阅读说明：本技术 用于图像采集的自动视图规划的连续学习 (Continuous learning for automatic view planning for image acquisition ) 是由 P.沙马 D.科马尼丘 于 2020-02-13 设计创作，主要内容包括：描述了用于根据与特定临床站点相关联的本地偏好自动识别医学图像中的解剖标志的系统和方法。接收用于执行医学过程的医学图像。使用预训练的机器学习算法在医学图像中识别解剖标志。从与特定临床站点相关联的用户接收与所识别的解剖标志有关的反馈。在用于执行医学过程的正常工作流程期间接收反馈。基于所接收的反馈来再训练所述预训练的机器学习算法,使得根据与所述特定临床站点相关联的本地偏好来训练经再训练的机器学习算法。(Systems and methods are described for automatically identifying anatomical landmarks in medical images based on local preferences associated with particular clinical sites. A medical image for performing a medical procedure is received. Anatomical landmarks are identified in medical images using a pre-trained machine learning algorithm. Feedback related to the identified anatomical landmark is received from a user associated with the particular clinical site. The feedback is received during a normal workflow for performing the medical procedure. Retraining the pre-trained machine learning algorithm based on the received feedback such that the retrained machine learning algorithm is trained according to local preferences associated with the particular clinical site.)

1. A method, comprising:

receiving a medical image for performing a medical procedure;

identifying anatomical landmarks in the medical image using a pre-trained machine learning algorithm;

receiving feedback from a user associated with a particular clinical site related to the identified anatomical landmark, the feedback received during a normal workflow for performing the medical procedure; and

retraining the pre-trained machine learning algorithm based on the received feedback such that the retrained machine learning algorithm is trained according to local preferences associated with the particular clinical site.

2. The method of claim 1, wherein receiving feedback from a user associated with a particular clinical site regarding the identified anatomical landmark comprises:

receiving feedback from the user regarding the identified anatomical landmark without prompting the user.

3. The method of claim 1, wherein the pre-trained machine learning algorithm is not trained according to the local preferences associated with the particular clinical site.

4. The method of claim 1, wherein the pre-trained machine learning algorithm is trained according to preferences associated with a general population of clinicians.

5. The method of claim 1, wherein retraining the pre-trained machine learning algorithm based on the received feedback comprises:

retraining the pre-trained machine learning algorithm locally at a particular clinical site.

6. The method of claim 1, wherein the feedback comprises an input from a user correcting the location of the identified anatomical landmark.

7. The method of claim 1, wherein the feedback comprises an input from a user rejecting the identified anatomical landmark.

8. The method of claim 1, further comprising:

receiving another medical image for performing another medical procedure;

identifying certain anatomical landmarks in the other medical image using a retrained machine learning algorithm; and

performing the other medical procedure based on the identified certain anatomical landmarks.

9. An apparatus, comprising:

means for receiving a medical image for performing a medical procedure;

means for identifying anatomical landmarks in the medical image using a pre-trained machine learning algorithm;

means for receiving feedback from a user associated with a particular clinical site related to the identified anatomical landmark, the feedback received during a normal workflow for performing the medical procedure; and

means for retraining the pre-trained machine learning algorithm based on the received feedback such that the retrained machine learning algorithm is trained according to local preferences associated with the particular clinical site.

10. The apparatus of claim 9, wherein the means for receiving feedback related to the identified anatomical landmark from a user associated with the particular clinical site comprises:

means for receiving the feedback from the user related to the identified anatomical landmark without prompting the user.

11. The apparatus of claim 9, wherein the pre-trained machine learning algorithm is not trained according to the local preferences associated with the particular clinical site.

12. The apparatus of claim 9, wherein the pre-trained machine learning algorithm is trained according to preferences associated with a general population of clinicians.

13. The apparatus of claim 9, wherein means for retraining the pre-trained machine learning algorithm based on the received feedback comprises:

means for retraining the pre-trained machine learning algorithm locally at the particular clinical site.

14. The apparatus of claim 9, further comprising:

means for receiving another medical image for performing another medical procedure;

means for identifying certain anatomical landmarks in the other medical image using a retrained machine learning algorithm; and

means for performing the other medical procedure based on the identified certain anatomical landmarks.

