System and method for reconstruction of 3-dimensional anatomy

Abstract

SYSTEM AND METHOD FOR RECONSTRUCTION OF 3-DIMENSIONAL ANATOMY Present disclosure relates to a system and method for reconstructing 3D anatomy. The method comprises receiving at least one of an axial X-ray image and a sagittal X-ray image of a spine of a patient from one or more sources. Thereafter, the method comprises determining at least one measurement angle using at least one of the axial X-ray image and the sagittal X-ray image and at least one historical 3D spine anatomical model in a database. Subsequently, the method comprises determining at least one of anatomical features and skeletal features of the spine using at least one of the axial X-ray image and the sagittal X-ray image of the spine. Lastly, the method comprises reconstructing a patient specific 3D spine anatomical model based on the at least one measurement angle and at least one of the anatomical features and the skeletal features of the spine using a trained CNN model.

Description

[0001] “SYSTEM AND METHOD FOR RECONSTRUCTION OF 3-DIMENSIONAL ANATOMY”

[0002] TECHNICAL FIELD

[0003] [1] The present subject matter generally relates to image processing, more particularly, to a system and a method for reconstruction of 3 -Dimensional (3D) anatomy.

[0004] BACKGROUND

[0005] [2] Medical imaging is a tool that is used for detection and diagnosis of various diseases and injuries of a patient. Conventionally, Computed Tomography (CT) scans (by way of 3D CT scanners) is used to determine anatomical and skeletal features of a patient, that are used to identify disease or injury within various regions of the body of the patient. Modelling 3D shapes from 2- Dimensional (2D) images has been limited to views with overlapping correspondences or stereo views. CT scanning requires imaging of overlapping frames for reconstruction of 3D image of the anatomical and skeletal features of the patient. Thus, the CT scanning requires a large amount of data to be captured and processed for the 3D imaging of the anatomical and skeletal features of the patient. Further, CT scanners are very expensive and thus are not affordable by all. Hence, a system and a method for determining to comprehend anatomical and skeletal features from 2D viewpoints and reconstruct a 3D anatomy without the need of the expensive conventional techniques that require correspondence of overlapping scans or stereo views to be processed is an ongoing effort.

[0006] [3] The information disclosed in this background of the disclosure section is for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

[0007] SUMMARY

[0008] [4] Embodiments of the present disclosure address the problems associated with the conventional medical imaging techniques. [5] In an embodiment, there is a method for reconstructing 3 -Dimensional (3D) anatomy. The method comprising receiving at least one of an axial X-ray image and a sagittal X-ray image of a spine of a patient from one or more sources. Thereafter, the method comprising determining at least one measurement angle using at least one of the axial X-ray images and the sagittal X-ray image and at least one historical 3D spine anatomical model in a database. Subsequently, the method comprising determining at least one of anatomical features and skeletal features of the spine using at least one of the axial X-ray images and the sagittal X-ray image of the spine. Lastly, the method comprising reconstructing a patient specific 3D spine anatomical model based on the at least one measurement angle and at least one of the anatomical features and the skeletal features of the spine using a trained Convolutional Neural Network (CNN) model.

[0009] [6] In another embodiment, there is a system for reconstructing 3D anatomy. The system comprising a processor, and a memory communicatively coupled to the processor. The processor is configured to receive at least one of an axial X-ray image and a sagittal X-ray image of a spine of a patient from one or more sources. Thereafter, the processor is configured to determine at least one measurement angle using at least one of the axial X-ray image and the sagittal X-ray image and at least one historical 3D spine anatomical model in a database. Subsequently, the processor is configured to determine at least one of anatomical features and skeletal features of the spine using at least one of the axial X-ray image and the sagittal X-ray image of the spine. Lastly, the processor is configured to reconstruct a patient specific 3D spine anatomical model based on the at least one measurement angle, and at least one of the anatomical features and the skeletal features of the spine using a trained CNN model.

[0010] [7] In yet another embodiment, the determining the at least one measurement angle using at least one of the axial X-ray image and the sagittal X-ray image and the at least one historical 3D spine anatomical model in the database comprises comparing the at least one of the axial X-ray image and the sagittal X-ray image with the at least one historical 3D spine anatomical model in the database, and determining the at least one measurement angle based on the comparison.

[0011] [8] In an embodiment, the patient specific 3D spine anatomical model refers to a 3D anatomical model that is specific in at least one of a spine size and a spine shape of the patient. [9] In another embodiment, a measurement angle refers to an angle at which at least one of the axial X-ray image and the sagittal X-ray image of the spine of the patient is captured.

[0012]

[0010] In yet another embodiment, a CNN model is trained for receiving a plurality of historical axial X-ray images and corresponding historical sagittal X-ray images of a spine captured at different measurement angles of at least one patient from the one or more sources. Thereafter, the CNN model is trained for determining historical anatomical features and corresponding historical skeletal features of the spine for each measurement angle of the at least one patient using the plurality of historical axial X-ray images and corresponding historical sagittal X-ray images of the spine captured at different measurement angles of the at least one patient. Subsequently, the CNN model is trained for reconstructing the at least one historical 3D spine anatomical model based on the historical anatomical features and the corresponding historical skeletal features of the spine of the at least one patient.

[0013]

[0011] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

[0014] BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

[0015]

[0012] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and together with the description, serve to explain the disclosed principles. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and / or methods in accordance with embodiments of the present subject matter are now described below, by way of example only, and with reference to the accompanying figures.

[0016]

[0013] FIG. 1 illustrates an environment for reconstructing 3D anatomy in accordance with some embodiments of the present disclosure.

[0014] FIG. 2 shows a detailed block diagram of a system for reconstructing 3D anatomy in accordance with some embodiments of the present disclosure.

[0017]

[0015] FIGS. 3 A to 3B illustrate flowcharts showing a method for reconstructing 3D anatomy in accordance with some embodiments of the present disclosure.

[0018]

[0016] FIG. 4 illustrates a block diagram of a computer system 400 for implementing embodiments consistent with the present disclosure

[0019]

[0017] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flowcharts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether or not such computer or processor is explicitly shown.

[0020] DETAILED DESCRIPTION

[0021]

[0018] In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

[0022]

[0019] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[0023]

[0020] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises... a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or method.

