermal camera, to obtain follow-up thermal camera images of the patient&#39;s breast area using the thermal camera, to use the one or more machine learning models to compare the baseline thermal camera images with the follow-up thermal camera images, and to further detect a change in the morphology, integrity, and/or temperature variation of the patient&#39;s breast area based on the comparison of the baseline thermal camera images with the follow-up thermal camera images. 
     Thus, in some implementations, the controller unit may be configured to compare only ultrasound images; in other implementations, the controller unit may be configured to compare ultrasound and optical images; in yet other implementations, the controller unit may be configured to compare ultrasound, optical, and 3D scan images; and in yet other implementations, the controller unit may be configured to compare ultrasound, optical, 3D scan, and thermal camera images. 
     Furthermore, in some implementations, the controller unit may be further configured to use the one or more machine learning models to perform a segmentation of an implant capsule for an implant associated with the patient&#39;s breast area. The controller unit may further determine a thickness of the implant capsule and/or determine a radial fold count for the implant capsule. Moreover, in some implementation, the controller unit may further be configured to use the one or more machine learning models to determine whether the patient&#39;s breast area is associated with an extracapsular rupture, an intracapsular rupture, capsular contracture, and/or a fluid collection, such as a seroma, abscess, or a hematoma. 
     In some implementations, a machine learning model may be trained to output a quantitative clinical grading value for the morphology or integrity of the patient&#39;s breast area based on the comparisons. The quantitative clinical grading value may include, for example, a Baker class value, an implant capsule thickness value, a radial fold count value, an implant capsule smoothness value, and/or another type of quantitative clinical grading value. 
     In some implementations, the controller unit may be further configured to obtain a 3D scan image of the patient&#39;s breast area using one or more depth cameras, generate a 3D geometrical feature plot using the obtained 3D scan image, and use the generated 3D geometrical feature plot as an input into the one or more machine learning models. The 3D geometrical feature may include, for example, curvature, symmetry, smoothness, distance from a particular location, and/or another type of 3D geometrical feature. 
     In some implementations, the controller unit may be further configured to use the one or more machine learning models to perform a segmentation of an implant capsule, a rupture area, or a fluid collection in ultrasound images of the patient&#39;s breast area and to use a set of ultrasound images that include the segmentation of the implant capsule, the rupture area, or the fluid collection to generate a three-dimensional (3D) shape of the implant capsule, the rupture area, or the fluid collection. 
     In some implementations, obtaining the ultrasound images may include dividing a breast area into sectors, scanning individual sectors using the ultrasound probe, and indicating, on a user interface, which sectors have been scanned. The user interface may be configured to receive a selection of a scanned sector and to display an ultrasound image associated with the selected scanned sector. 
     In some implementations, implant assessment may be performed without obtaining baseline images. Thus, implant assessment may be performed using one or more machine learning models on only follow-up images for a patient, without baseline images being available for the patient. The follow-up images may include one or more of ultrasound images, optical images, 3D scan images, and/or thermal camera images of the patient&#39;s breast area. For example, the controller unit may be configured to obtain an ultrasound image of a patient&#39;s breast area, obtain at least one of an optical image of the patient&#39;s breast area, a three-dimensional (3D) scan image of the patient&#39;s breast area, or a thermal camera image of the patient&#39;s breast area, and use one or more machine learning models to compare the ultrasound image with the at least one of the optical image of the patient&#39;s breast area, the 3D scan image of the patient&#39;s breast area, or the thermal camera image of the patient&#39;s breast area. The controller unit may then detect a change in a morphology or integrity of the patient&#39;s breast area based on the comparison and generate a recommendation for a medical intervention based on the detected change. Furthermore, in some implementations, the controller unit may perform particular types of processing without using a machine learning model. For example, the controller unit may generate a 3D geometrical feature plot from one or more 3D scan images without using a machine learning model. 
     A machine learning model 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, and/or to classify the image, or an identified feature in the image, into a particular class from a set of classes. 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. 
     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 based on a rectifier activation function. The output layer may include a fully connected layer that generates an output that identifies a feature of interest and/or that classifies an input into a particular class from a set of classes. 
     A CNN may be trained using supervised learning, in which a set of images that has been labeled to identify a feature of interest and/or classified in a particular class from a set of classes. 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. 
     Although implementations described herein refer to scanning a breast area, 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 morphology and/or integrity of an implant in another part of a patient&#39;s body, such as a gluteal muscle implant, a calf muscle implant, a pectoral muscle implant, a deltoid muscle implant, and/or another type of implant. 
       FIG.  2 A  is a diagram illustrating an exemplary system  201  according to an implementation described herein. As shown in  FIG.  2 A , system  201  may include a controller unit  210 , an ultrasound unit  220 , an ultrasound probe cable  225 , a depth camera  230 , and a depth camera cable  235 . 
     Controller unit  210  may house and include one or more processors and/or processing logic configured to process reflected ultrasound energy that is received by ultrasound probe  220  to produce ultrasound images of a scanned anatomical region and to process optical images captured by depth camera  230 . Furthermore, controller unit  210  may include display  215  to enable a user to control the operation of ultrasound probe  220  during an ultrasound scan and/or depth camera  230  while capturing optical images, monitor the progress of an ultrasound scan, view ultrasound images from a scan, view optical images captured by depth camera  230 , activate a machine learning model to detect a change in a morphology or integrity of the patient&#39;s breast area based on obtained ultrasound images and/or optical images, and/or generate a recommendation for a medical intervention based on the changes detected by the machine learning model. For example, display  215  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. 
     Furthermore, display  215  may provide instructions for positioning ultrasound probe  220  relative to a selected anatomical portion of a patient. Alternatively, ultrasound probe  220  may include a small display (not shown in  FIG.  2 A ) that provides instructions for positioning ultrasound probe  220 . Display  215  may also display two-dimensional or three-dimensional images of the selected anatomical region. In some implementations, display  215  may include a graphical user interface (GUI) that allows the user to select various features associated with an ultrasound scan. For example, display  215  may include selection items (e.g., buttons, dropdown menu items, checkboxes, etc.) to select an aiming mode for ultrasound probe  220  and/or to initiate a 3D scan after ultrasound probe  220  has been successfully positioned with respect to a patient&#39;s area of interest. Furthermore, display  215  may include selection items to select particular types of ultrasound images to be obtained, such as B-mode images, probability mode (P-mode) images, Doppler mode ultrasound images (e.g., Power Doppler, Continuous Wave Doppler, Pulsed Wave Doppler, etc.), harmonic mode ultrasound images, motion mode (M-mode) ultrasound images, and/or any other type of imaging modality that uses ultrasound data. 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. 
     Ultrasound probe  220  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  220  may be configured to transmit ultrasound signals in a range that extends from approximately about two megahertz (MHz) to approximately 10 or more MHz (e.g., 18 MHz). In other implementations, ultrasound probe  220  may be configured to transmit ultrasound signals in a different range. Furthermore, ultrasound probe  220  may house one or more motors for controlling the movement of the ultrasound transducer. 
     Moreover, ultrasound probe  220  may be used to obtain elastography ultrasound images. To capture an elastography ultrasound image, also referred to as acoustic radiation force impulse imaging (ARFI), ultrasound probe  220  may generate a focused ultrasound beam to apply pressure to a patient&#39;s tissue via an acoustic radiation force. The extent to which the tissue is displaced along the axis of the beam may indicate tissue stiffness. An elastography ultrasound image may display the measured tissue stiffness at each pixel in the ultrasound image. Additionally, or alternatively, ultrasound probe  220  may be used to obtain shear-wave elasticity imaging (SWEI) elastography ultrasound images, in which shear waves caused by the acoustic radiation force are measured. 