15. A non-transitory computer-readable medium storing computer program instructions that, when executed by a processor, cause the processor to perform operations comprising:

receiving a medical image for performing a medical procedure;

identifying anatomical landmarks in the medical image using a pre-trained machine learning algorithm;

receiving feedback from a user associated with a particular clinical site related to the identified anatomical landmark, the feedback received during a normal workflow for performing the medical procedure; and

retraining the pre-trained machine learning algorithm based on the received feedback such that the retrained machine learning algorithm is trained according to local preferences associated with the particular clinical site.

16. The non-transitory computer-readable medium of claim 15, wherein receiving feedback from a user associated with a particular clinical site regarding the identified anatomical landmark comprises:

receiving the feedback from the user relating to the identified anatomical landmark without prompting the user.

17. The non-transitory computer-readable medium of claim 15, wherein the pre-trained machine learning algorithm is not trained according to the local preferences associated with the particular clinical site.

18. The non-transitory computer-readable medium of claim 15, wherein the feedback comprises an input from a user correcting the location of the identified anatomical landmark.

19. The non-transitory computer-readable medium of claim 15, wherein the feedback comprises an input from a user rejecting the identified anatomical landmark.

20. The non-transitory computer-readable medium of claim 15, the operations further comprising:

receiving another medical image for performing another medical procedure;

identifying certain anatomical landmarks in the other medical image using a retrained machine learning algorithm; and

performing the other medical procedure based on the identified certain anatomical landmarks.

Technical Field

The present invention relates generally to continuous learning of automatic view planning for image acquisition, and more particularly to local live continuous learning for automatic view planning to address local preferences associated with particular clinical sites.

Background

During cardiac Magnetic Resonance Imaging (MRI) acquisition, a localizer scan is typically acquired to locate the heart and specify (script) long and short axis views of the heart. Based on the localizer scan, a standard MRI image is planned. To locate the heart and define the long and short axis views of the heart, anatomical landmarks are identified in two-dimensional (2D) slices extracted from the 3D volume scanned by the localizer. Anatomical landmarks may be identified manually by annotating the 2D slice by a user, or automatically using machine learning algorithms.

Traditional machine learning algorithms for identifying anatomical landmarks are pre-trained prior to deployment to a clinical site. Such pre-trained machine learning algorithms are typically trained for a specific definition of how anatomical landmarks should appear in a 2D slice. Training is performed offline, and pre-trained machine learning models are deployed on imaging scanners at a plurality of different clinical centers. The pre-trained machine learning algorithm is updated only when a centrally managed software release is released.

Such conventional pre-trained machine learning algorithms for automatically recognizing anatomical landmarks are not conventionally used by many clinical centers. One reason for this is the diversity across different clinical centers with respect to how to identify the definition of anatomical landmarks in 2D slices. Such conventional algorithms are centrally and generally trained to be deployed at a plurality of different clinical centers according to preferences associated with a global population of clinicians, and without regard to the local preferences of the clinician at each particular clinical center.

Disclosure of Invention

In accordance with one or more embodiments, systems and methods are described for automatically identifying anatomical landmarks in medical images based on local preferences associated with particular clinical sites. A medical image for performing a medical procedure is received. Anatomical landmarks are identified in medical images using a pre-trained machine learning algorithm. Feedback related to the identified anatomical landmark is received from a user associated with the particular clinical site. The feedback is received during a normal workflow for performing the medical procedure. Retraining the pre-trained machine learning algorithm based on the received feedback such that the retrained machine learning algorithm can be trained according to local preferences associated with the particular clinical site.

According to one embodiment, feedback related to the identified anatomical landmark is received without prompting the user.

According to one embodiment, the pre-trained machine learning algorithm is not trained according to local preferences associated with a particular clinical site, but rather is trained according to preferences associated with a general population of clinicians.

According to one embodiment, the pre-trained machine learning algorithm is retrained locally at a particular clinical site.

According to one embodiment, the feedback includes an input from the user correcting the position of the identified anatomical landmark and an input from the user rejecting the identified anatomical landmark.