[0024]

[0021] In the following detailed description of embodiments of the disclosure, reference is made to the accompanying drawings which illustrates specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[0025]

[0022] Overview:

[0026]

[0023] In an aspect of the present disclosure, a system includes a user device and a server communicatively coupled by way of a communication network. The user device further includes a sensing unit that is configured to capture a first set of images corresponding to an axial view of anatomy of a user, and a second set of images corresponding to a sagittal view of the anatomy of the user. The server includes a processing circuitry that is configured to determine a set of anatomical features and a set of skeletal features from the first and second sets of images. The processing circuitry is further configured to determine a 3D anatomy of the user based on the set of anatomical features and the set of skeletal features. Furthermore, the processing circuitry is configured to generate one or more morphological slices based on the 3D anatomy of the user. Furthermore, the processing circuitry is configured to determine a set of anatomical measures for the set of anatomical features and a set of skeletal measures for the set of skeletal features based on the one or more morphological slices.

[0027]

[0024] In another aspect of the present disclosure, a method includes capturing, by way of a sensing unit of a user device, a first set of images corresponding to an axial view of anatomy of a user, and a second set of images corresponding to a sagittal view of the anatomy of the user. The method further includes determining, by way of a processing circuitry of a user device, a set of anatomical features and a set of skeletal features from the first and second sets of images. Furthermore, the method includes determining, by way of the processing circuitry, a 3D anatomy of the user based on the set of anatomical features and the set of skeletal features. Furthermore, the method includes generating, by way of the processing circuitry, one or more morphological slices based on the 3D anatomy of the user. Furthermore, the method includes determining, by way of the processing circuitry, a set of anatomical measures for the set of anatomical features and a set of skeletal measures for the set of skeletal features based on the one or more morphological slices.

[0028]

[0025] The system may include a user device and a server, that may be communicatively coupled to each other by way of a communication network. In some aspects of the present disclosure, the user device and the server may be communicatively coupled to each other through one or more wired and / or wireless communication networks established therebetween. The user device may be configured to capture a first set of images corresponding to an axial view of anatomy of a user, and a second set of images corresponding to a sagittal view of the anatomy of the user. The first set of images and the second set of images may be in a format of 2D Computer Assisted Radio Monitoring (C-ARM) images. The user device may further be configured to enable the user to select a set of anatomical features and a set of skeletal features. Furthermore, the user device may be configured to display (or present) a set of anatomical measures for the set of anatomical features and a set of skeletal measures for the set of skeletal features to the user.

[0029]

[0026] In an exemplary aspect of the present disclosure, the user device may include a sensing unit, a user interface, a device processing unit, a device memory, a console, and a first communication interface.

[0030]

[0027] In some aspects of the present disclosure, the sensing unit may include but not limited to one or more sensors (preferably, one or more radio camera sensors) that may be configured to capture the first set of images corresponding to an axial view of anatomy of the user, and the second set of images corresponding to the sagittal view of the anatomy of the user. Aspects of the present disclosure are intended to include or otherwise cover any type of sensors including known, related art, and / or later developed technologies.

[0028] Examples of the one or more radio camera sensors may include but are not limited to an X-Ray scanner, a CT scanner camera, a medical imaging sensor, and the like. Aspects of the present disclosure are intended to include or otherwise cover any type of sensors capable of medical imaging including known, related art, and / or later developed technologies.

[0031]

[0029] The user interface may include an input interface (not shown) for receiving inputs from the user. Examples of the input interface of the user interface may include, but are not limited to, a touch interface, a mouse, a keyboard, a motion recognition unit, a gesture recognition unit, a voice recognition unit, or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the input interface including known, related art, and / or later developed technologies. The user interface may further include an output interface (not shown) for displaying (or presenting) an output to the user. Examples of the output interface of the user interface may include, but are not limited to, a digital display, an analog display, a touch screen display, a graphical user interface, a website, a webpage, a keyboard, a mouse, a light pen, an appearance of a desktop, and / or illuminated characters. Aspects of the present disclosure are intended to include and / or otherwise cover any type of the output interface including known and / or related, or later developed technologies.

[0032]

[0030] The device processing unit may include suitable logic, instructions, circuitry, interfaces, and / or codes for executing various operations, such as the operations associated with the user device, and the like. In some aspects of the present disclosure, the device processing unit may utilize one or more processors such as Arduino or raspberry pi or the like. Further, the device processing unit may be configured to control one or more operations executed by the user device in response to the input received at the user interface from the user. Examples of the device processing unit may include, but are not limited to, an application-specific integrated circuit (ASIC) processor, a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a field-programmable gate array (FPGA), a Programmable Logic Control unit (PLC), and the like. Aspects of the present disclosure are intended to include or otherwise cover any type of processing unit including known, related art, and / or later developed processing units.

[0031] The device memory may be configured to store the logic, instructions, circuitry, interfaces, and / or codes of the device processing unit, data associated with the user device, and data associated with the system. Examples of the device memory may include, but are not limited to, a Read-Only Memory (ROM), a Random- Access Memory (RAM), a flash memory, a removable storage drive, a hard disk drive (HDD), a solid-state memory, a magnetic storage drive, a Programmable Read Only Memory (PROM), an Erasable PROM (EPROM), and / or an Electrically EPROM (EEPROM). Aspects of the present disclosure are intended to include or otherwise cover any type of device memory including known, related art, and / or later developed memories.

[0033]

[0032] The console may be configured as a computer -executable application, to be executed by the device processing unit. The console may include suitable logic, instructions, and / or codes for executing various operations and may be controlled by the server. The one or more computer executable applications may be stored in the device memory. Examples of the one or more computer executable applications 10 may include, but are not limited to, an audio application, a video application, a social media application, a navigation application, or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the computer executable application including known, related art, and / or later developed computer executable applications.

[0034]

[0033] The first communication interface may be configured to enable the user device to communicate with the server over the communication network. Examples of the first communication interface may include, but are not limited to, a modem, a network interface such as an Ethernet card, a communication port, and / or a Personal Computer Memory Card International Association (PCMCIA) slot and card, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, and a local buffer circuit. It will be apparent to a person of ordinary skill in the art that the first communication interface may include any device and / or apparatus capable of providing wireless or wired communications between the user device with the server.

[0034] The processing circuitry may be configured to determine the set of anatomical features and the set of skeletal features from the first and second sets of images. The processing circuitry may further configured to determine a 3D anatomy of the user based on the set of anatomical features and the set of skeletal features. In some aspects of the present disclosure, the processing circuitry may further be configured to generate parametric values and / or weights of a first engine (not shown) of the processing circuitry to determine the 3D anatomy of the user. The first engine may be trained by way of a training dataset that may include a plurality of anatomical and skeletal images. The training dataset may be stored in the database.