     Ultrasound probe  220  may include a trigger  224  and a handle  226 . A user (e.g., a medical practitioner, etc.) may hold ultrasound probe  220  via handle  226  and press trigger  224  to activate one or more ultrasound transceivers to transmit ultrasound signals toward a patient&#39;s area of interest (e.g., a breast area, a particular body organ, a body joint, a blood vessel, etc.). To scan an area of interest, ultrasound probe  220  may be positioned against a surface portion of a patient that is proximate to the anatomical portion to be scanned. The user may apply acoustic gel, or gel pads, to the skin of the patient over the area of interest to provide an acoustical impedance match when ultrasound probe  220  is placed against the skin. The user may then activate a 3D scan of the area of interest by pressing trigger  124 , by pressing a scan button on display  215 , by speaking a voice command, and/or using another type of scan activation technique. Ultrasound probe  220  may then transmit ultrasound signals through the area of interest and may receive reflected ultrasound signals. The reflected ultrasound signals may be processed into images that are displayed on display  215  and/or stored by controller unit  210 . 
     Activation of trigger  224  initiates an ultrasound scan of a selected anatomical portion while ultrasound probe  220  is in contact with a surface portion of a patient&#39;s body when the patient&#39;s area of interest is scanned. Ultrasound probe  220  may communicate with controller unit  210  via a wired connection, such as via cable  225 . In other implementations, ultrasound probe  220  may communicate with controller unit  210  via a wireless connection (e.g., Bluetooth®, WiFi, etc.). 
     Depth camera  230  may be configured to capture optical images that include depth information. For example, for each pixel, depth camera  230  may obtain color information (e.g., RGB values) and a depth value. Thus, depth camera  230  may be configured to obtain optical images that include RGB information for each pixel, 3D scan images that include depth information for each pixel, and/or a combined optical and 3D scan image that includes RBG-D information for each pixel. Depth camera  230  may communicate with controller unit  210  via a wired connection, such as via cable  235 . In other implementations, depth camera  230  may communicate with controller unit  210  via a wireless connection (e.g., Bluetooth, WiFi, etc.). 
       FIG.  2 B  is a diagram illustrating an exemplary system  202  according to another implementation described herein. As shown in  FIG.  2 B , system  202  may include controller unit  210 , ultrasound probe  220 , depth camera  230 , and a thermal camera  240 . In system  202 , controller unit  210  may be implemented in a tablet computer device and ultrasound unit  220  and depth camera  230  may communicate with controller unit  210  using short-range wireless signals, such as, for example, Bluetooth®, WiFi, ZigBee, and/or another short-range wireless communication standard. 
     Thermal camera  240  may include a handle  242  and a set of thermal sensors  244 . Thermal camera  240  may be configured to capture thermal camera images of a patient&#39;s breast area. For example, thermal sensors  244  may capture infrared (IR) radiation emitted by, and/or reflected from, the patient and generate a thermographic image that indicates the intensity of the emitted/reflected IR radiation at each pixel in the image. 
     Ultrasound probe  220  may include a tracking device  222 , depth camera  230  may include a tracking device  232 , and thermal camera  240  may include a tracking device  242 . Tracking devices  222 ,  232 , and  242  may include, for example, a gyroscope, an electromagnetic tracker, an IR tracker, an optical tracker, and/or another type of tracker, which may be used to keep track of the position of ultrasound probe  220 , depth camera  230 , and thermal camera  240 . Keeping track of the position of ultrasound probe  220 , depth camera  230 , and/or thermal camera  240  while scanning a patient&#39;s breast area may facilitate covering or providing ultrasound signals over the whole breast area to generate a 3D scan. For example, a user may be guided by controller unit  210  to move ultrasound probe  220 , depth camera  230 , and/or thermal camera  240  in particular directions, to hold ultrasound probe  220 , depth camera  230 , and/or thermal camera  240  at particular angles and/or particular distances away from the patient&#39;s breast area, and/or to otherwise adjust ultrasound probe  220 , depth camera  230 , and/or thermal camera  240  during a scan. 
     Although  FIGS.  2 A and  2 B  show exemplary components of systems  201  and  202 , in other implementations, systems  201  and  202  may include fewer components, different components, additional components, or differently arranged components than depicted in FIGS.  2 A and  2 B. Additionally or alternatively, one or more components of systems  201  or  202  may perform one or more tasks described as being performed by one or more other components of systems  201  and  202 . 
     As an example, thermal camera  240  may also be included in system  201 . As another example, in other embodiments, ultrasound probe  220  and/or depth camera  230  may each correspond to a self-contained device that includes a microprocessor housed within ultrasound probe  220 , 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  220  may be used to display the generated images and/or to view other information associated with the operation of ultrasound probe  220 . In yet other implementations, ultrasound probe  220  and/or depth camera  230  may be coupled to a general-purpose computer, such as a laptop and/or a desktop computer (via a wired or wireless connection) that includes software that at least partially controls the operation of ultrasound probe  220 , depth camera  230 , and/or thermal camera  240  that includes software to process image information received from ultrasound probe  220 , depth camera  230 , and/or thermal camera  240 . 
     As another example, while  FIGS.  2 A and  2 B  depict ultrasound probe  220  as a hand-held device, in other implementations, ultrasound probe  220  may not correspond to a hand-held device. In some implementations, ultrasound probe  220  may be mounted to a motor assembly on an articulating arm. A patient may lie down on a table in a supine position and the articulating arm may be lowered on the patient&#39;s breast area, whereupon the motor assembly may move ultrasound probe  220  to scan different sections of the patient&#39;s breast area. In other implementations, ultrasound probe  220  may be mounted to a motor assembly in a recession of a table and a patient may lie down in a prone position with the breast area positioned in the recession, whereupon the motor assembly may move ultrasound probe  220  to scan the patient&#39;s breast area. 
       FIG.  3 A  is a diagram of a first exemplary implementation of ultrasound probe  220  according to an implementation described herein. As shown in  FIG.  3 A , ultrasound probe  220  may include a single transducer element coupled to two rotational motors. In this implementation, ultrasound probe  220  may include a base  310  connected to a dome  315 , a theta motor  320 , a spindle  330 , a phi motor  340 , and a transducer bucket  350  with a transducer  360 . Theta motor  320 , phi motor  340 , and/or transducer  360  may include wired or wireless electrical connections that electrically connect theta motor  320 , phi motor  340 , and/or transducer  360  to controller unit  210 . 
     Dome  315  may enclose transducer bucket  350  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  310  may house theta motor  320  and provide structural support to ultrasound probe  220 . Base  310  may connect to dome  315  and may form a seal with dome  315  to protect the components of ultrasound probe  220  from the external environment. Theta motor  220  may rotate spindle  330  with respect to base  310  in a longitudinal direction with respect to transducer  360 , by rotating around a vertical axis referred to herein as a theta (θ) rotational plane  325 . Spindle  330  may terminate in a shaft  335  and phi motor  340  may be mounted onto shaft  335 . Phi motor  340  may rotate around an axis orthogonal to the theta rotational plane  325  around a horizontal axis referred to herein as a phi (ϕ) rotational plane  345 . Transducer bucket  350  may be mounted to phi motor  340  and may move with phi motor  340 . 