According to an embodiment, a further medical image for performing a further medical procedure is received. Certain anatomical landmarks are identified in another medical image using a retrained machine learning algorithm. Another medical procedure is performed based on the identified certain anatomical landmarks.

These and other advantages of the present invention will be apparent to those of ordinary skill in the art by reference to the following detailed description and the accompanying drawings.

Drawings

FIG. 1 illustrates a clinical site in accordance with one or more embodiments;

FIG. 2 illustrates a method for retraining a pre-trained machine learning algorithm for automatically identifying anatomical landmarks in medical images according to local preferences associated with a particular clinical site, in accordance with one or more embodiments;

FIG. 3 illustrates a method for identifying anatomical landmarks in medical images using retrained machine learning algorithms in accordance with one or more embodiments;

FIG. 4 illustrates a workflow for retraining a machine learning algorithm to identify anatomical landmarks in medical images in accordance with one or more embodiments; and

FIG. 5 shows a high-level block diagram of a computer.

Detailed Description

The present invention relates generally to a method and system for continuous learning for automatic view planning for image acquisition. Embodiments of the present invention are described herein to give a visual understanding of such methods and systems. Digital images are typically composed of a digital representation of one or more objects (or shapes). The digital representation of an object is generally described herein in terms of identifying and manipulating the object. Such manipulations are virtual manipulations performed in the memory or other circuitry/hardware of a computer system. Thus, it should be understood that embodiments of the present invention may be performed by a computer system using data stored within the computer system.

Fig. 1 illustrates a clinical site 100 in accordance with one or more embodiments. The clinical site 100 may be, for example, a hospital, an imaging room, a site associated with an imaging device, a medical clinic, or any other clinical site. In one embodiment, the clinical site 100 may include a plurality of geographically remote sites, such as, for example, related sites affiliated with the same hospital or medical practice. The clinical site 100 includes a workstation 102 for assisting a clinician (e.g., a doctor, nurse, medical professional, or any other user) in performing a medical procedure on a patient 106 (or any other subject). Workstation 102 may be implemented using any suitable computing device, such as, for example, computer 502 of fig. 5.

The workstation 102 may receive medical images of a patient 106 from one or more medical imaging systems 104 to perform a medical procedure. The medical imaging system 104 may be of any modality, such as, for example, two-dimensional (2D) or three-dimensional (3D) Computed Tomography (CT), x-ray, Magnetic Resonance Imaging (MRI), Ultrasound (US), Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), or any other suitable modality or combination of modalities. In another embodiment, the workstation 102 may receive the images by loading previously stored images of the patient acquired using the medical imaging system 104.

In an exemplary embodiment, the medical procedure to be performed on the patient 106 is a cardiac cine MRI examination and the medical imaging system 104 is an MRI system. A cardiac cine MRI examination typically requires long and short axis views of the heart of the patient 106. Before acquiring a complete MRI image of the heart, localizer scans are acquired to identify the position of the heart, specify long and short axis views of the heart, and perform other view planning tasks. To locate the heart and define the long and short axis views of the heart, anatomical landmarks are identified in 2D slices extracted from the 3D volume scanned by the localizer. Exemplary anatomical landmarks identified from the 2D slice include the left and right ventricular apices, the mitral valve point defining the left ventricular base plane, the right ventricular insertion point, and the like.

Anatomical landmarks may be identified from 2D slices or other medical images using one or more machine learning algorithms. The machine learning algorithm may be deployed at the clinical site 100, for example, as part of the medical imaging system 104 or the workstation 102. The machine learning algorithm is pre-trained offsite (i.e., not at the clinical site 100) before being deployed at the clinical site 100. The pre-trained machine learning algorithm is trained centrally to recognize anatomical landmarks in medical images according to preferences of a general population of clinicians and not according to local preferences associated with a particular clinical site (e.g., local preferences of clinicians associated with the clinical site 100). The preferences of the clinician are reflected in training data defining how anatomical landmarks should be identified in the medical image (i.e., which pixels in the medical image should be associated with anatomical landmarks). In one embodiment, the pre-trained machine learning algorithm may be, for example, a conventional machine learning algorithm trained and applied to identify anatomical landmarks in medical images as is known in the art. Based on the identified anatomical landmarks, a view/slice prescription (description) is calculated and presented to the clinician. The clinician may then provide user feedback by, for example, confirming, rejecting, or editing the automatically specified views/slices.