[0035]

[0035] In some aspects of the present disclosure, to generate the parameters or weights of the first engine (i.e., training), the processing circuitry may be configured to assign one or more anatomical landmarks and / or one or more skeletal landmarks to each image of the plurality of anatomical and skeletal images, respectively. The processing circuitry may further be configured to generate a set of anatomical priors based on the one or more anatomical landmarks assigned to each image of the plurality of anatomical and skeletal images. In some aspects of the present disclosure, the processing circuitry may be configured to generate the parameters or weights of the first engine using one or more deep learning techniques. Preferably, the processing circuitry may be configured to generate the first engine by way of at least one of, an encoder (not shown) of the processing circuitry, a deep convolutional network (not shown) of the processing circuitry, a decoder (not shown) of the processing circuitry, and a combination thereof.

[0036]

[0036] Furthermore, the processing circuitry may be configured to generate one or more morphological slices based on the 3D anatomy of the user. Furthermore, the processing circuitry may be configured to determine a set of anatomical measures for the set of anatomical features and a set of skeletal measures for the set of skeletal features based on the one or more morphological slices. In some aspects of the present disclosure, the set of anatomical measures and the set of skeletal measures may include but not limited to Transverse Pedicle Width (TPW), Sagittal Pedicle Width (SPW), Minimum Pedicle Diameter (MPD), and the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the anatomical measures and / or skeletal measures including known, related art, and / or related to later developed technologies. In some aspects of the present disclosure, the processing circuitry may be configured to determine the set of anatomical measures and the set of skeletal measures by way of at least one of, a shape matching process, a 3D model fitting process, and a combination thereof.

[0037]

[0037] The processing circuitry may include suitable logic, instructions, circuitry, interfaces, and / or codes for executing various operations of the system. The processing circuitry may be configured to host and enable the console running on (or installed on) the user device to execute the operations associated with the system by communicating one or more commands and / or instructions over the communication network. Examples of the processing circuitry may include, but are not limited to, an ASIC processor, a RISC processor, a CISC processor, a FPGA, and the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the processing circuitry including known, related art, and / or later developed processing circuitries.

[0038]

[0038] The database may be configured to store the logic, instructions, circuitry, interfaces, and / or codes of the processing circuitry for executing various operations. The database may be further configured to store therein, data associated with user registered with the system. Specifically, the database may be configured to store training dataset that may include a plurality of anatomical and skeletal images that may be used for training the first engine of the processing circuitry. Aspects of the present disclosure are intended to include and / or otherwise cover any type of the data associated with the system. In some aspects of the present disclosure, the database may further be configured to store the data or the report(s) based on the one or more abnormalities on the powerline generated by the processing circuitry. Examples of the database may include but are not limited to, a ROM, a RAM, a flash memory, a removable storage drive, a HDD, a solid-state memory, a magnetic storage drive, a PROM, an EPROM, and / or an EEPROM. In some aspects, a set of centralized or distributed network of peripheral memory devices may be interfaced with the server, as an example, on a cloud server. Aspects of the present disclosure are intended to include or otherwise cover any type of the database including known, related art, and / or later developed databases.

[0039]

[0039] The communication network may include suitable logic, circuitry, and interfaces that may be configured to provide a plurality of network ports and a plurality of communication channels for transmission and reception of data related to operations of various entities (such as the user device and the server) of the system. Each network port may correspond to a virtual address (or a physical machine address) for transmission and reception of the communication data. For example, the virtual address may be an Internet Protocol Version 4 (IPV4) (or an IPV6 address) and the physical address may be a Media Access Control (MAC) address. The communication network may be associated with an application layer for implementation of communication protocols based on one or more communication requests from the user device and the server. The communication data may be transmitted or received, via the communication protocols. Examples of the communication protocols may include, but are not limited to, Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), Simple Mail Transfer Protocol (SMTP), Domain Network System (DNS) protocol, Common Management Interface Protocol (CMIP), Transmission Control Protocol and Internet Protocol (TCP / IP), User Datagram Protocol (UDP), Fong Term Evolution (ETE) communication protocols, or any combination thereof.

[0040]

[0040] In an aspect of the present disclosure, the communication data may be transmitted or received via at least one communication channel of a plurality of communication channels in the communication network. The communication channels may include, but are not limited to, a wireless channel, a wired channel, a combination of wireless and wired channel thereof. The wireless or wired channel may be associated with a data standard which may be defined by one of a Local Area Network (LAN), a Personal Area Network (PAN), a Wireless Local Area Network (WLAN), a Wireless Sensor Network (WSN), Wireless Area Network (WAN), Wireless Wide Area Network (WWAN), a metropolitan area network (MAN), a satellite network, the Internet, a fiber optic network, a coaxial cable network, an infrared (IR) network, a radio frequency (RF) network, and a combination thereof. Aspects of the present disclosure are intended to include or otherwise cover any type of communication channel, including known, related art, and / or later developed technologies.

[0041]

[0041] The system by way of the sensing unit of the user device may capture the first set of images corresponding to an axial view of anatomy of the user, and the second set of images corresponding to a sagittal view of the anatomy of the user. The system, by way of the processing circuitry may determine the set of anatomical features and the set of skeletal features from the first and second sets of images. In some aspects of the present disclosure, the system by way of the processing circuitry may generate the parametric values and / or weights of the first engine to determine the 3D anatomy of the user. The system by way of the processing circuitry may further train the first engine by way of a training dataset that may include a plurality of anatomical and skeletal images. In some aspects of the present disclosure, the training dataset may be stored in the database.

[0042]

[0042] In some aspects of the present disclosure, to generate the parameters or weights of the first engine (i.e., training), the system by way of the processing circuitry may assign one or more anatomical landmarks and / or one or more skeletal landmarks to each image of the plurality of anatomical and skeletal images, respectively. The system by way of the processing circuitry may further generate a set of anatomical priors based on the one or more anatomical landmarks assigned to each image of the plurality of anatomical and skeletal images.

[0043]

[0043] In some aspects of the present disclosure, the system by way of the processing circuitry may be configured to generate the parameters or weights of the first engine using one or more deep learning techniques. Preferably, the processing circuitry may be configured to generate the first engine by way of at least one of, the encoder (not shown) of the processing circuitry, the deep convolutional network (not shown) of the processing circuitry, the decoder (not shown) of the processing circuitry, and a combination thereof.

[0044]

[0044] The system, by way of the processing circuitry may determine the 3D anatomy of the user based on the set of anatomical features and the set of skeletal features. The system, by way of the processing circuitry may generate one or more morphological slices based on the 3D anatomy of the user. The system, by way of the processing circuitry may determine the set of anatomical measures for the set of anatomical features and the set of skeletal measures for the set of skeletal features based on the one or more morphological slices. In some aspects of the present disclosure, the system by way of the processing circuitry may determine the set of anatomical measures and the set of skeletal measures by way of at least one of, a shape matching process, a 3D model fitting process, and a combination thereof.