     Transducer  360  may be mounted to transducer bucket  350 . Transducer  360  may include a piezoelectric transducer, a capacitive transducer, and/or another type of ultrasound transducer. Transducer  360 , along with transceiver circuitry associated with transducer  360 , 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  360  may transmit and receive ultrasound signals in a signal direction  365  that is substantially perpendicular to the surface of transducer  360 . 
     Signal direction  365  may be controlled by the movement of phi motor  340  and the orientation of phi motor may be controlled by theta motor  320 . For example, phi motor  340  may rotate back and forth across an angle that is less than 180 degrees to generate ultrasound image data for a particular plane and theta motor  320  may rotate to particular positions to obtain ultrasound image data for different planes. 
     In a 3D scan mode, theta motor  320  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  340  may rotate to obtain ultrasound image data for the particular plane. The movement of theta motor  320  and phi motor  340  may be interlaced in the 3D scan motor. For example, the movement of phi motor  340  in a first direction may be followed by a movement of theta motor  320  from a first plane to a second plane, followed by the movement of phi motor  340  in a second direction opposite to the first direction, followed by movement of theta motor  320  from the second plane to a third plane, etc. Such interlaced movement may enable ultrasound probe  220  to obtain smooth continuous volume scanning as well as improving the rate at which the scan data is obtained. 
       FIG.  3 B  is a diagram of a second exemplary implementation of ultrasound probe  220  according to an implementation described herein. As shown in  FIG.  3 B , ultrasound probe  220  may include a one-dimensional (1D) array of transducer elements coupled to a rotation motor. In this implementation, ultrasound probe  220  may include a base  310  connected to dome  315 , a theta motor  320 , a spindle  330 , and a transducer bucket  370  with a 1D transducer array  375 . Theta motor  320  and/or 1D transducer array  375  may include wired or wireless electrical connections that electrically connect theta motor  320  and/or 1D transducer array  375  to controller unit  210 . 
     Base  310  may house theta motor  320  and provide structural support to ultrasound probe  220 . Base  310  may connect to dome  315  and may form a seal with dome  315  to protect the components of ultrasound probe  220  from the external environment. Theta motor  320  may rotate spindle  330  with respect to base  310  in longitudinal direction with respect to 1D transducer array  375  by rotating around theta rotational plane  325 . Spindle  330  may terminate in transducer bucket  370 . 1D transducer array  375  may be mounted to transducer bucket  370 . 1D transducer array  375  may include a curved 1D array of piezoelectric transducers, capacitive transducers, and/or other types of ultrasound transducers. 1D transducer array  375  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 1D transducer array  375  may transmit and receive ultrasound signals in a particular direction of a set of directions, illustrated as item  376  in  FIG.  3 B . Thus, together, the elements of 1D transducer array  375  may generate ultrasound image data for a particular plane. In a 3D scan mode, theta motor  320  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  375  may obtain ultrasound image data for the particular plane. In some implementations, ultrasound probe  220  may not include theta motor  320  and/or dome  315 . For example, ultrasound probe  220  may correspond to a hand-held probe that is moved manually by a user to different positions. 
       FIG.  3 C  is a diagram of a third exemplary ultrasound probe  220  according to an implementation described herein. While in second exemplary ultrasound probe  220  of  FIG.  2 B , 1D transducer array  375  rotates around spindle  330  in a longitudinal direction with respect to 1D transducer array  375 , in third exemplary ultrasound probe  220  of  FIG.  3 C , 1D transducer array  375  rotates around spindle  330  in transverse direction with respect to 1D transducer array  375 , causing 1D transducer array  375  to move back and forth in a fan-like motion. As shown in  FIG.  3 C , ultrasound probe  220  may include 1D transducer array  375  mounted to transducer bucket  370  with base  310 , theta motor  320 , and spindle  330  mounted in a horizontal direction with respect to 1D transducer array  375  and positioned perpendicularly to the center of set of directions  376 . Thus, theta motor  320  may rotate spindle  330  with respect to base  310  in a transverse direction with respect to 1D transducer array  375  by rotating around theta rotational plane  325 , causing 1D transducer array  375  to move back and forth in a fan-like motion. 
       FIG.  3 D  is a diagram of a fourth exemplary ultrasound probe  220  according to an implementation described herein. As shown in  FIG.  3 D , ultrasound probe  220  may include a two-dimensional (2D) array of transducer elements. In this implementation, ultrasound probe  220  may include a base  310 , a spindle  330 , and a transducer bucket  380  with a 2D transducer array  385 . 2D transducer array  385  may include wired or wireless electrical connections that electrically connects 2D transducer array  385  to controller unit  210 . 
     Base  310  may provide structural support to ultrasound probe  220  and secure spindle  330 . Spindle  330  may terminate in transducer bucket  380 . 2D transducer array  385  may be mounted to transducer bucket  380 . 2D transducer array  385  may include a 2D array of piezoelectric transducers, capacitive transducers, and/or other types of ultrasound transducers. 2D transducer array  385  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  385  may transmit and receive ultrasound signals in a particular direction of a set of directions, illustrated as item  390  in  FIG.  3 D . Thus, together, the elements of 2D transducer array  385  may generate ultrasound image data for multiple planes to generate a 3D ultrasound scan. In other words, 2D transducer array  385  may be controlled to tilt an ultrasound beam electronically in a particular direction. In a 3D scan mode, 2D transducer array  385  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  385  may be activated substantially simultaneously to obtain a full 3D scan of the area of interest. 
     In some implementations, ultrasound probe  220  may include a motor assembly  395 . Motor assembly  395  may include a first motor configured to move ultrasound probe  220  in an X direction and a second motor configured to move ultrasound probe  220  in a Y direction. Thus, if ultrasound probe  220  includes motor assembly  395 , rather than being held by an operator, ultrasound probe  220  may be controlled by controller unit  210  to automatically perform a full scan of a patient&#39;s breast area. In other implementations, motor assembly  395  may include a single linear motor. In yet other implementations, motor assembly  395  may include a rotating motor. In some implementations, motor assembly  220  may be attached to an articulating arm which may be used to place ultrasound probe  220  in position over a patient&#39;s breast area (and/or another body area). In other implementations, motor assembly  220  may be located in a recession of a table. A patient may lie prone on the table with the patient&#39;s breast area positioned over the recession, whereupon motor assembly  220  may be controlled to move ultrasound probe  220  within the recession to scan the patient&#39;s breast area. While not shown in  FIG.  3 A,  3 B , or  3 C, in other implementations, ultrasound probe  220  of  FIG.  3 A,  3 B , or  3 C may also include motor assembly  395  to automate a full scan of a patient&#39;s breast area (and/or another area of the patient&#39;s body). 
     Although  FIGS.  3 A,  3 B,  3 C, and  3 D  show exemplary components of ultrasound probe  220 , in other implementations, ultrasound probe  220  may include fewer components, different components, additional components, or differently arranged components than depicted in  FIGS.  3 A,  3 B,  3 C, and  3 D . Additionally or alternatively, one or more components of ultrasound probe  220  may perform one or more tasks described as being performed by one or more other components of ultrasound probe  220 . 