To account for local preferences associated with the clinical site 100 (e.g., preferences of a clinician associated with the clinical site 100), embodiments of the present invention provide local site (i.e., at the clinical site 100) retraining of machine learning algorithms based on user feedback. The clinician is not explicitly prompted for user feedback, and may not know that such user feedback is used to retrain the machine learning algorithm. Instead, user feedback is received from the clinician during the course of a normal clinical workflow for performing the medical procedure. Embodiments of the present invention may be employed at different clinical sites to enable automatic identification of anatomical landmarks according to local preferences associated with the respective clinical site. Embodiments of the present invention enable faster release cycles of updates to machine learning algorithms because machine learning algorithms can be retrained locally in the field at any time without having to wait for centrally managed software updates. Advantageously, more clinical sites will employ automatic identification of anatomical landmarks for view planning, thereby standardizing and accelerating the image acquisition workflow.

It should be appreciated that although the embodiments discussed herein are discussed with respect to view planning in which anatomical landmarks in medical images are automatically identified for MRI image acquisition based on local preferences associated with a clinical site, the invention is not so limited. Embodiments of the present invention may be used to automatically identify any object of interest based on preferences associated with any entity.

Fig. 2 illustrates a method 200 for retraining a pre-trained machine learning algorithm for automatically identifying anatomical landmarks in medical images according to local preferences associated with a particular clinical site, in accordance with one or more embodiments. The method 200 will be discussed with reference to the clinical site 100 of fig. 1. In one embodiment, the steps of the method 200 may be performed by the workstation 102 or the medical imaging system 104 of fig. 1.

At step 202, a medical image is received. The medical image may be used to perform a medical procedure at a particular clinical site (e.g., clinical site 100 of fig. 1). The particular clinical site may be, for example, a hospital, an imaging room, a location associated with an imaging device, a medical clinic, or any other clinical site.

The medical image may be of any suitable modality. In one embodiment, the medical image is a 2D slice extracted from a 3D volume scanned by a localizer from an MRI imaging system for performing a cine cardiac MRI examination. The medical image may be received directly from a medical imaging system (e.g., medical imaging system 104 of fig. 1). Alternatively, the medical image may be received by loading a medical image previously acquired from the medical imaging system from a storage or memory of the computer system, or receiving a medical image that has been transmitted from a remote computer system. In one embodiment, the medical imaging system is associated with (e.g., located at) a particular clinical site in which a medical procedure is being performed.

At step 204, one or more anatomical landmarks are identified in the medical image using a pre-trained machine learning algorithm. The anatomical landmark may be any object of interest in the medical image. For example, the anatomical landmarks may include the left and right ventricular apices, the mitral valve point defining the left ventricular base plane, and the right ventricular insertion point. The pre-trained machine learning algorithm may also calculate a plane or view prescription at step 204. The pre-trained machine learning algorithm may be any suitable machine learning algorithm for identifying anatomical landmarks in medical images, such as, for example, convolutional neural networks, deep reinforcement learning, recurrent neural networks, and other deep learning algorithms. In one embodiment, the pre-trained machine learning algorithm may be a conventional machine learning algorithm known in the art for identifying anatomical landmarks in medical images.

A pre-trained machine learning algorithm identifies one or more anatomical landmarks by associating pixels in a medical image with one or more anatomical structures. The pre-trained machine learning algorithm is trained according to general preferences of a broad population of clinicians, which define how anatomical landmarks should be recognized in medical images. The pre-trained machine learning algorithm is not trained according to local preferences associated with a particular clinical site. For example, a clinician or other user associated with a particular clinical site may have different preferences regarding which pixels in a medical image should be associated with one or more landmarks than those associated with other clinical sites. However, the pre-trained machine learning algorithm is not trained according to those local preferences associated with a particular clinical site.