[0045]

[0045] As mentioned, there remains a need for a system and a method for comprehending the anatomical and the skeletal features from the 2D viewpoints and reconstructing the 3D anatomy of a user. The present aspect, therefore, provides the system and the method for comprehending the anatomical and skeletal features from the 2D viewpoints, and reconstructing the 3D anatomy without the need of the expensive conventional techniques (such as CT scanning) that require correspondence of overlapping scans or stereo views to be processed. As the system facilitates comprehending anatomical and skeletal features from 2D viewpoints and is capable of deciding on the appropriate screw / device dimensions, the system therefore can be used in surgical scenarios. The system further removes the dependencies of CT scanning at a facility to perform severe surgeries. As the system may provide ability to identify the 3D anatomy from 2D C-Arm images, thus the system may facilitate additional information on the structural geometry of the anatomy, which may result in advantages in terms of ease of operation and enhancement of efficacy in surgical scenarios. The system provides the measurement of anatomical and skeletal features from 2D viewpoints (which is traditionally computed from a 3D CT scan by segmenting the slices) by way of 2D C-ARM images, which are projected to 3D anatomy using deep learning pre -trained models. Thus removes the dependency of overlapping images and / or determination of nonoverlapping dependencies.

[0046]

[0046] FIG. 1 illustrates an environment for reconstructing 3D anatomy in accordance with some embodiments of the present disclosure.

[0047]

[0047] As shown in the FIG. 1, the environment 100 includes a user device 101, a communication network 103, a system 105, and a database 113. The user device 101 communicates with the system 105 and the database 113 through the communication network 103.

[0048]

[0048] The user device 101 (also, referred as one or more sources) captures at least one of an axial X-ray image and a sagittal X-ray image of a spine of a patient. In an embodiment, the user device 101 captures a plurality of axial X-ray images and sagittal X-ray images of the spine of the patient. The axial X-ray image and the sagittal X-ray image may be in a format of 2D Computer Assisted Radio Monitoring (C-ARM) image. In an embodiment, the user device 101 may include a sensing unit, a user interface, a device processing unit, a device memory, a console, and a communication interface (not shown in FIG. 1). The sensing unit may include, but not limited to, one or more sensors (preferably, one or more radio camera sensors) that are configured to capture the at least one of the axial X-ray image and the sagittal X-ray image of the spine of the patient or the plurality of axial X-ray images and sagittal X-ray images of the spine of the patient. Examples of the one or more radio camera sensors may include, but are not limited to, an X-Ray scanner, a CT scanner camera, a medical imaging sensor, and the like. Aspects of the present disclosure are intended to include any type of sensors capable of medical imaging including known, related art, and / or later developed technologies. The user interface of the user device 101 may include an input interface (not shown in FIG. 1 ) for receiving inputs from the user. Examples of the input interface of the user interface may include, but are not limited to, a touch interface, a mouse, a keyboard, a motion recognition unit, a gesture recognition unit, a voice recognition unit, or the like. The user interface may further include an output interface (not shown in FIG. 1) for displaying (or presenting) an output to the user. Examples of the output interface of the user interface may include, but are not limited to, a digital display, an analog display, a touch screen display, a graphical user interface, and the like. The device processing unit (not shown in FIG. 1) of the user device 101 may include suitable logic, instructions, circuitry, interfaces, and / or codes for executing various operations, such as the operations associated with the user device 101, and the like. In some aspects of the present disclosure, the device processing unit may utilize one or more processors such as Arduino or Raspberry pi or the like. Further, the device processing unit may be configured to control one or more operations executed by the user device 101 in response to the input received at the user interface from the user. Examples of the device processing unit may include, but are not limited to, an application-specific integrated circuit (ASIC) processor, a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a field -programmable gate array (FPGA), a Programmable Eogic Control unit (PEC), and the like. The device memory (not shown in FIG. 1) may be configured to store the logic, instructions, circuitry, interfaces, and / or codes of the device processing unit, data associated with the user device 101. Examples of the device memory may include, but are not limited to, a Read-Only Memory (ROM), a Random- Access Memory (RAM), a flash memory, a removable storage drive, a hard disk drive (HDD), a solid-state memory, a magnetic storage drive, a Programmable Read Only Memory (PROM), an Erasable PROM (EPROM), and / or an Electrically EPROM (EEPROM). The console (not shown in FIG. 1) of the user device 101 may be configured as a computer -executable application, to be executed by the device processing unit. The console may include suitable logic, instructions, and / or codes for executing various operations and may be controlled by the system 105. The one or more computer executable applications may be stored in the device memory. Examples of the one or more computer executable applications may include, but are not limited to, an audio application, a video application, a social media application, a navigation application, or the like. The communication interface of the user device 101 employs communication protocols / methods such as, without limitation, audio, analog, digital, monoaural, Radio Corporation of America (RCA) connector, stereo, IEEE®- 1394 high speed serial bus, serial bus, Universal Serial Bus (USB), infrared, Personal System / 2 (PS / 2) port, Bayonet Neill-Concelman (BNC) connector, coaxial, component, composite, Digital Visual Interface (DVI), High-Definition Multimedia Interface (HDMI®), Radio Frequency (RF) antennas, S-Video, Video Graphics Array (VGA), IEEE® 802.1 Ib / g / n / x, Bluetooth, cellular e.g., Code-Division Multiple Access (CDMA), High- Speed Packet Access (HSPA+), Global System for Mobile communications (GSM®), Long-Term Evolution (LTE®), Worldwide interoperability for Microwave access (WiMax®), or the like.

[0049]

[0049] For sake of explanation, only one database 113 is shown in the FIG. 1. However, in practice, there may be more than one database 113 (also, referred as one or more sources). The communication network 103 can be any of the following, but is not limited to, communication protocols / methods: a direct interconnection, an e-commerce network, a Peer-to-Peer (P2P) network, Local Area Network (LAN), Wide Area Network (WAN), wireless network (for example, using Wireless Application Protocol), Internet, Wi-Fi, Bluetooth and the like.