       FIG.  4 A  is a diagram of a first exemplary depth camera  230  that uses a structured-light 3D scanner to obtain depth information for a captured image. As shown in  FIG.  4 A , depth camera  230  may include a digital camera  410 , a structured light projector  420 , a depth sensor  430 , and a handle  440 . Digital camera  410  may include an array of digital image sensors, such as charged-coupled device (CCD) sensors, complementary metal-oxide-semiconductor (CMOS) sensors, and/or other types of digital image sensors configured to capture color optical images. For example, digital camera  410  may obtain RGB values for each pixel in a captured optical image. 
     Structured light projector  420  may project patterns of IR beams on a scene being photographed and depth sensor  430  may include one or more IR sensors to sense reflected IR beams and measure the distance traveled by each IR beam to determine depth information for each pixel. The depth (D) information is correlated to the RGB values to generate RGB-D information for each pixel. Handle  440  may enable an operator to hold depth camera  230  and aim the depth camera at an area of interest for a patient, such as the patient&#39;s breast area. Depth camera  230  may include wired or wireless electrical connections that electrically connects depth camera  230  to controller unit  210 . 
       FIG.  4 B  is a diagram of a second exemplary depth camera  230  that uses an array of individual cameras. As shown in  FIG.  4 B , depth camera  230  may include a camera array  450  and handle  440 . Camera array  450  may include an array of individual cameras that each include an array of digital image sensors, such as CCD sensors, CMOS sensors, and/or other types of digital image sensors configured to capture color optical images. For example, camera array  450  may include a 4×4 array of 16 cameras. The array of cameras may be used to obtain depth information for an image through, for example, parallax detection and/or super-resolution techniques. Handle  440  may enable an operator to hold depth camera  230  and aim the depth camera at an area of interest for a patient, such as the patient&#39;s breast area. Depth camera  230  may include wired or wireless electrical connections that electrically connects depth camera  230  to controller unit  210 . 
       FIG.  4 C  is a diagram of a third exemplary depth camera system  460  that uses a first depth camera  230 -A and a second depth camera  230 -B mounted on a frame  470  with handle  440 . Frame  470  may include adjustable connections  475  that may be used to move first depth camera  230 -A and second depth camera  230 -B to particular positions to scan a patient&#39;s breast area. In some implementations, adjustable connections  475  may include motors that may be electronically controlled by controller unit  210  to move first depth cameras  230 -A and  230 -B. Depth camera system  460  may facilitate capturing the whole breast area at a close range within a smaller time period than a single depth camera  230 . 
     Although  FIGS.  4 A,  4 B, and  4 C  show exemplary components of depth camera  230 , in other implementations, depth camera  230  may include fewer components, different components, additional components, or differently arranged components than depicted in  FIGS.  4 A,  4 B, and  4 C . Additionally or alternatively, one or more components of depth camera  230  may perform one or more tasks described as being performed by one or more other components of depth camera  230 . For example, in some implementations, depth camera  230  may include one or more gyroscopes to track the position of depth camera  230  during image capture. 
       FIG.  5    is a diagram illustrating example components of a device  500  according to an implementation described herein. Controller unit  210 , ultrasound probe  220 , and/or depth camera  230  may each include one or more devices  500 . As shown in  FIG.  5   , device  500  may include a bus  510 , a processor  520 , a memory  530 , an input device  540 , an output device  550 , and a communication interface  560 . 
     Bus  510  may include a path that permits communication among the components of device  500 . Processor  520  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  520  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  530  may include any type of dynamic storage device that may store information and/or instructions, for execution by processor  520 , and/or any type of non-volatile storage device that may store information for use by processor  520 . For example, memory  530  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  540  may allow an operator to input information into device  500 . Input device  540  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  500  may be managed remotely and may not include input device  540 . In other words, device  500  may be “headless” and may not include a keyboard, for example. 
     Output device  550  may output information to an operator of device  500 . Output device  550  may include a display, a printer, a speaker, and/or another type of output device. For example, device  500  may include a display, which may include a liquid-crystal display (LCD) for displaying content to the customer. In some embodiments, device  500  may be managed remotely and may not include output device  550 . In other words, device  500  may be “headless” and may not include a display, for example. 
     Communication interface  560  may include a transceiver that enables device  500  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  560  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  560  may be coupled to an antenna for transmitting and receiving RF signals. 
     Communication interface  560  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  560  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  560  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  500  may perform certain operations relating to implant assessment using ultrasound and/or optical images. Device  500  may perform these operations in response to processor  520  executing software instructions contained in a computer-readable medium, such as memory  530 . 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  530  from another computer-readable medium or from another device. The software instructions contained in memory  530  may cause processor  520  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. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software. 
     Although  FIG.  5    shows exemplary components of device  500 , in other implementations, device  500  may include fewer components, different components, additional components, or differently arranged components than depicted in  FIG.  5   . Additionally or alternatively, one or more components of device  500  may perform one or more tasks described as being performed by one or more other components of device  500 . 
       FIG.  6    is a diagram illustrating exemplary functional components of controller unit  210 . The functional components of controller unit  210  may be implemented, for example, via processor  520  executing instructions from memory  530 . Alternatively, some or all of the functional components of controller unit  210  may be implemented via hard-wired circuitry. As shown in  FIG.  6   , controller unit  210  may include a camera controller  610 , an optical images DB  612 , an optical machine learning (ML) model  614 , a 3D scan controller  620 , a 3D scan DB  622 , a 3D scan ML model  624 , a thermal camera controller  630 , a thermal images DB  632 , a thermal ML model  634 , an ultrasound probe controller  640 , a ultrasound images DB  642 , a ultrasound ML model  644 , a breast morphology ML model  650 , a recommendation module  660 , and a user interface  670 . 
     Camera controller  610  may be configured to control depth camera  230  and to collect optical image data from depth camera  230 . For example, camera controller  620  may instruct a user to position depth camera  230  in particular locations to take a series of optical images and store the optical images in optical images DB  612 . 
     3D scan controller  620  may control depth camera  230  to obtain 3D scan images. For example, 3D scan controller  620  may keep track of the position of depth camera  230  to guide the user to capture images from several angles. Depth camera  230  may store the position information associated with each captured optical image. The position information may be used to generate a point cloud and/or 3D triangular mesh of the patient&#39;s breast area. 3D scan controller  620  may store captured 3D scan images in 3D scan images DB  622 . In some implementations, optical and 3D scan information may be stored together as RGB-D images. 
     Thermal camera controller  630  may control thermal camera  240  to obtain thermal camera images. For example, thermal camera controller  630  may keep track of the position of thermal camera  240  to guide the user to capture thermal images from several angles. Thermal camera controller  630  may store captured thermal camera images in thermal images DB  632 . 
     Ultrasound probe controller  640  may be configured to control ultrasound probe  220  and to collect ultrasound image data from ultrasound probe  220 . For example, ultrasound probe controller  640  may perform a 3D scan of a sector in a sector grid associated with a patient&#39;s breast area by generating ultrasound images in particular planes by controlling one or more motors and/or particular transducers of ultrasound probe  220 . For a particular sector, ultrasound probe controller  640  may control ultrasound probe  220  to perform a 3D scan that includes all radial, antiradial, sagittal, and transverse planes in the sector. Furthermore, ultrasound probe controller  640  may be configured to control ultrasound probe  220  to obtain elastography ultrasound images. Ultrasound probe controller  640  may store obtained ultrasound images in ultrasound images DB  642 . 