At step 206, feedback regarding the identified anatomical landmark is received from a user (e.g., a clinician) associated with the particular clinical site. The feedback may be in any suitable form. In one embodiment, the feedback may be explicit feedback, where the user corrects the position of anatomical landmarks in the medical image identified by the pre-trained machine learning algorithm. For example, the user may correct the position of an anatomical landmark in the medical image by editing which pixels in the medical image are or are not associated with the anatomical landmark. In another embodiment, the feedback may also be implicit feedback, where the user does not correct the position of the anatomical landmark in the medical image, but rejects or overrides (overrides) the position of the anatomical landmark in the medical image identified by the pre-trained machine learning algorithm with the manually identified anatomical landmark.

At step 208, optionally, a medical procedure is performed based on the identified anatomical landmarks and the received feedback.

Step 202-. In one embodiment, step 202-208 is repeated a predetermined number of iterations before proceeding to step 210. This will allow a sufficient amount of feedback to be acquired at step 210 for retraining the pre-trained machine learning algorithm. In another embodiment, if the pre-trained machine learning algorithm is not frequently used (i.e., if the number of iterations of step 202-208 is less than the predetermined number of iterations), the pre-trained machine learning algorithm is automatically retrained at step 210.

At step 210, the pre-trained machine learning algorithm is retrained based on the received feedback, the identified anatomical landmarks and the medical images identified by the pre-trained machine learning algorithm. For example, in one embodiment, where the feedback is a correction to the position of the identified landmarks, the identified anatomical landmarks modified by the feedback are used as the ground truth for the medical image. In another example, where the feedback is a rejection of the location of the anatomical landmark, the rejection is used as a negative example for retraining the pre-trained machine learning algorithm. Advantageously, the retrained machine learning algorithm is trained according to local preferences associated with a particular clinical site.

To enable local, on-site retraining of the pre-trained machine learning algorithm, the pre-trained machine learning algorithm is deployed at a particular clinical site (e.g., on the medical imaging system 104 or the workstation 102) using a training framework. The training framework may include training code for retraining the pre-trained machine learning algorithm. In one embodiment, the pre-trained machine learning algorithm may be retrained in accordance with the workflow 400 of FIG. 4.

In one embodiment, step 202 is performed during a normal procedure or workflow for performing a medical procedure at a particular clinical site 208. Thus, the feedback received at step 206 is received from the user during a normal workflow for performing the medical procedure. The user is not prompted or asked for feedback, and the user may not know that the feedback is to be used to retrain the pre-trained machine learning algorithm at step 210. In another embodiment, step 202-.

In one embodiment, at step 210, the pre-trained machine learning algorithm may be retrained using not only feedback from users associated with a particular clinical site, but also feedback from other clinical sites. For example, a particular clinical site and other clinical sites may be related clinical sites that frequently work together or collaborate with each other. Thus, in this embodiment, the pre-trained machine learning algorithm may be retrained in a distributed manner using feedback accumulated from multiple clinical sites at step 210.

In one embodiment, at step 204, a pre-trained machine learning algorithm calculates a plurality of different candidate prescriptions for the view, and the user selects one of the candidate prescriptions. Instead of retraining the pre-trained machine learning model at step 210, the machine learning model associated with the selected candidate prescription is used as the retrained machine learning model resulting from step 210. The different candidate prescriptions may be based on multiple machine learning algorithms trained with slightly different definitions of the identification of anatomical landmarks, or may be based on a single machine learning algorithm that outputs probabilistic locations of anatomical landmarks. The candidate prescriptions may be presented to the user in an order customized for the particular clinical site. In one embodiment, a Deep Reinforcement Learning (DRL) algorithm may be employed to determine an order in which candidate prescriptions are presented to a user. Based on the user's behavior, the DRL algorithm can be trained to learn the user's preferences, which can be used to dynamically change the order in which candidate prescriptions are presented.