[0050]

[0050] The system 105 may be, but not limited to, a server, personal computers, laptops, minicomputers, mainframe computers, any non-transient and tangible machine that can execute a machine-readable code, cloud-based servers, distributed server networks, or a network of computer systems. In the embodiment, the system 105 includes an Input / Output (I / O) interface 107, a memory 109, and a processor 111. During real-time phase or testing phase, the I / O interface 107 receives at least one of an axial X-ray image and a sagittal X-ray image of a spine of a patient from one or more sources i.e., the user device 101. During training phase, the I / O interface 107 receives a plurality of historical axial X-ray images and corresponding historical sagittal X-ray images of a spine captured at different measurement angles of at least one patient from the one or more sources i.e., the database 113. For capturing an axial X-ray image, a C-arm of a medical imaging device is placed around a patient and the C-arm is turned 90 degrees so that X-ray beam of the medical imaging device is perpendicular to patient's long axis. For capturing a sagittal X- ray image, the C-arm of the medical imaging device is placed across the patient and the C-arm is turned 90 degrees so that the beam of the medical imaging device is perpendicular to patient's short axis. The X-ray images are captured at different measurement angles close to historical true axial and sagittal X-ray images of the spine. Here, the term “historical” refers to data from past (i. e. , history). The I / O interface 107 employs communication protocols / methods such as, without limitation, audio, analog, digital, monoaural, Radio Corporation of America (RCA) connector, stereo, IEEE®- 1394 high speed serial bus, serial bus, Universal Serial Bus (USB), infrared, Personal System / 2 (PS / 2) port, Bayonet Neill-Concelman (BNC) connector, coaxial, component, composite, Digital Visual Interface (DVI), High-Definition Multimedia Interface (HDMI®), Radio Frequency (RF) antennas, S-Video, Video Graphics Array (VGA), IEEE® 802.1 Ib / g / n / x, Bluetooth, cellular e.g., Code -Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global System for Mobile communications (GSM®), Long-Term Evolution (LTE®), Worldwide interoperability for Microwave access (WiMax®), or the like.

[0051]

[0051] The at least one of an axial X-ray image and a sagittal X-ray image, and the plurality of historical axial X-ray images and corresponding historical sagittal X-ray images received by the I / O interface 107 is stored in the memory 109. The memory 109 is communicatively coupled to the processor 111 of the system 105. The memory 109, also, stores processor-executable instructions which may cause the processor 111 to execute the instructions for reconstructing 3D anatomy. The memory 109 includes, without limitation, memory drives, removable disc drives, etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid- state drives, etc.

[0052]

[0052] The processor 111 includes at least one data processor for reconstructing 3D anatomy. The processor 111 may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.

[0053] The database 113 stores the plurality of historical axial X-ray images and corresponding historical sagittal X-ray images of a spine captured at different measurement angles of at least one patient. The database 113 is updated at pre-defined intervals of time. These updates relate to addition of at least one historical axial X-ray image and corresponding historical sagittal X-ray image. Here, the term “historical” refers to data from past (i.e., history). The database 113 may be located at a remote location or on a cloud computing platform.

[0053]

[0054] Hereinafter, the operation of the system 105 comprising two phases: (1) a training phase, and (2) a real-time or a testing phase is explained briefly.

[0054]

[0055] During the training phase, a CNN model is trained. The CNN model or CNN architecture is specially curated or tailored to handle the axial X-ray images and sagittal X-ray images of a spine including historical axial X-ray images and historical sagittal X-ray images. The CNN model (reference 225 shown in FIG. 2) of the system 105 receives a plurality of historical axial X-ray images and corresponding historical sagittal X-ray images of a spine captured at different measurement angles of at least one patient from the one or more sources i.e., the database 113 via the I / O interface. A measurement angle refers to an angle at which at least one of the axial X-ray image and the sagittal X-ray image of the spine of the patient is captured. Thereafter, the CNN model of the system 105 determines historical anatomical features and corresponding historical skeletal features of the spine for each measurement angle of the at least one patient using the plurality of historical axial X-ray images and corresponding historical sagittal X-ray images of the spine captured at different measurement angles of the at least one patient. Lastly, the CNN model of the system 105 reconstructs the at least one historical 3D spine anatomical model based on the historical anatomical features and the corresponding historical skeletal features of the spine of the at least one patient. The (reconstructed) at least one historical 3D spine anatomical model is then stored in the database 113.

[0055]

[0056] In an embodiment, as a part of validation process, the CNN model of the system 105 determines first historical anatomical features and corresponding first historical skeletal features of the spine for each measurement angle of the at least one patient using the plurality of historical axial X-ray images. Thereafter, the CNN model of the system 105 reconstructs the at least one first historical 3D spine anatomical model based on the first historical anatomical features and corresponding first historical skeletal features of the spine. The CNN model of the system 105 compares the (reconstructed) at least one first historical 3D spine anatomical model with the at least one historical 3D spine anatomical model. If the comparison results in an error value that is beyond a pre -defined threshold value, the CNN model of the system 105 optimizes or trains itself to minimize the error value. Analogously, in another embodiment, as a part of validation process, the CNN model of the system 105 determines second historical anatomical features and corresponding second historical skeletal features of the spine for each measurement angle of the at least one patient using the plurality of historical sagittal X-ray images. Thereafter, the CNN model of the system 105 reconstructs the at least one second historical 3D spine anatomical model based on the second historical anatomical features and corresponding second historical skeletal features of the spine. The CNN model of the system 105 compares the (reconstructed) at least one second historical 3D spine anatomical model with the at least one historical 3D spine anatomical model. If the comparison results in an error value that is beyond the pre-defined threshold value, the CNN model of the system 105 optimizes or trains itself to minimize the error value.

[0056]

[0057] During the real-time or the testing phase, the system 105 receives at least one of an axial X-ray image and a sagittal X-ray image of a spine of a patient from one or more sources i.e., the user device 101. Thereafter, the CNN model of the system 105 determines at least one measurement angle using at least one of the axial X-ray image and the sagittal X-ray image and at least one historical 3D spine anatomical model stored in the database 113. A measurement angle refers to an angle at which at least one of the axial X-ray image and the sagittal X-ray image of the spine of the patient is captured. Subsequently, the CNN model of the system 105 determines at least one of anatomical features and skeletal features of the spine using at least one of the axial X- ray image and the sagittal X-ray image of the spine. In detail, the CNN model of the system 105 compares the at least one of the axial X-ray image and the sagittal X-ray image with the at least one historical 3D spine anatomical model stored in the database 113 and determines the at least one measurement angle based on the comparison. Lastly, the CNN model of the system 105 reconstructs a patient specific 3D spine anatomical model based on the at least one measurement angle and at least one of the anatomical features and the skeletal features of the spine. In an embodiment, the system 105 may reconstruct or simulate the patient specific 3D spine anatomical models using an application such as Slicer. The patient specific 3D spine anatomical model refers to a 3D anatomical model that is specific in at least one of a spine size and a spine shape of the patient. The system 105 transmits via the I / O interface 107 the patient specific 3D spine anatomical model to the user device 101 for display.

[0057]

[0058] FIG. 2 shows a detailed block diagram of a system for reconstructing 3D anatomy in accordance with some embodiments of the present disclosure.

[0058]

[0059] The system 105, in addition to the I / O interface 107, and processor 111 described above, includes data 211 and one or more modules 221, which are described herein in detail. In the embodiment, the data 211 may be stored within the memory 109. The data 211 include, for example, image data 213, historic data 215, and miscellaneous data 217.