     Optical ML model  614  may include a trained CNN model and/or another type of ML model. Optical ML model  614  may be trained to process and characterize optical images from optical images DB  612 . For example, optical ML model  614  may pre-process optical images before performing classification and/or feature extraction. Optical ML model  614  may be configured to classify one or more optical images into a particular quantitative clinical grading value. 
     3D scan ML model  624  may include a trained CNN model and/or another type of ML model. 3D scan ML model  624  may be trained to process and characterize 3D scan images from 3D scan images DB  622 . For example, 3D scan ML model  624  may pre-process 3D scan images before performing classification and/or feature extraction, such as generating a point cloud and/or a 3D triangular mesh based on one or more 3D scan images. 3D scan ML model  624  may be configured to classify one or more 3D scan images into a particular quantitative clinical grading value. Additionally, or alternatively, 3D scan ML model  624  may be configured to generate a 3D geometric feature plot; perform segmentation of an implant capsule, a rupture area and/or a fluid collection; and/or generate a 3D shape of the implant capsule, rupture area, and/or fluid collection based on the segmentation. 
     Thermal ML model  634  may include a trained CNN model and/or another type of ML model. Thermal ML model  634  may be trained to process and characterize thermal camera images from thermal images DB  632 . For example, thermal ML model  634  may pre-process thermal camera images before performing classification and/or feature extraction. Thermal ML model  634  may be configured to classify one or more thermal camera images into a particular quantitative clinical grading value. Additionally, or alternatively, thermal ML model  634  may be configured to generate a temperature variation plot; generate a 3D geometric feature plot; perform segmentation of an implant capsule, a rupture area and/or a fluid collection; and/or generate a 3D shape of the implant capsule, rupture area, and/or fluid collection based on the segmentation. 
     Ultrasound ML model  644  may include a trained CNN model and/or another type of ML model. Ultrasound ML model  644  may be trained to process and characterize ultrasound images from ultrasound images DB  642 . For example, ultrasound ML model  644  may pre-process ultrasound images before performing classification and/or feature extraction. Ultrasound ML model  644  may be configured to classify one or more ultrasound images into a particular quantitative clinical grading value. Additionally, or alternatively, ultrasound ML model  644  may be configured to generate a 3D geometric feature plot; perform segmentation of an implant capsule, a rupture area and/or a fluid collection; generate a stiffness plot based on elastography images; and/or generate a 3D shape of the implant capsule, rupture area, and/or fluid collection based on the segmentation. 
     Furthermore, ultrasound ML model  644  may include a CNN model and/or another type of ML model trained to perform segmentation to identify the boundary of an implant capsule in an ultrasound image, to determine a thickness of the implant capsule, and/or to determine a radial fold count for the implant capsule. Additionally, or alternatively, ultrasound ML model  644  may include a CNN model and/or another type of ML model trained to identify an extracapsular rupture, a diffuse snowstorm sign associated with an extracapsular rupture, an intracapsular rupture, a capsular contracture, a fluid collection (e.g., a seroma, a hematoma, a breast abscess, etc.), a breast mass, and/or other types of pathologies that may be associated with a breast or a breast implant in an ultrasound image. 
     Breast morphology ML model  650  may include a trained CNN model and/or another type of ML model. Breast morphology ML model  650  may take as input the output of one or more of optical ML model  614 , 3D scan ML model  624 , thermal ML model  634 , and/or ultrasound ML model  644 . Alternatively, in some implementations, one or more of optical ML model  614 , 3D scan ML model  624 , thermal ML model  634 , and/or ultrasound ML model  644  and breast morphology ML model  650  may be combined into a single ML model. 
     Breast morphology ML model  650  may be trained to identify a change in the morphology or integrity of a patient&#39;s breast and/or breast implant. For example, breast morphology model  670  may generate a quantitative clinical grading value based on a set of images, such as a Baker class value, an implant capsule thickness value, a radial fold count value, an implant capsule smoothness value, and/or another type of quantitative clinical grading value. 
     Additionally, or alternatively, breast morphology ML model  650  may be trained to generate a 3D geometrical feature plot. The 3D geometrical feature may include, for example, curvature, symmetry, smoothness, distance from a particular location, and/or another type of 3D geometrical feature. Furthermore, breast morphology ML model  650  may be trained to use the generated 3D geometrical feature plot to identify a lack of symmetry between a left and a right breast, to determine the size and/or volume of a breast, to determine the angle of a breast with respect to the patient&#39;s rib cage, to determine the distance between a point on a breast and a reference point on the patient&#39;s rib cage, to identify a volume region that has experienced contracture, to identify a volume region that has experienced a rupture, to identify volume a region of fluid collection, and/or to identify another feature within an optical image that includes depth information. 
     Furthermore, breast morphology ML model  650  may be trained to compare baseline optical, 3D scan, thermal camera, and/or ultrasound images for a patient&#39;s breast area, a baseline 3D geometrical feature plot, and/or a baseline 3D shape of an implant capsule with follow-up optical, 3D scan, thermal camera, and/or ultrasound images for the patient&#39;s breast area, a follow-up 3D geometrical feature plot, and/or a follow-up 3D shape of the implant capsule and output a quantitative clinical grading value based on the comparison. In some implementations, breast morphology ML model  650  may further output an image that has been segmented to identify the boundaries around areas in the patient&#39;s breast area that have changed from the baseline optical image to the follow-up optical image. 
     Recommendation module  660  may provide the output of breast morphology ML model  650  and may generate a recommendation for a particular type of medical intervention based on the output of breast morphology ML model  650 . As an example, if breast morphology ML model  650  outputs a Baker class of 1 or 2, recommendation module  660  may recommend a non-surgical intervention to correct a capsular contracture, such as manual therapy, a steroid injection, or a drug prescription. As another example, if breast morphology ML model  650  outputs a Baker class of 3 or 4, recommendation module  660  may recommend surgical intervention to correct a capsular contracture. As yet another example, if breast morphology ML model  650  outputs an identified seroma, recommendation module  660  may recommend an aspiration procedure. As yet another example, if breast morphology ML model  650  outputs a particular thickness value of an implant capsule and/or a particular radial fold count value, recommendation module  660  may recommend a particular medical intervention based on the generated value. 
     User interface  670  may generate or include a user interface (e.g., a graphical user interface) that displays ultrasound images and/or optical images to a user via display  215 , displays recommendations to the user, and that is configured to receive selections and/or commands from the user via a touchscreen associated with display  215 , via one or more control keys located on controller unit  210 , on ultrasound probe  220 , and/or depth camera  230 , via a microphone included in controller unit  210 , and/or via another type of input method. 
     For example, a user may select a particular type of ultrasound image to obtain, to perform a 3D ultrasound scan using a sector grid, select to perform an elastography ultrasound scan, select to capture optical and/or 3D scan images using depth camera  230 , select to retrieve a patient record that includes previously obtained ultrasound and optical images, select to perform a machine learning model comparison of baseline and follow-up ultrasound, optical, 3D scan, and/or thermal, select to use a machine learning model to perform segmentation and analysis of an implant capsule, select to identify implant or breast pathologies using a machine learning model, and/or select to perform another type of function for which controller unit  210  has been configured. 
     Although  FIG.  6    shows exemplary components of controller unit  210 , in other implementations, controller unit  210  may include fewer components, different components, additional components, or differently arranged components than depicted in  FIG.  6   . Additionally, or alternatively, one or more components of controller unit  210  may perform one or more tasks described as being performed by one or more other components of controller unit  210 . 