In one embodiment, multiple machine learning algorithms are deployed at a particular clinical site (e.g., at the workstation 102 or the medical imaging system 104). Each of the plurality of machine learning algorithms is trained with a different definition of the identification of anatomical landmarks. One of a plurality of machine learning algorithms may be used to identify anatomical landmarks in medical images (e.g., at step 204 of fig. 2). Based on feedback received from the user regarding the identified anatomical landmark (e.g., at step 206 of fig. 2), one of a plurality of machine learning algorithms may be automatically selected. Thus, instead of retraining the pre-trained machine learning algorithm at step 210, the selected machine learning algorithm is used as the retrained machine learning model resulting from step 210.

Fig. 3 illustrates a method 300 for identifying anatomical landmarks in medical images using a retrained machine learning algorithm in accordance with one or more embodiments. The method 300 will be discussed with reference to the clinical site 100 of fig. 1. In one embodiment, the steps of the method 300 may be performed by the workstation 102 or the medical imaging system 104 of fig. 1.

At step 302, a medical image is received. The medical image may be used to perform a medical procedure at a particular clinical site (e.g., clinical site 100 of fig. 1). The medical image may be of any suitable modality. For example, the medical image may be a 2D slice extracted from a 3D volume scanned by a localizer from an MRI imaging system for performing a cine cardiac MRI examination.

At step 304, anatomical landmarks are identified in the medical image using a retrained machine learning algorithm. The retrained machine learning algorithm is trained according to local preferences associated with a particular clinical site. Retraining the retrained machine learning algorithm from a pre-trained machine learning algorithm based on feedback received from a user associated with a particular clinical site. The feedback relates to another anatomical landmark identified using a pre-trained machine learning algorithm and received during a normal workflow for performing another medical procedure. In one embodiment, the retrained machine learning algorithm is the retrained machine learning algorithm resulting from step 210 of fig. 2.

At step 306, a medical procedure is performed based on the identified landmarks.

Fig. 4 illustrates a workflow 400 for retraining a pre-trained machine learning algorithm to identify anatomical landmarks in medical images in accordance with one or more embodiments. The workflow 400 may be performed during an offline or training phase. Once retrained in accordance with the workflow 400, the retrained machine learning algorithm may be applied during an online or inference phase. For example, a machine learning algorithm retrained in accordance with the workflow 400 may be applied at step 304 of fig. 3.

At step 402, a training image is received. In one embodiment, the training images include the medical image received at step 202 of fig. 2, anatomical landmarks identified at step 204 by a pre-trained machine learning algorithm, and feedback received at step 206. The identified anatomical landmarks and the received feedback are used as a ground truth for identifying anatomical landmarks in the received medical image.

At step 404, features of interest are extracted from the medical image.

At step 406, the pre-trained machine learning algorithm is retrained to identify anatomical landmarks based on the features of interest.

The systems, apparatus, and methods described herein may be implemented using digital circuitry, or using one or more computers using well-known computer processors, memory units, storage devices, computer software, and other components. Generally, a computer includes a processor for executing instructions and one or more memories for storing instructions and data. The computer can also include or be coupled to one or more mass storage devices, such as one or more magnetic disks, internal hard disks and removable disks, magneto-optical disks, and the like.

The systems, apparatuses, and methods described herein may be implemented using a computer operating in a client-server relationship. Typically, in such systems, client computers are located remotely from server computers and interact via a network. The client-server relationship may be defined and controlled by computer programs running on the respective client and server computers.

The systems, apparatuses, and methods described herein may be implemented within a network-based cloud computing system. In such a network-based cloud computing system, a server or another processor connected to a network communicates with one or more client computers via the network. For example, a client computer may communicate with a server via a web browser application program that resides on and operates on the client computer. Client computers may store data on servers and access data via a network. A client computer may send a request for data or a request for an online service to a server via a network. The server may perform the requested service and provide data to the client computer(s). The server may also transmit data suitable for causing the client computer to perform specified functions (e.g., perform calculations, display specified data on a screen, etc.). For example, the server may send a request adapted to cause a client computer to perform one or more of the steps or functions of the methods and workflows described herein, including one or more of the steps or functions of fig. 2-4. Certain steps or functions of the methods and workflows described herein, including one or more of the steps or functions of fig. 2-4, may be performed by a server or by another processor in a network-based cloud computing system. Certain steps or functions of the methods and workflows described herein, including one or more of the steps of fig. 2-4, may be performed by a client computer in a network-based cloud computing system. The steps or functions of the methods and workflows described herein, including one or more of the steps of fig. 2-4, can be performed by a server and/or client computer in a network-based cloud computing system in any combination.