[0059]

[0060] The image data 213 includes at least one of an axial X-ray image and a sagittal X-ray image of a spine of a patient received from one or more sources.

[0060]

[0061] The historic data 215 includes a plurality of historical axial X-ray images and corresponding historical sagittal X-ray images of a spine captured at different measurement angles of at least one patient received from the one or more sources.

[0061]

[0062] The miscellaneous data 217 stores data, including temporary data and temporary files, generated by one or more modules 221 for performing the various functions of the system 105.

[0062]

[0063] In the embodiment, the data 211 in the memory 109 are processed by the one or more modules 221 present within the memory 109 of the system 105. In the embodiment, the one or more modules 221 are implemented as dedicated hardware units. As used herein, the term module refers to an Application Specific Integrated Circuit (ASIC), an electronic circuit, a Field- Programmable Gate Arrays (FPGA), Programmable System-on-Chip (PSoC), a combinational logic circuit, and / or other suitable components that provide the described functionality. In some implementations, the one or more modules 221 are communicatively coupled to the processor 111 for performing one or more functions of the system 105. The said modules 221 when configured with the functionality defined in the present disclosure results in a novel hardware.

[0063]

[0064] In one implementation, the one or more modules 221 include, but are not limited to, a transceiver 223, and a CNN model 225. The one or more modules 221, also, includes miscellaneous modules 227 to perform various miscellaneous functionalities of the system 105.

[0064]

[0065] Transceiver 223: During the real-time phase or the testing phase, the transceiver 223 receives at least one of an axial X-ray image and a sagittal X-ray image of a spine of a patient from one or more sources. The transceiver 223 transmits via the I / O interface 107 the patient specific 3D spine anatomical model to the user device 101 for display. During the training phase, the transceiver 223 receives a plurality of historical axial X-ray images and corresponding historical sagittal X-ray images of a spine captured at different measurement angles of at least one patient from the one or more sources. The transceiver 223 transmits via the I / O interface 107 the at least one historical 3D spine anatomical model to the database 113 for storage.

[0065]

[0066] CNN model 225: During the real-time phase or the testing phase, the CNN model 225 determines at least one measurement angle using at least one of the axial X-ray image and the sagittal X-ray image and at least one historical 3D spine anatomical model in the database 113. In detail, the CNN model 225 compares the at least one of the axial X-ray image and the sagittal X- ray image with the at least one historical 3D spine anatomical model in the database 113 and determines the at least one measurement angle based on the comparison. A measurement angle refers to an angle at which at least one of the axial X-ray image and the sagittal X-ray image of the spine of the patient is captured. The CNN model 225 determines at least one of anatomical features and skeletal features of the spine using at least one of the axial X-ray image and the sagittal X-ray image of the spine of the patient. The CNN model 225 reconstructs a patient specific 3D spine anatomical model based on the at least one measurement angle and at least one of the anatomical features and the skeletal features of the spine of the patient. The patient specific 3D spine anatomical model refers to a 3D anatomical model that is specific in at least one of a spine size and a spine shape of the patient.

[0067] During the training phase, the CNN model 225 receives a plurality of historical axial X- ray images and corresponding historical sagittal X-ray images of the spine captured at different measurement angles of at least one patient from the one or more sources. The CNN model 225 determines historical anatomical features and corresponding historical skeletal features of the spine for each measurement angle of the at least one patient using the plurality of historical axial X-ray images and corresponding historical sagittal X-ray images of the spine captured at different measurement angles of the at least one patient. The CNN model 225 reconstructs the at least one historical 3D spine anatomical model based on the historical anatomical features and the corresponding historical skeletal features of the spine of the at least one patient.

[0066]

[0068] FIGS. 3 A to 3B illustrate flowcharts showing a method for reconstructing 3D anatomy in accordance with some embodiments of the present disclosure.

[0067]

[0069] As illustrated in the FIGS. 3A to 3B, the methods 300a and 300b include one or more steps for reconstructing 3D anatomy. The methods 300a and 300b may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions or implement particular abstract data types.

[0068]

[0070] The order in which the methods 300a and 300b are described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.

[0069]

[0071] The below steps from 301 to 307 are performed by the system 105 during testing phase or real-time phase.

[0070]

[0072] At step 301, the transceiver 223 of the system 105 receives at least one of an axial X-ray image and a sagittal X-ray image of a spine of a patient from one or more sources.

[0073] At step 303, the CNN model 225 of the system 105 determines at least one measurement angle using at least one of the axial X-ray image and the sagittal X-ray image and at least one historical 3D spine anatomical model in the database 113. A measurement angle refers to an angle at which at least one of the axial X-ray image and the sagittal X-ray image of the spine of the patient is captured.

[0071]

[0074] At step 305, the CNN model 225 of the system 105 determines at least one of anatomical features and skeletal features of the spine using at least one of the axial X-ray image and the sagittal X-ray image of the spine of the patient.

[0072]

[0075] At step 307, the CNN model 225 of the system 105 reconstructs a patient specific 3D spine anatomical model based on the at least one measurement angle and at least one of the anatomical features and the skeletal features of the spine of the patients. The patient specific 3D spine anatomical model refers to a 3D anatomical model that is specific in at least one of a spine size and a spine shape of the patient.

[0073]

[0076] The below step from 309 to 313 are performed by the system during training phase of the CNN model.

[0074]

[0077] At step 309, the CNN model 225 of the system 105 receives a plurality of historical axial X-ray images and corresponding historical sagittal X-ray images of a spine captured at different measurement angles of at least one patient from the one or more sources.

[0075]

[0078] At step 311, the CNN model 225 of the system 105 determines historical anatomical features and corresponding historical skeletal features of the spine for each measurement angle of the at least one patient using the plurality of historical axial X-ray images and corresponding historical sagittal X-ray images of the spine captured at different measurement angles of the at least one patient.

[0079] At step 313, the CNN model 225 of the system 105 reconstructs the at least one historical 3D spine anatomical model based on the historical anatomical features and the corresponding historical skeletal features of the spine of the at least one patient.

[0076]

[0080] Some of the technical advantages of the present disclosure are listed below.

[0077]

[0081] The system and the method of the present disclosure allows reconstructing 3D anatomy without the need of the expensive conventional techniques (such as CT scanning) that require correspondence of overlapping scans or stereo views to be processed.

[0078]

[0082] As the system of the present disclosure facilitates comprehending anatomical and skeletal features from 2D viewpoints, the system therefore can be used in surgical scenarios. The system of the present disclosure further removes the dependencies of CT scanning at a facility to perform severe surgeries.

[0079]

[0083] As the system of the present disclosure provides ability to identify the 3D anatomy from 2D C-Arm images, the system may facilitate additional information on the structural geometry of the anatomy, which may result in advantages in terms of ease of operation and enhancement of efficacy in surgical scenarios.

[0080]

[0084] The system of the present disclosure provides the measurement of anatomical and skeletal features from 2D viewpoints (which is traditionally computed from a 3D CT scan by segmenting the slices) by way of 2D C-ARM images, which are projected to 3D anatomy using trained CNN models. Thus, this approach removes the dependency of overlapping images typically required while using CT scanning.

[0081]

[0085] FIG. 4 illustrates a block diagram of a computer system 400 for implementing embodiments consistent with the present disclosure.

[0082]

[0086] In an embodiment, the computer system 400 may be used to implement the system 105. The computer system 400 may include a central processing unit (“CPU” or “processor”) 402. The processor 402 may include at least one data processor for reconstructing 3D anatomy. The processor 402 may include specialized processing units such as, integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.

[0083]

[0087] The processor 402 may be disposed in communication with one or more input / output (I / O) devices via an I / O interface 401. The I / O interface 401 employ communication protocols / methods such as, without limitation, audio, analog, digital, monaural, Radio Corporation of America (RCA) connector, stereo, IEEE®-1394 high speed serial bus, serial bus, Universal Serial Bus (USB), infrared, Personal System / 2 (PS / 2) port, Bayonet Neill-Concelman (BNC) connector, coaxial, component, composite, Digital Visual Interface (DVI), High-Definition Multimedia Interface (HDMI®), Radio Frequency (RF) antennas, S-Video, Video Graphics Array (VGA), IEEE® 802.1 Ib / g / n / x, Bluetooth, cellular e.g., Code-Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global System for Mobile communications (GSM®), Long-Term Evolution (LTE®), Worldwide interoperability for Microwave access (WiMax®), or the like.

[0084]

[0088] Using the I / O interface 401, the computer system 400 may communicate with one or more I / O devices such as input devices 412 and output devices 413. For example, the input devices 412 may be an antenna, keyboard, mouse, joystick, (infrared) remote control, camera, card reader, fax machine, dongle, biometric reader, microphone, touch screen, touchpad, trackball, stylus, scanner, storage device, transceiver, video device / source, etc. The output devices 413 may be a printer, fax machine, video display (e.g., Cathode Ray Tube (CRT), Liquid Crystal Display (LCD), Light- Emitting Diode (LED), plasma, Plasma Display Panel (PDP), Organic Light-Emitting Diode display (OLED) or the like), audio speaker, etc.

[0085]

[0089] In some embodiments, the computer system 400 consists of the system 105. The processor 402 may be disposed in communication with the communication network 103 via a network interface 403. The network interface 403 may communicate with the communication network 103. The network interface 403 may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10 / 100 / 1000 Base T), Transmission Control Protocol / Internet Protocol (TCP / IP), token ring, IEEE® 802.11a / b / g / n / x, etc. The communication network 103 may include, without limitation, a direct interconnection, Local Area Network (LAN), Wide Area Network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, etc. Using the network interface 403 and the communication network 103, the computer system 400 may communicate with the user device 101 and the database 113. The network interface 403 may employ connection protocols include, but not limited to, direct connect, Ethernet (e.g., twisted pair 10 / 100 / 1000 Base T), Transmission Control Protocol / Internet Protocol (TCP / IP), token ring, IEEE® 802.11 a / b / g / n / x, etc.

[0086]

[0090] The communication network 103 includes, but is not limited to, a direct interconnection, a Peer to Peer (P2P) network, Local Area Network (LAN), Wide Area Network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, Wi-Fi, and such.

[0087]

[0091] In some embodiments, the processor 402 may be disposed in communication with a memory 405 (e.g., RAM, ROM, etc. not shown in FIG. 4) via a storage interface 404. The storage interface 404 may connect to memory 405 including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as, Serial Advanced Technology Attachment (SATA), Integrated Drive Electronics (IDE), IEEE®-1394, Universal Serial Bus (USB), fiber channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc.

[0088]

[0092] The memory 405 may store a collection of program or database components, including, without limitation, user interface 406, an operating system 407, etc. In some embodiments, computer system 400 may store user / application data, such as, the data, variables, records, etc., as described in this disclosure. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase.

[0089]

[0093] The operating system 407 may facilitate resource management and operation of the computer system 400. Examples of operating systems include, without limitation, APPLE® MACINTOSH® OS X®, UNIX®, UNIX-like system distributions (E.G., BERKELEY SOFTWARE DISTRIBUTION® (BSD), FREEBSD®, NETBSD®, OPENBSD, etc.), LINUX® DISTRIBUTIONS (E.G., RED HAT®, UBUNTU®, KUBUNTU®, etc.), IBM®OS / 2®, MICROSOFT® WINDOWS® (XP®, VISTA® / 7 / 8, 10 etc.), APPLE® IOS®, GOOGLE™ ANDROID™, BLACKBERRY® OS, or the like.

[0090]

[0094] In some embodiments, the computer system 400 may implement web browser 408 stored program components. Web browser 408 may be a hypertext viewing application, such as MICROSOFT® INTERNET EXPLORER®, GOOGLE™ CHROME™, MOZILLA® FIREFOX®, APPLE® SAFARI®, etc. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS), Secure Sockets Layer (SSL), Transport Layer Security (TLS), etc. Web browser 408 may utilize facilities such as AJAX, DHTML, ADOBE® FLASH®, JAVASCRIPT®, JAVA®, Application Programming Interfaces (APIs), etc. The computer system 400 may implement a mail server (not shown in FIG. 4) stored program component. The mail server may be an Internet mail server such as Microsoft Exchange, or the like. The mail server may utilize facilities such as ASP, ACTIVEX®, ANSI® C++ / C#, MICROSOFT®, .NET, CGI SCRIPTS, JAVA®, JAVASCRIPT®, PERL®, PHP, PYTHON®, WEBOBJECTS®, etc. The mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), MICROSOFT® exchange, Post Office Protocol (POP), Simple Mail Transfer Protocol (SMTP), or the like. The computer system 400 may implement a mail client (not shown in FIG. 4) stored program component. The mail client may be a mail viewing application, such as APPLE® MAIL, MICROSOFT® ENTOURAGE®, MICROSOFT® OUTLOOK®, MOZILLA® THUNDERBIRD®, etc.

[0091]

[0095] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer -readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer -readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non -transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, non-volatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.

[0096] The described operations may be implemented as a method, system or article of manufacture using standard programming and / or engineering techniques to produce software, firmware, hardware, or any combination thereof. The described operations may be implemented as code maintained in a “non-transitory computer readable medium”, where a processor may read and execute the code from the computer readable medium. The processor is at least one of a microprocessor and a processor capable of processing and executing the queries. A non-transitory computer readable medium may include media such as magnetic storage medium (e.g., hard disk drives, floppy disks, tape, and the like), optical storage (CD-ROMs, DVDs, optical disks, and the like), volatile and non-volatile memory devices (e.g., EEPROMs, ROMs, PROMs, RAMs, DRAMs, SRAMs, Flash Memory, firmware, programmable logic, and the like), and the like. Further, non-transitory computer-readable media include all computer-readable media except for a transitory. The code implementing the described operations may further be implemented in hardware logic (e.g., an integrated circuit chip, Programmable Gate Array (PGA), Application Specific Integrated Circuit (ASIC), and the like).

[0092]

[0097] Still further, the code implementing the described operations may be implemented in “transmission signals”, where transmission signals may propagate through space or through a transmission media, such as an optical fiber, copper wire, and the like. The transmission signals in which the code or logic is encoded may further comprise a wireless signal, satellite transmission, radio waves, infrared signals, Bluetooth, and the like. The transmission signals in which the code or logic is encoded is capable of being transmitted by a transmitting station and received by a receiving station, where the code or logic encoded in the transmission signal may be decoded and stored in hardware or a non-transitory computer readable medium at the receiving and transmitting stations or devices. An “article of manufacture” comprises non-transitory computer readable medium, hardware logic, and / or transmission signals in which code may be implemented. A device in which the code implementing the described embodiments of operations is encoded may comprise a computer readable medium or hardware logic. Of course, those skilled in the art will recognize that many modifications may be made to this configuration without departing from the scope of the invention, and that the article of manufacture may comprise suitable information bearing medium known in the art.

[0098] The terms “an embodiment”, “embodiment”, “embodiments”, “the embodiment”, “the embodiments”, “one or more embodiments”, “some embodiments”, and “one embodiment” mean “one or more (but not all) embodiments of the invention(s)” unless expressly specified otherwise.

[0093]

[0099] The terms “including”, “comprising”, “having” and variations thereof mean “including but not limited to”, unless expressly specified otherwise.

[0094]

[0100] The enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise.

[0095]

[0101] The terms “a”, “an” and “the” mean “one or more”, unless expressly specified otherwise.

[0096]

[0102] A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention.

[0097]

[0103] When a single device or article is described herein, it will be readily apparent that more than one device / article (whether or not they cooperate) may be used in place of a single device / article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be readily apparent that a single device / article may be used in place of the more than one device or article, or a different number of devices / articles may be used instead of the shown number of devices or programs. The functionality and / or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality / features. Thus, other embodiments of the invention need not include the device itself.

[0098]

[0104] Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

[0099]

[0105] While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the scope being indicated by the following claims.

[0100]

[0106] REFERRAL NUMERALS:

Claims

We claim:

1. A method for reconstructing 3-Dimensional (3D) anatomy, the method comprising: receiving at least one of an axial X-ray image and a sagittal X-ray image of a spine of a patient from one or more sources; determining at least one measurement angle using at least one of the axial X-ray image and the sagittal X-ray image and at least one historical 3D spine anatomical model in a database; determining at least one of anatomical features and skeletal features of the spine using at least one of the axial X-ray image and the sagittal X-ray image of the spine; and reconstructing a patient specific 3D spine anatomical model based on the at least one measurement angle and at least one of the anatomical features and the skeletal features of the spine using a trained Convolutional Neural Network (CNN) model.

2. The method as claimed in claim 1 , wherein determining the at least one measurement angle using at least one of the axial X-ray image and the sagittal X-ray image and the at least one historical 3D spine anatomical model in the database comprises: comparing the at least one of the axial X-ray image and the sagittal X-ray image with the at least one historical 3D spine anatomical model in the database; and determining the at least one measurement angle based on the comparison.

3. The method as claimed in claim 1, wherein the patient specific 3D spine anatomical model refers to a 3D anatomical model that is specific in at least one of a spine size and a spine shape of the patient.

4. The method as claimed in claim 1, wherein a measurement angle refers to an angle at which at least one of the axial X-ray image and the sagittal X-ray image of the spine of the patient is captured.

5. The method as claimed in claim 1, wherein a CNN model is trained for:receiving a plurality of historical axial X-ray images and corresponding historical sagittal X-ray images of a spine captured at different measurement angles of at least one patient from the one or more sources; determining historical anatomical features and corresponding historical skeletal features of the spine for each measurement angle of the at least one patient using the plurality of historical axial X-ray images and corresponding historical sagittal X-ray images of the spine captured at different measurement angles of the at least one patient; and reconstructing the at least one historical 3D spine anatomical model based on the historical anatomical features and the corresponding historical skeletal features of the spine of the at least one patient.

6. A system for reconstructing 3-Dimensional (3D) anatomy, the system comprising: a processor; and a memory communicatively coupled to the processor, wherein the processor is configured to: receive at least one of an axial X-ray image and a sagittal X-ray image of a spine of a patient from one or more sources; determine at least one measurement angle using at least one of the axial X-ray image and the sagittal X-ray image and at least one historical 3D spine anatomical model in a database; determine at least one of anatomical features and skeletal features of the spine using at least one of the axial X-ray image and the sagittal X-ray image of the spine; and reconstruct a patient specific 3D spine anatomical model based on the at least one measurement angle, and at least one of the anatomical features and the skeletal features of the spine using a trained Convolutional Neural Network (CNN) model.

7. The system as claimed in claim 6, wherein the processor is configured to: compare the at least one of the axial X-ray image and the sagittal X-ray image with the at least one historical 3D spine anatomical model in the database; and determine the at least one measurement angle based on the comparison.

8. The system as claimed in claim 6, wherein the patient specific 3D spine anatomical model refers to a 3D anatomical model that is specific in at least one of a spine size and a spine shape of the patient.

9. The system as claimed in claim 6, wherein a measurement angle refers to an angle at which at least one of the axial X-ray image and the sagittal X-ray image of the spine of the patient is captured.

10. The system as claimed in claim 6, wherein the processor is configured to train a CNN model to: receive a plurality of historical axial X-ray images and corresponding historical sagittal X- ray images of a spine captured at different measurement angles of at least one patient from the one or more sources; determine historical anatomical features and corresponding historical skeletal features of the spine for each measurement angle of the at least one patient using the plurality of historical axial X-ray images and corresponding historical sagittal X-ray images of the spine captured at different measurement angles of the at least one patient; and reconstruct the at least one historical 3D spine anatomical model based on the historical anatomical features and the corresponding historical skeletal features of the spine of the at least one patient.

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