       FIG.  7    is a flowchart of a process for implant assessment using machine learning according to an implementation described herein. In some implementations, the process of  FIG.  7    may be performed by ultrasound system  201  or  202 . In other implementations, some or all of the process of  FIG.  7    may be performed by another device or a group of devices separate from ultrasound system  201  or  202 . 
     The process of  FIG.  7    may include obtaining baseline optical, 3D scan, and/or thermal camera images (block  710 ). For example, controller unit  210  may guide a user, via display  115  and/or a speaker, to capture a set of optical and/or 3D scan images via depth camera  230 . In some implementations, the user may be instructed to continuously move depth camera  230  around the patient&#39;s breast area while depth camera  230  captures a set of optical images. In other implementations, the user may be instructed to position depth camera  230  at particular angles with respect to the patient&#39;s breast area. In yet other implementations, the patient may be asked to move to different positions while depth camera  230  remains stationary. Furthermore, the patient may be instructed to assume different positions, such as a supine position, an inclined position, an upright position, etc. A similar procedure may be performed using thermal camera  240  to capture thermal camera images. 
     In some implementations, depth camera  230  may include a laser guide to facilitate aiming depth camera  230  at the correct location on the patient&#39;s body. Furthermore, in some implementations, the user may be guided to obtain a first set of 3D scan images from a first distance away from the patient and to obtain a second set of 3D scan images from a second distance away from the patient. The obtained 3D scan images may be stored in 3D scan images DB  622  in connection with information identifying the patient, information identifying the date when the images were taken, information identifying the images as baseline images, and/or other types of information associated with the optical images. 
     Baseline ultrasound images may be obtained (block  720 ). For example, controller unit  210  may guide a user, via display  115  and/or a speaker, to capture a set of ultrasound images via ultrasound probe  110 . Controller unit  210  may generate a sector grid and may guide the user to perform a 3D ultrasound scan for each sector in the sector grid. Display  115  may display a progress of the scan by indicating which sectors have already been scanned. Additionally, the user may select, or be instructed to, obtain a set of elastography ultrasound images using ultrasound probe  220 . In other implementations, ultrasound probe  220  may include a gyroscope and/or another type of position tracking device and the user may be guided to move ultrasound probe  220  as a brush by filling in a bull&#39;s eye plot centered on the patient&#39;s breast. The obtained ultrasound imaged may be stored in ultrasound images DB  642  in connection with information identifying the patient, information identifying the date when the images were taken, information identifying the images as baseline images, and/or other types of information associated with the ultrasound images. 
     Follow-up optical, 3D scan, and/or thermal camera images may be obtained (block  730 ) and follow-up ultrasound images may be obtained (block  740 ). For example, the patient may return at a later time for a follow-up visit. Follow-up optical, 3D scan, and/or thermal camera images and ultrasound images of the patient&#39;s breast area may be obtained during the follow-up visit and stored in optical images DB  612 , 3D scan images DB  622 , thermal images DB  632 , and/or ultrasound image DB  642  similarly to as described above with respect to blocks  710  and  720 . 
     One or more machine learning models may be used to compare the baseline and follow-up images (block  750 ) and a change in breast or implant morphology or integrity may be detected based on the comparisons made using the one or more machine learning models (block  760 ). For example, breast morphology ML model  650  may compare baseline ultrasound, optical, 3D scan, and/or thermal camera images of the patient&#39;s breast area, a baseline 3D geometrical feature plot, and/or a baseline 3D shape of an implant capsule, with follow-up ultrasound, optical, 3D scan, and/or thermal camera images of the patient&#39;s breast area, a follow-up 3D geometrical feature plot, and/or a follow-up 3D shape of an implant capsule. 
     In some implementations, breast morphology ML model  650  may output a quantitative clinical grading value based on the comparison. In other implementations, breast morphology ML model  650  may classify the input into another type of class from a set of classes, such as whether a rupture is present, a type of rupture, whether a particular breast or implant pathology is present, and/or another type of classification. 
     A medical intervention recommendation may be generated based on the detected change (block  770 ). For example, recommendation module  660  may provide the output of breast morphology ML model  650  and may generate a recommendation for a particular type of medical intervention based on the output of breast morphology ML model  650 , such a recommendation based on a quantitative clinical grading value, a recommendation based on an identified pathology, and/or another type of recommendation. 
     In some implementations, implant assessment may be performed without obtaining baseline images. For example, breast morphology ML model  650  may obtain an ultrasound image of a patient&#39;s breast area, obtain at least one of an optical image of the patient&#39;s breast area, a three-dimensional (3D) scan image of the patient&#39;s breast area, or a thermal camera image of the patient&#39;s breast area, and compare the ultrasound image with the at least one of the optical image of the patient&#39;s breast area, the 3D scan image of the patient&#39;s breast area, or the thermal camera image of the patient&#39;s breast area. 
       FIG.  8    is a flowchart of a process for characterizing an implant capsule using machine learning according to an implementation described herein. In some implementations, the process of  FIG.  8    may be performed by ultrasound system  201  or  202 . In other implementations, some or all of the process of  FIG.  8    may be performed by another device or a group of devices separate from ultrasound system  201  or  202 . 
     The process of  FIG.  8    may include capturing an ultrasound image (block  810 ). For example, ultrasound probe  220  may capture an ultrasound image of a patient&#39;s breast area. The ultrasound image may include an image of an implant capsule. A machine learning model may be used to perform a segmentation of an implant capsule in the captured ultrasound image (block  820 ). For example, ultrasound ML model  642  may perform segmentation on the captured ultrasound image to identify the boundaries of the implant capsule. 
     A quantitative clinical grading value may be determined based on the segmentation (block  830 ). For example, ultrasound ML model  642  may use the identified boundaries of the implant capsule to determine a set of thickness values for the implant capsule and to determine an average thickness for the implant capsule based on the determined set of thickness values. As another example, ultrasound ML model  642  may use the identified boundaries of the implant capsule to identify a set of radial folds. For example, ultrasound ML model  642  may identify points at which the curvature of a boundary of the implant capsule changes from positive to negative or vice versa. ultrasound ML model  642  may determine the number of radial folds per unit distance of the implant capsule. As yet another example, ultrasound ML model  642  may determine a smoothness value based on the segmented implant capsule. 
     A morphology or integrity of the implant may be determined based on the determined quantitative clinical grading value (block  850 ). For example, ultrasound ML model  642  may use the determined average thickness and radial fold count to classify the implant capsule into a particular Baker class and/or to determine whether the implant capsule is associated with capsular contracture. 
       FIG.  9    is a flowchart of a process for characterizing the morphology or integrity of an implant using machine learning according to an implementation described herein. In some implementations, the process of  FIG.  9    may be performed by ultrasound system  201  or  202 . In other implementations, some or all of the process of  FIG.  9    may be performed by another device or a group of devices separate from ultrasound system  201  or  202 . 
     The process of  FIG.  9    may include capturing an ultrasound image (block  910 ). For example, ultrasound probe  220  may capture an ultrasound image, or a series of ultrasound images, of a patient&#39;s breast area. The ultrasound image may include an image of a breast implant. Segmentation may be performed to identify the borders of an implant capsule, a rupture area, and/or a fluid collection in each of a series of ultrasound images (block  920 ). For example, ultrasound ML model  644  may perform segmentation to identify the borders of an implant capsule, a rupture area, or a fluid collection. 