The systems, apparatus, and methods described herein may be implemented using a computer program product tangibly embodied on an information carrier, e.g., on a non-transitory machine-readable storage device, for execution by a programmable processor; and the methods and workflow steps described herein including one or more of the steps or functions of fig. 2-4 may be implemented using one or more computer programs executable by such a processor. A computer program is a set of computer program instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result. A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

A high-level block diagram of an example computer 502 that may be used to implement the systems, apparatus, and methods described herein is depicted in FIG. 5. The computer 502 includes a processor 504 operatively coupled to a data storage device 512 and a memory 510. Processor 504 controls the overall operation of computer 502 by executing computer program instructions that define such operation. The computer program instructions may be stored in a data storage device 512 or other computer readable medium and loaded into memory 510 when execution of the computer program instructions is desired. Thus, the method and workflow steps or functions of fig. 2-4 may be defined by computer program instructions stored in memory 510 and/or data storage device 512 and controlled by processor 504 executing the computer program instructions. For example, the computer program instructions may be implemented as computer executable code programmed by one skilled in the art to perform the method and workflow steps or functions of fig. 2-4. Thus, by executing the computer program instructions, the processor 504 performs the method and workflow steps or functions of fig. 2-4. The computer 504 may also include one or more network interfaces 506 for communicating with other devices via a network. The computer 502 may also include one or more input/output devices 508 that enable user interaction with the computer 502 (e.g., display, keyboard, mouse, speakers, buttons, etc.).

Processor 504 may include both general and special purpose microprocessors and may be a single processor or one of multiple processors of computer 502. Processor 504 may include, for example, one or more Central Processing Units (CPUs). The processor 504, data storage device 512, and/or memory 510 may include, be supplemented by, or incorporated in, one or more application-specific integrated circuits (ASICs), and/or one or more field-programmable gate arrays (FPGAs).

The data storage device 512 and the memory 510 each include a tangible, non-transitory computer-readable storage medium. The data storage 512 and memory 510 may each include high speed random access memory, such as Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), double data rate synchronous dynamic random access memory (DDR RAM), or other random access solid state memory devices, and may include non-volatile memory, such as one or more magnetic disk storage devices, such as internal hard disks and removable disks, magneto-optical disk storage devices, flash memory devices, semiconductor memory devices, such as Erasable Programmable Read Only Memory (EPROM), Electrically Erasable Programmable Read Only Memory (EEPROM), compact disk read only memory (CD-ROM), digital versatile disk read only memory (DVD-ROM) disks, or other non-volatile solid state memory devices.

Input/output devices 508 may include peripheral devices such as printers, scanners, display screens, and the like. For example, input/output devices 508 may include a display device, such as a Cathode Ray Tube (CRT) or Liquid Crystal Display (LCD) monitor, a keyboard and a pointing device, such as a mouse or a trackball, by which a user may provide input to computer 502.

Any or all of the systems and apparatus discussed herein, including elements of the medical imaging system 104 and workstation 102 of fig. 1, may be implemented using one or more computers, such as the computer 502.

Those skilled in the art will recognize that an implementation of an actual computer or computer system may have other structures and may contain other components as well, and that FIG. 5 is a high-level representation of some components of such a computer for illustrative purposes.

The foregoing detailed description is to be understood as being in every respect illustrative and exemplary, but not restrictive, and the scope of the invention disclosed herein is not to be determined from the detailed description, but rather from the claims as interpreted according to the full breadth permitted by the patent laws. It is to be understood that the embodiments shown and described herein are merely illustrative of the principles of this invention and that various modifications may be implemented by those skilled in the art without departing from the scope and spirit of the invention. Various other combinations of features may be implemented by those skilled in the art without departing from the scope and spirit of the invention.