     A 3D shape of an implant capsule, rupture area, and/or a fluid collection may be generated based on the boundaries identified in each of the captured ultrasound images (block  930 ). For example, ultrasound ML model  644  may stitch together or connect the boundaries of a 3D shape of an implant capsule, rupture area, and/or a fluid collection using the boundaries identified in each of the ultrasound images and the position information associated with each ultrasound image. 
     A morphology or integrity of an implant may be determined based on the generated 3D shape (block  940 ). For example, breast morphology ML model  650  may determine a quantitative clinical grading based on the generated 3D shape and/or based on comparing the generated 3D shape to a previously generated baseline 3D shape. 
       FIG.  10 A  is a diagram  1000  illustrating the use of a sector grid for performing an ultrasound. As shown in  FIG.  10 A , a sector grid  1010  may be overlaid over a patient&#39;s breast area. In some implementations, sector grid  1010  may include a physical marker, such as a sector grid printed onto a translucent thin film that is overlaid over the patient&#39;s breast area or a removable stencil that may be transferred onto the patient&#39;s skin. In other implementations, sector grid  1010  may be projected onto the patient&#39;s skin using a laser projector that is associated with controller unit  210 . In yet other implementations, sector grid  1010  may be generated using augmented reality. For example, depth camera  230  (or another camera device) may be aimed at the patient&#39;s breast area and an image of the patient&#39;s breast area may be displayed by display  215  with sector grid  1010  overlaid over the image of the patient&#39;s breast area. 
     Sector grid  1010  may be used to keep track of which sectors of the patient&#39;s breast area have been scanned. Controller unit  210  may track the position of ultrasound probe  220  to determine which sector is being scanned and/or which sectors have already been scanned. For example, by aiming ultrasound probe  220  at a particular sector, a user may click trigger  224 , activate a selection item on display  215 , speak a command, or perform another type of action to perform a 3D scan of the particular sector. A 3D scan may capture all radial, anti-radial, sagittal, and/or transverse planes using a B-mode ultrasound image (and/or another type of ultrasound image). 
       FIG.  10 B  is a diagram  1020  illustrating the progression of an ultrasound scan using sector grid  1010 . As shown in  FIG.  10 B , a sector grid  1010 -L may be associated with a left breast and a sector grid  1010 -R may be associated with a right breast. A user interface  1022  on display  215  may initially depict empty sector grids  1010 -L and  1010 -R, indicating that no sectors have been scanned yet. As the user progresses with scanning the patient&#39;s breast area, user interface  1024  may display the progress of the scan. For example, a sector  1026  that has been scanned may be filled in with a particular shading, color, or pattern. Furthermore, a sector  1028  may be designated as a sector of interest with another shading, color, or pattern to indicate that one or more machine learning models have identified an object of interest in the sector, such as a change in the morphology or integrity of an implant, and/or an implant or breast pathology. When the user has scanned all the sectors, sector grid  1010  may be completely filled in with the particular shading, color, or pattern, as shown in user interface  1030 . In some implementations, scanning of sector grid  1010  may be automated by controller unit  210  via motor assembly  395 . 
       FIG.  10 C  is a diagram  1040  illustrating selection of a scanned sector to view an ultrasound image associated with the selected sector. In response to a user selecting a sector  1050 , the sector may be highlighted and an ultrasound image  1060  associated with the sector may be displayed in the user interface. In some implementations, the user may scroll or flip through ultrasound images associated with the selected sector. While  FIGS.  10 A- 10 C  illustrate a sector grid, in other implementations, other types of markers may be used to keep track of which areas of a patient&#39;s breast area have been scanned. For example, sector grid  1010  may include a dot pattern and/or another type of marker. 
       FIG.  10 D  is a diagram  1070  illustrating the planes of a 3D scan performed for each sector of sector grid  1010 . Ultrasound probe  220  may perform a series of scans to generate a set of ultrasound images in a radial plane  1072  with respect to the breast. Ultrasound probe  220  may further perform a series of scans to generate a set of ultrasound images in an antiradial plane  1074  with respect to the breast. Moreover, ultrasound probe  220  may perform a series of scans to generate a set of ultrasound images in a sagittal plane  1076  with respect to the breast and a series of scans to generate a set of ultrasound images in a transverse plane  1078  with respect to the breast. 
       FIG.  11    is a diagram  1101  illustrating the use of elastography in assessing a breast implant. As shown in  FIG.  11   , an ultrasound image  1110  may undergo segmentation to identify the boundaries of implant capsule  1120 . Similarly, an elastography image  1120  may undergo segmentation to identify areas with different values of stiffness. For example, a segmented area  1140  in elastography image  1130  may provide different information than the segmented implant capsule  1120  in an ultrasound image. Thus, ultrasound ML model  644  may be trained to perform segmentation to identify areas associated with a particular tissue stiffness and/or to identify particular a particular pathology associated with a particular tissue stiffness. 
       FIG.  12 A  is a diagram  1201  illustrating the use of a 3D triangular mesh on a 3D scan image. As shown in  FIG.  12 A , one or more optical images captured using depth camera  230  may be processed to remove backgrounds and converted into a point cloud or a 3D triangular mesh  1210  that covers the patient&#39;s breast area. If the depth data in the captures optical images includes noise that is above a particular threshold, noise reduction techniques may be applied to the data to improve the accuracy of the point cloud. 
       FIG.  12 B  is a diagram  1202  illustrating a 3D geometrical feature plot where the 3D geometrical feature corresponds to curvature. As shown in  FIG.  12 B , diagram  1202  illustrates a generated 3D curvature plot. 3D curvature plot may display areas associated with a particular range of curvature values with a particular shading, color, or pattern. The 3D curvature plot may be used to identify a change in the morphology or integrity of a patient&#39;s breast and/or breast implant. For example, the 3D curvature plot may be used to perform a symmetry analysis to determine whether the left and right breasts are within a particular symmetry threshold. Furthermore, a baseline and follow-up 3D curvature plot may be used to visually assess a change in the shape of the patient&#39;s breast area. 
     The 3D curvature plot may be used to calculate one or more values for the patient&#39;s breast area, such as, for example, the size and/or volume of a breast, the angle of a breast with respect to the patient&#39;s rib cage, the distance between a point on a breast and a reference point on the patient&#39;s rib cage, and/or another measurement value. Moreover, depth camera  230  may be used to obtain 3D scan images in different postures, such as while the patient is supine, incline, upright, standing, etc. 3D curvature plots associated with different postures may be used to track the change in the shape of the patient&#39;s breasts in different postures to generate additional information relating to the morphology or integrity of the patient&#39;s breast and/or breast implant. 
     Furthermore, the generated point cloud and/or 3D triangular mesh, and/or the generated 3D curvature plot may be used as an input into a trained machine learning model, such as breast morphology ML model  650 . For example, breast morphology ML model  650  may be trained to detect, based on an input of a 3D curvature plot, an area in the 3D curvature plot that has experienced contracture, an area in the 3D curvature plot that has experienced a rupture, and/or an area in the 3D curvature plot that has experienced another type of abnormality. 
       FIG.  13 A  illustrates a segmented ultrasound image  1301  that includes a normal implant capsule  1310 . As shown in  FIG.  13 A , normal implant capsule  1310  may include clearly delineated and smooth boundaries and may not exhibit any thickening due to fibrous tissue or radial folds.  FIG.  13 B  illustrates a segmented ultrasound image  1302  that includes a thickened implant capsule  1320  with excessive fibrous tissue as a result of capsular contracture.  FIG.  13 C  illustrates a segmented ultrasound image  1303  that includes radial folds  1330  in the implant capsule, giving the implant capsule a wavy appearance. Radial folds  1330  in an implant capsule may be caused by capsular contracture of the implant. 
       FIG.  14 A  illustrates an ultrasound image  1401  that includes a segmented implant capsule  1410 . For example, implant capsule ML model  650  may be trained to perform segmentation to identify the boundaries of an implant capsule.  FIG.  14 B  illustrates an ultrasound image  1402  that includes computed thickness values  1420  determined based on the segmented implant capsule  1410 . For example, implant capsule ML model  650  may be trained to calculate the thickness of the implant capsule based on the distance between the boundaries of the implant capsule identified by the segmentation. 
       FIG.  14 C  illustrates an ultrasound image  1403  that includes identified radial folds  1430 . For example, implant capsule ML model  650  may be trained to identify radial fold points based on the segmented implant capsule  1410 . The determined thickness of the implant capsule and/or the determined radial fold count for the implant capsule may be used to determine whether the implant capsule is associated with capsular contracture and/or an extent of the capsular contracture, such as the Baker class associated with the capsular contracture. 
       FIG.  15    is a diagram illustrating generation of a 3D shape  1520  from a series of segmented ultrasound images  1510 -A to  1510 -N. As shown in  FIG.  15   , each ultrasound image  1510  may correspond to a different slice through the implant, enabling the stitching together or connecting of the segmented boundary from each ultrasound image  1510  to generate 3D shape  1520  of an implant capsule. 
       FIG.  16 A  illustrates a segmented ultrasound image  1601  that includes an identified extracapsular rupture  1610 . For example, ultrasound ML model  644  may be trained to identify an extracapsular rupture in an ultrasound image and to perform segmentation to identify the borders of extracapsular rupture  1610 . Extracapsular rupture  1610  may be used to generate a medical intervention recommendation, such as, for example, to recommend the patient for an implant replacement.  FIG.  16 B  illustrates a segmented ultrasound image  1602  that includes an identified intracapsular rupture  1620 . For example, ultrasound ML model  644  may be trained to identify an intracapsular rupture in an ultrasound image and to perform segmentation to identify the borders of intracapsular rupture  1620 . Intracapsular rupture  1620  may be used to generate a medical intervention recommendation, such as, for example, to recommend the patient for an implant replacement. Ultrasound ML model  644  may be trained to identify other types of specific features, such as, for example, a diffuse snowstorm sign in an ultrasound image. A diffuse snowstorm sign may indicate extracapsular rupture and may be used to generate a medical intervention recommendation, such as, for example, to recommend the patient for an implant replacement. 
       FIGS.  17 A and  17 B  illustrate segmentation to identify fluid collections.  FIG.  17 A  illustrates a segmented ultrasound image  1701  that includes an identified peri-implant hematoma  1710 . For example, ultrasound ML model  644  may be trained to identify a hematoma in an ultrasound image and to perform segmentation to identify the borders of hematoma  1710 . Hematoma  1710  may be used to help with an implant assessment by a medical professional.  FIG.  17 B  illustrates an ultrasound image  1702  that includes an identified breast abscess  1720 . For example, ultrasound ML model  644  may be trained to identify a breast abscess in an ultrasound image and to perform segmentation to identify the borders of breast abscess  1720 . Breast abscess  1720  may be used to help with an implant assessment by a medical professional. 
       FIG.  18 A  is a diagram  1801  illustrating a first exemplary ML model according to an implementation described herein. As shown in  FIG.  18 A , optical images  1810 , 3D scan images  1812 , thermal camera images  1814 , and ultrasound images  1816  may be provided as input into trained ML model  1820 . For example, trained ML model  1820  may include optical ML model  614 , 3D scan ML model  624 , thermal ML model  634 , and/or ultrasound ML model  644 . Trained ML model  1820  may be trained to generate output  1830  that includes one or more of a 3D geometrical feature plot based on the inputted images; to classify the inputted images into a particular quantitative clinical grading value; to perform a segmentation of an implant capsule, a rupture area and/or a fluid collection; and/or to generate a 3D shape of an implant capsule, rupture area and/or fluid collection based on the segmentation. In other implementation, multiple ML models may be trained to generate these different types of classification and/or feature extraction outputs. 
       FIG.  18 B  is a diagram  1802  illustrating a second exemplary ML model according to an implementation described herein. As shown in  FIG.  18 B , input  1840  may include baseline optical images, baseline 3D scan images, baseline thermal camera images, and/or baseline ultrasound images. Additionally, input  1840  may include a baseline 3D geometrical feature plot and/or a baseline 3D shape of the implant capsule generated by another ML model. 
     Input  1850  may include follow-up optical images, follow-up 3D scan images, follow-up thermal camera images, and/or follow-up ultrasound images. Additionally, input  1850  may also include a follow-up 3D geometrical feature plot and/or a follow-up 3D shape of the implant capsule generated by the other ML model. ML model  1860  may be trained to take inputs  1840  and  1850  and to classify the inputs into a particular quantitative clinical grading value, such as, for example, a Baker class value, an implant capsule thickness value, a radial fold count value, an implant capsule smoothness value, and/or another type of quantitative clinical grading value. 
       FIG.  19 A  is a diagram illustrating an exemplary user interface  1901  according to an implementation described herein. As shown in  FIG.  19 A , controller unit  210  may generate a display  1910  that includes different types of images side by side. For example, display  1910  may display a 3D mesh image  1912  next to a thermal camera image  1914 . The images may be displayed in real-time or near real-time. Controller unit  210  may display any combination of types of images, such as an ultrasound image next to an optical image, an ultrasound image next to a 3D scan image, an ultrasound image next to a thermal camera image, an optical image next to a 3D scan image, an optical image next to a thermal camera image, and/or a 3D scan image next to a thermal camera image. A user may select which types of images to display on display  1910 . 
       FIG.  19 B  is a diagram illustrating an exemplary user interface  1902  according to an implementation described herein. As shown in  FIG.  19 B , controller unit  210  may generate a display  1920  that includes a first type of image with an ultrasound image and an indication of a location on the first type of image corresponding to the ultrasound image. For example, display  1920  may display an optical image  1922  of a patient&#39;s breast area and an indication  1924  that corresponds to the location on the patient&#39;s breast area associated with a displayed ultrasound image  1926 . The user may select different areas on optical image  1922  to display ultrasound images associated with a selected area. Furthermore, controller unit  210  may display, on optical image  1922 , indications associated with areas where particular abnormalities have been detected. 
       FIG.  19 C  is a diagram illustrating an exemplary user interface  1903  according to an implementation described herein. As shown in  FIG.  19 C , controller unit  210  may generate a display  1930  that includes a first type of image with an overlay of a generated 3D shape. For example, display  1930  may display an optical image  1932  with an overlaid generated 3D shape  1934  that was generated from a series of ultrasound images segmented to identify the boundaries of an implant capsule. 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  FIGS.  7 ,  8   , and  9 , 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. 
     No element, act, or instruction used in the present application should be construed as critical or essential to the embodiments unless explicitly described as such. Also, as used herein, the article “a” is intended to include one or more items. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise.