Human body positioning method for x-ray transmission examination

Abstract

A human body positioning method for X-ray transmission examination. The method comprises: acquiring a visible light image of a patient on a hospital bed, and a preoperative medical image of the patient obtained by using a fluoroscopic examination device (S101); on the basis of the visible light image, obtaining a corresponding parameterized patient model, and on the basis of the parameterized patient model, performing fitting to obtain a standard skeletal model (S102); performing segmentation and reconstruction on the preoperative medical image, so as to obtain a personalized skeletal model (S103); and on the basis of a target bony landmark, fusing the personalized skeletal model and the standard skeletal model to obtain a target fused image, so as to position a target organ of the patient (S104). By using the method in the present disclosure, a target organ can be quickly positioned preoperatively on the basis of a target bony landmark.

Description

A human positioning method for X-ray transmission examination TECHNICAL FIELD

[0001] The present disclosure relates to the medical and engineering cross field, and particularly relates to a human positioning method for X-ray transmission examination. BACKGROUND

[0002] In surgery, the commonly used ways of X-ray transmission examination include: 1) fluoroscopy X-ray imaging: during surgery, a fluoroscopy X-ray device is used to guide the operation of the doctor through real-time imaging by penetrating X-rays. This way is usually used for orthopedic surgery and internal fixation placement. 2) Digital subtraction angiography (DSA): in interventional surgery, the doctor can monitor the situation inside the blood vessel in real time by introducing contrast agent into the patient's blood vessel combined with X-ray imaging technology, which is used to guide the interventional surgery. These ways can provide real-time X-ray transmission imaging information during surgery to help the doctor accurately position and operate, which has an important auxiliary role for some surgeries that require accurate positioning and operation.

[0003] Fluoroscopy X-ray devices (also known as X-ray fluoroscopy devices) are commonly used for real-time dynamic imaging in surgery, mainly including C-arm, O-arm and G-arm. The C-arm X-ray device is usually composed of a fixed articulated arm and a movable X-ray detector, which is shaped like the letter "C". This device is widely used in orthopedic surgery, trauma surgery, cardiac catheterization and other surgical and therapeutic procedures to provide real-time X-ray imaging. The O-arm X-ray device is an X-ray device that rotates around the patient, which is usually applied to imaging and guided surgery in the operating room. The O-arm device can provide high-resolution 3D imaging, which helps the doctor to accurately position and plan the surgery when performing complex surgery. The G-arm X-ray device is similar to the C-arm, but has slight differences in structure, which is also usually used for X-ray imaging and guided surgery in the operating room. It can provide multi-angle X-ray imaging to help the doctor observe and guide in real time during the surgery.

[0004] X-ray transmission examination expects to obtain prior information about the position and shape of the organ to be examined. Since X-ray transmission examination has radiation damage problem, the maximum number of scans that a patient can safely accept within a certain period of time is limited. Due to this limitation, the doctor must carefully plan the imaging scheme before surgery, and any solution that helps quickly and accurately locate the target organ without excessive beam imaging will greatly help the clinician and reduce the risk of radiation damage to the patient. For example, in radiotherapy (e.g., a cancer treatment method using high-dose radiation to kill cancer cells and shrink tumors), it is common practice to use patient-specific molds (usually made of plastic or plaster) to keep the patient in exactly the same position during preoperative organ scanning and subsequent treatment. This customized mold helps accurately aim the target organ area without the need to rescan the patient and reposition the tumor before each treatment. However, this method limits the clinician's operating space and has considerable time and money costs. The prior art also proposes an in-vivo organ deformation model based on different postures of the subject, which can extract the organ shape representation of a specific patient and predict its deformed shape according to different posture parameters. However, this method uses FEM (Finite element method) to simulate posture-related organ deformation, and the accuracy and generalization ability for different organs need to be verified.

[0005] SUMMARY

[0006] The present disclosure proposes a human positioning method, system, electronic device and computer readable storage medium for X-ray transmission examination to quickly locate the target organ according to the target bone landmark point before surgery.

[0007] To achieve the above object, the first aspect of the present disclosure proposes a human positioning method for X-ray transmission examination, comprising:

[0008] Obtaining a visible light image of a patient on a hospital bed and a preoperative medical image of the patient obtained by a fluoroscopy device;

[0009] Obtaining a corresponding patient parameterized model based on the visible light image, and fitting a standard bone model based on the patient parameterized model;

[0010] Segmenting and reconstructing the preoperative medical image to obtain a personalized bone model;

[0011] Based on the target bone landmark point, the personalized bone model is fused with the standard bone model to obtain a target fusion image, so as to realize the positioning of the target organ of the patient.

[0012] In the method of the first aspect of the present disclosure, fitting the standard bone model based on the patient parameterized model comprises: generating bone-wrapped surface data, and obtaining skin surface data based on the patient parameterized model; fitting the standard bone model based on the bone-wrapped surface data and the skin surface data.

[0013] In the method of the first aspect of the present disclosure, the standard bone model is fitted based on the bone-wrapped surface data and the skin surface data using a nonlinear least squares method.

[0014] In the method of the first aspect of the present disclosure, fitting the standard bone model based on the bone-wrapped surface data and the skin surface data using a nonlinear least squares method comprises: setting a deformation surface parameter, obtaining an energy based on the deformation surface parameter, the bone-wrapped surface data and the skin surface data, and minimizing the energy using a nonlinear least squares method, thereby obtaining the standard bone model.

[0015] In the method of the first aspect of the present disclosure, fusing the personalized bone model and the standard bone model based on the target bone landmark to obtain a target fusion image comprises: selecting a first target bone landmark set of the personalized bone model in a preset order and a preset number; selecting a second target bone landmark set of the standard bone model in a preset order and a preset number; and fusing the first target bone landmark set and the second target bone landmark set in a corresponding order to obtain the target fusion image.

[0016] In the method of the first aspect of the present disclosure, fusing the first target bone landmark set and the second target bone landmark set in a corresponding order to obtain the target fusion image comprises: one-to-one corresponding each first target bone landmark in the first target bone landmark set and the second target bone landmark set in a corresponding order; obtaining a spatial transformation combination, adjusting the personalized bone model based on the spatial transformation combination, so that the adjusted personalized bone model is consistent with the coordinate system of the standard bone model, thereby obtaining the target fusion image.

[0017] To achieve the above object, the second aspect of the present disclosure provides a human positioning system for X-ray transmission examination, comprising:

[0018] An image acquisition module is configured to acquire a visible light image of a patient on a hospital bed and preoperative medical images of the patient obtained by a fluoroscopy device.

[0019] A bone modeling module is configured to obtain a corresponding patient parameterized model based on the visible light image, and fit a standard bone model based on the patient parameterized model.

[0020] a reconstruction module configured to perform segmentation reconstruction on the preoperative medical image to obtain a personalized bone model;

[0021] a fusion module configured to fuse the personalized bone model and the standard bone model based on target bone landmarks to obtain a target fusion image, so as to realize target organ positioning of the patient.

[0022] In the system of the second aspect of the present disclosure, the bone modeling module, when used for fitting a standard bone model based on the patient parameterized model, is specifically configured to: generate bone wrapping surface data, and obtain skin surface data based on the patient parameterized model; and fit a standard bone model based on the bone wrapping surface data and the skin surface data.

[0023] To achieve the above object, the third aspect of the present disclosure provides an electronic device, comprising: a processor, and a memory connected with the processor in communication; the memory stores computer execution instructions; and the processor executes the computer execution instructions stored in the memory to implement the method provided in the first aspect of the present disclosure.

[0024] To achieve the above object, the fourth aspect of the present disclosure provides a computer readable storage medium, wherein the computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by a processor to implement the method provided in the first aspect of the present disclosure.

[0025] The human positioning method and system for X-ray transmission examination, the electronic device and the storage medium provided by the present disclosure obtain a visible light image of a patient on a hospital bed and a preoperative medical image of the patient obtained by a fluoroscopy device; obtain a corresponding patient parameterized model based on the visible light image, and fit a standard bone model based on the patient parameterized model; perform segmentation reconstruction on the preoperative medical image to obtain a personalized bone model; and fuse the personalized bone model and the standard bone model based on target bone landmarks to obtain a target fusion image, so as to realize target organ positioning of the patient. In this case, the standard bone model obtained by using the visible light image and the personalized bone model obtained by using the preoperative medical image are combined, the personalized bone model and the standard bone model are fused based on target bone landmarks to obtain a target fusion image, and the position and shape of the target organ in the posture of the patient corresponding to the visible light image can be obtained based on the bone position in the target fusion image. Thus, the target organ is quickly positioned according to the target bone landmarks before surgery, so as to better assist doctors in formulating imaging schemes before surgery, and reduce the scanning radiation dose during intraoperative X-ray transmission examination.

[0026] It is to be understood that the details set forth herein do not limit the scope of the embodiments of the present disclosure to the preferred embodiments described. Rather, the scope of the present disclosure is defined by the appended claims. BRIEF DESCRIPTION OF DRAWINGS

[0027] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, including the appended drawings.

[0028] Fig. 1 is a flowchart of a method for positioning a human body in X-ray transmission examination according to an embodiment of the present disclosure;

[0029] Fig. 2 is a schematic diagram of a shooting scene according to an embodiment of the present disclosure;

[0030] Fig. 3 is a schematic diagram of a visible light image and a SMPL patient three-dimensional model according to an embodiment of the present disclosure;

[0031] Fig. 4 is a schematic diagram of a bone landmark point and a body surface three-dimensional model according to an embodiment of the present disclosure;

[0032] Fig. 5 is a schematic diagram of fitting a standard bone model according to a SMPL patient three-dimensional model according to an embodiment of the present disclosure;

[0033] Fig. 6 is a schematic diagram of a preoperative medical image and a segmentation reconstruction result according to an embodiment of the present disclosure;

[0034] Fig. 7 is a schematic diagram of selection of a bone landmark point according to an embodiment of the present disclosure;

[0035] Fig. 8 is a schematic diagram of the effect of fusion of a reconstructed spine model and a standard bone model according to an embodiment of the present disclosure;

[0036] Fig. 9 is a block diagram of a system for positioning a human body in X-ray transmission examination according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0037] Embodiments of the present disclosure are described in detail below with reference to the accompanying drawings, examples of which are illustrated in the drawings, wherein the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by reference to the drawings are exemplary and are intended to explain the present disclosure, and cannot be understood as limiting the present disclosure.

[0038] The method and system for positioning a human body in X-ray transmission examination according to an embodiment of the present disclosure are described below with reference to the accompanying drawings.

[0039] The method for positioning a human body in X-ray transmission examination according to an embodiment of the present disclosure is provided to quickly position a target organ according to a target bone landmark point before surgery.

[0040] FIG. 1 is a flowchart of a human positioning method for X-ray transmission examination according to an embodiment of the present disclosure.

[0041] As shown in FIG. 1, the human positioning method for X-ray transmission examination includes the following steps:

[0042] In step S101, an optical image of a patient on a patient bed and a preoperative medical image of the patient obtained by a perspective examination device are acquired.

[0043] In step S101, the optical image can be obtained by a visible light camera arranged on the X-ray perspective examination device. Specifically, the visible light camera is installed near the detector plane of the X-ray perspective examination device, and the optical image of the patient on the patient bed is captured by the visible light camera.

[0044] In step S101, the preoperative medical image can be obtained by scanning the perspective examination device. The scanning methods of the preoperative medical image mainly include computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET-CT scanning), ultrasonic imaging, etc.

[0045] FIG. 2 is a schematic diagram of a shooting scene according to an embodiment of the present disclosure. As shown in FIG. 2, the perspective examination device is an X-ray perspective examination device. The X-ray perspective examination device obtains a preoperative medical image of a patient on a patient bed through an X-ray source and a detector. A visible light camera is installed near the detector plane of the X-ray perspective examination device, and an optical image of the patient on the patient bed is captured by the visible light camera.

[0046] In step S102, a corresponding patient parameterized model is obtained based on the optical image, and a standard skeleton model is fitted based on the patient parameterized model.

[0047] As can be easily understood, there are various parameterized representations of three-dimensional human modeling. SMPL (Skinned Multi-Person Linear) is a parameterized 3D human model that efficiently describes and generates three-dimensional human morphologies with a small number of parameters. SMPL can convert input pose parameters and shape parameters into the pose and shape of a 3D human model. Specifically, SMPL is defined as a parameterized human model M(β, θ) that maps a shape parameter vector β and a pose parameter vector θ to the vertices of a human body, where β is used to control the shape of the human body and θ is used to control the pose of the human body. SMPL-H is a variant of the SMPL model that adds details of the hand skeleton to the SMPL, making the generated human model more realistic. SMPL-X is an extended version of the SMPL model that is suitable for people of various body types, muscle mass, and body shape characteristics, and can simulate more realistic human shapes and movements. STAR (Sparse Trained Articulated Human Body Regressor) is a framework for body pose reconstruction and animation generation that combines the techniques of SMPL and BlendSCAPE, which converts pose-dependent deformation factors into a set of sparse spatial local pose correction blending shape functions, where each joint only affects a sparse subset of mesh vertices, enabling the reconstruction of 3D human poses from a single image or video and animation generation and editing.

[0048] In the embodiments of the present disclosure, the SMPL model is used when modeling the patient in three dimensions, and the patient parameterized model obtained based on the visible light image in step S102 is an SMPL patient three-dimensional model.

[0049] The specific steps of obtaining the corresponding SMPL patient three-dimensional model based on the visible light image include: (1) pose estimation: estimating the joint positions and poses of the patient in the visible light image through computer vision and deep learning techniques to obtain joint position and pose estimation results; (2) shape modeling: estimating the three-dimensional shape of the patient using the joint position and pose estimation results and using existing human shape modeling methods to obtain the shape parameter vector β and the pose parameter vector θ of the patient; (3) alignment and matching: obtaining a learned SMPL, inputting the obtained shape parameter vector β and pose parameter vector θ of the patient as input data into the learned SMPL to output the vertices of the patient, and all the vertices constitute the SMPL patient three-dimensional model, thereby realizing the alignment and matching of the estimated three-dimensional shape of the patient and the SMPL model.

[0050] FIG. 3 is a visible light image and a SMPL patient three-dimensional model according to an embodiment of the present disclosure. In FIG. 3, (a) is a visible light image of a patient, and (b) and (c) are front and side views of a SMPL patient three-dimensional model obtained by using the method of the present disclosure.

[0051] In step S102, the standard bone model is fitted based on the patient parameterized model, including: generating bone-wrapped surface data, and obtaining skin surface data based on the patient parameterized model; fitting the standard bone model based on the bone-wrapped surface data and the skin surface data. The standard bone model is fitted based on the bone-wrapped surface data and the skin surface data using a nonlinear least squares method.

[0052] The standard bone model is fitted based on the bone-wrapped surface data and the skin surface data using a nonlinear least squares method, including: setting a deformed surface parameter, obtaining an energy based on the deformed surface parameter, the bone-wrapped surface data, and the skin surface data, and minimizing the energy using a nonlinear least squares method, thereby obtaining the standard bone model.

[0053] Specifically, taking the SMPL patient three-dimensional model as an example, the process of fitting the standard bone model based on the SMPL patient three-dimensional model is as follows:

[0054] FIG. 4 is a bone landmark and a body surface three-dimensional model according to an embodiment of the present disclosure; and FIG. 5 is a schematic diagram of fitting a standard bone model according to a SMPL patient three-dimensional model according to an embodiment of the present disclosure.

[0055] The generated SMPL patient three-dimensional model and the camera parameters corresponding to the visible light camera are used to project the three-dimensional pose onto a two-dimensional image plane. In the projected two-dimensional image, some specific joint points or bone landmarks (referred to as bone landmarks) are extracted, such as head, shoulder, elbow, wrist, etc. two-dimensional bone landmarks, as shown in FIG. 4(a) located at the head, shoulder, elbow, wrist, hip, knee, ankle, etc. These bone landmarks can be connected to form the initial spatial configuration of the standard bone model. For the initial spatial configuration of the bone, a watertight Genus-0 surface is first generated to wrap the initial spatial configuration of the bone, referred to as bone-wrapped surface W (i.e., bone-wrapped surface data). The bone-wrapped surface is a smooth, water-tight two-dimensional manifold surface, which does not include the internal rib area, and does not include the small hole between the pelvis or ulna and radius.

[0056] Based on the generated SMPL patient three-dimensional model, a body surface three-dimensional model (such as FIG. 4(b)) is obtained, which is the skin surface data, also referred to as skin surface M. The standard bone model B is generated based on the skin surface M, and the initial vertex configuration of the deformed surface X (i.e., the deformed surface parameter) is first set Then the energy, which consists of a fitting term and a regularization term, is minimized using a non-linear least squares method. The fitting term is used to attract the deformed surface X to the skeleton-wrapped surface W; the regularization term is used to prevent the deformed surface X from deforming in physically unreasonable ways from its initial vertex configuration in physically unreasonable ways:

[0057] where B is the standard skeleton model (also referred to as energy), ω fit is the fitting term weight, ω reg is the regularization term weight.

[0058] The regularization is formulated as a discrete bending energy, which penalizes changes in mean curvature:

[0059] where x i and x represent the vertices of the deformed surface X and the initial vertex configuration R i ∈ SO(3) represents the best rotation matrix that aligns the vertices Laplacian Δx i and x A i represents the Voronoi area.

[0060] The fitting term penalizes the squared distance of the vertices x i ∈ X to the target positions t i ∈ W:

[0061] where the target positions t i are points on the skeleton-wrapped surface W, and there are three types: nearest point correspondence, fixed correspondence, or collision target. The weight ω i is determined by the type of the target position t i (nearest point correspondence is 0.1, fixed correspondence is 1, and collision target is 100). After minimizing equation (1), the final standard skeleton model B is obtained. The standard skeleton model is a three-dimensional skeleton model. As shown in (a) of FIG. 5, it is a three-dimensional model of a patient with a skeleton landmark point, and (b) of FIG. 5 shows the obtained standard skeleton model B.

[0062] In step S103, the preoperative medical image is segmented and reconstructed to obtain a personalized skeleton model.

[0063] In step S103, the preoperative medical image can be directed to the human spine, so that the personalized skeleton model obtained is a reconstructed spine model. It should be noted that the preoperative medical image can also be directed to different parts such as arms, heads, and pelvic bones as needed, so as to obtain a reconstructed model of the corresponding part of the skeleton.

[0064] In the embodiments of the present disclosure, taking the CT scan image of the human spine selected from the preoperative medical image as an example, FIG. 6 is a schematic diagram of the preoperative medical image and the segmentation reconstruction result provided by the embodiments of the present disclosure. As shown in (a) of FIG. 6, the CT scan image of the human spine taken preoperatively, the CT scan image is segmented and reconstructed to obtain the reconstructed spine model shown in (b) of FIG. 6, the reconstructed spine model includes lumbar vertebrae, thoracic vertebrae and cervical vertebrae and the like.

[0065] In step S104, the personalized bone model is fused with the standard bone model based on the target bone landmark points to obtain a target fusion image, so as to realize the positioning of the target organ of the patient.

[0066] In step S104, the personalized bone model is fused with the standard bone model based on the target bone landmark points to obtain a target fusion image, including: selecting a first target bone landmark point set of the personalized bone model in a preset order and a preset number; selecting a second target bone landmark point set of the standard bone model in a preset order and a preset number; and fusing the first target bone landmark point set and the second target bone landmark point set in a corresponding order to obtain the target fusion image. Wherein, the first target bone landmark point set and the second target bone landmark point set are fused in a corresponding order to obtain the target fusion image, including: one-to-one corresponding each first target bone landmark point in the first target bone landmark point set and the second target bone landmark point set in a corresponding order; obtaining a spatial transformation combination, adjusting the personalized bone model based on the spatial transformation combination, so that the adjusted personalized bone model is consistent with the coordinate system of the standard bone model, thereby obtaining the target fusion image.

[0067] In step S104, taking the reconstructed spine model as an example, the target bone landmark points are selected from the positions of the lumbar vertebrae, the thoracic vertebrae and the cervical vertebrae. The preset number is 5 for example, and the preset order is the first cervical vertebra, the seventh cervical vertebra, the seventh thoracic vertebra, the fourth lumbar vertebra and the fifth lumbar vertebra for example.

[0068] FIG. 7 is a diagram illustrating selection of bone landmarks according to an embodiment of the present disclosure. As shown in FIG. 7, the reconstructed spine model includes lumbar vertebrae, thoracic vertebrae, cervical vertebrae, and sacral vertebrae, etc. The cervical vertebrae include the first cervical vertebra C1, the second cervical vertebra C2, the third cervical vertebra C3, …, and the seventh cervical vertebra C7. The thoracic vertebrae include the first thoracic vertebra T1, the second thoracic vertebra T2, …, the seventh thoracic vertebra T7, …, the eleventh thoracic vertebra T11, and the twelfth thoracic vertebra T12. The lumbar vertebrae include the first lumbar vertebra L1, the second lumbar vertebra L2, …, the fourth lumbar vertebra L4, and the fifth lumbar vertebra L5. The sacral vertebrae include the first sacral vertebra S1, etc. The center point m1 of the first cervical vertebra C1, the center point m2 of the seventh cervical vertebra C7, the center point m3 of the seventh thoracic vertebra T7, the center point m4 of the fourth lumbar vertebra L4, and the center point m5 of the fifth lumbar vertebra L5 are selected as five first target bone landmarks in the reconstructed spine model, obtaining a first target bone landmark set, denoted as LM1 = {m1, m2, m3, m4, m5}. These first target bone landmarks are selected mainly because of their anatomical particularity and recognizability. For example, the seventh cervical vertebra C7 is the lowest cervical vertebra, which is characterized by a prominent spinous process, and is referred to as a prominent vertebra.

[0069] For the standard bone model, a second target bone landmark set is obtained in a preset order and a preset number, denoted as LM2 = {n1, n2, n3, n4, n5}, where n1, n2, n3, n4, and n5 are the center points of the first cervical vertebra C'1, the seventh cervical vertebra C'7, the seventh thoracic vertebra T'7, the fourth lumbar vertebra L'4, and the fifth lumbar vertebra L'5 in the standard bone model, respectively.

[0070] The target bone landmarks in the two sets of target bone landmark sets are in a one-to-one correspondence: i = 1, 2, 3, 4, 5, m i ∈ LM1, n i ∈ LM2, then n i = lRm i + t + ε i , i = 1, 2, 3, 4, 5. Wherein l > 0 is a scaling factor; R ∈ SO(3) is a three-dimensional rotation matrix; is a three-dimensional translation vector; The unknown additive noise is modeled, assuming that the noise is subject to a zero-mean isotropic Gaussian distribution with a standard deviation σ i . Under the condition of maximum likelihood estimation, the scaling factor l * , the rotation matrix R * , and the translation vector t * are optimized and solved:

[0071] In the formula, l * , R* t * The spatial transformation combination is used to adjust the coordinate system of the reconstructed spine model, so that the adjusted reconstructed spine model is consistent with the coordinate system of the standard bone model.

[0072] FIG. 8 is a schematic diagram of the effect of the fusion of the reconstructed spine model and the standard bone model according to an embodiment of the present disclosure. As shown in (a) of FIG. 8, the fusion image obtained by fusing the reconstructed spine model and the standard bone model. As shown in (b) and (c) of FIG. 8, the side view and front view of the fusion image of the SMPL patient three-dimensional model corresponding to the reconstructed spine model and the standard bone model.

[0073] In step S104, the target fusion image can include a first fusion image obtained by fusing the personalized bone model and the standard bone model, and the target fusion image can also include a second fusion image of a patient parameterized model corresponding to the personalized bone model and the standard bone model, a third fusion image of a preoperative medical image corresponding to the personalized bone model and the standard bone model, etc.

[0074] In step S104, since the relative positions of the bones and the target organs in the personalized bone model are fixed, after fusing the personalized bone model and the standard bone model, the relative positions of the bones and the target organs are mapped to the standard bone model, so that the target fusion image can include the position and shape information of the target organs in the patient's posture in the visible light image. Therefore, based on the target fusion image, the position and shape of the human body, especially the target organs, can be quickly positioned during the X-ray transmission examination process. Assist doctors to develop imaging schemes before surgery and reduce the scanning radiation dose.

[0075] To achieve the above-mentioned embodiments, the present disclosure further provides a human body positioning system for X-ray transmission examination.

[0076] FIG. 9 is a block diagram of a human body positioning system for X-ray transmission examination according to an embodiment of the present disclosure.

[0077] As shown in FIG. 9, the human body positioning system for X-ray transmission examination includes an image acquisition module, a bone modeling module, a reconstruction module, and a fusion module, wherein:

[0078] The image acquisition module is configured to acquire a visible light image of a patient on a hospital bed and a preoperative medical image of the patient obtained by a fluoroscopy device;

[0079] The bone modeling module is configured to obtain a corresponding patient parameterized model based on the visible light image, and fit a standard bone model based on the patient parameterized model;

[0080] a reconstruction module, configured to perform segmentation reconstruction on the preoperative medical image to obtain the personalized bone model;

[0081] a fusion module, configured to fuse the personalized bone model and the standard bone model based on the target bone landmark points to obtain a target fusion image, so as to realize target organ positioning of the patient.

[0082] Further, in a possible implementation manner of the embodiment of the present disclosure, in the bone modeling module, the standard bone model is fitted based on the patient parameterized model, including: generating bone wrapping surface data, and obtaining skin surface data based on the patient parameterized model; fitting the standard bone model based on the bone wrapping surface data and the skin surface data.

[0083] Further, in a possible implementation manner of the embodiment of the present disclosure, in the bone modeling module, the standard bone model is fitted based on the bone wrapping surface data and the skin surface data using a nonlinear least square method.

[0084] Further, in a possible implementation manner of the embodiment of the present disclosure, in the bone modeling module, the standard bone model is fitted based on the bone wrapping surface data and the skin surface data using a nonlinear least square method, including: setting a deformation surface parameter, obtaining energy based on the deformation surface parameter, the bone wrapping surface data and the skin surface data, and minimizing the energy using the nonlinear least square method, so as to obtain the standard bone model.

[0085] Further, in a possible implementation manner of the embodiment of the present disclosure, in the fusion module, the target bone landmark points are selected from lumbar vertebrae, thoracic vertebrae and cervical vertebrae positions.

[0086] Further, in a possible implementation manner of the embodiment of the present disclosure, the fusion module is specifically configured to: select a first target bone landmark point set of the personalized bone model in a preset order and a preset number; select a second target bone landmark point set of the standard bone model in the preset order and the preset number; and fuse the first target bone landmark point set and the second target bone landmark point set in a corresponding order to obtain the target fusion image.

[0087] Further, in a possible implementation manner of the embodiment of the present disclosure, in the fusion module, the first target bone landmark point set and the second target bone landmark point set are fused in the corresponding order to obtain the target fusion image, including: one-to-one corresponding each first target bone landmark point in the first target bone landmark point set and the second target bone landmark point set in the corresponding order; obtaining a spatial transformation combination, adjusting the personalized bone model based on the spatial transformation combination, so that the adjusted personalized bone model is consistent with a coordinate system of the standard bone model, thereby obtaining the target fusion image.

[0088] It should be noted that the aforementioned explanation of the embodiment of the human positioning method for X-ray transmission examination is also applicable to the embodiment of the human positioning system for X-ray transmission examination, and will not be repeated here.

[0089] In the embodiment of the present disclosure, a visible light image of a patient on a sickbed and a preoperative medical image of the patient obtained by using a fluoroscopy device are acquired; a corresponding patient parameterized model is obtained based on the visible light image, and a standard bone model is fitted based on the patient parameterized model; an individualized bone model is obtained by segmenting and reconstructing the preoperative medical image; and the individualized bone model and the standard bone model are fused based on target bone landmark points to obtain a target fusion image, so as to realize positioning of a target organ of the patient. In this case, the standard bone model obtained by using the visible light image and the individualized bone model obtained by using the preoperative medical image are combined, the individualized bone model and the standard bone model are fused based on the target bone landmark points to obtain the target fusion image, and the position and shape of the target organ under the posture of the patient corresponding to the visible light image can be obtained based on the bone position in the target fusion image. Therefore, the target organ is quickly positioned according to the target bone landmark points before surgery, so as to better assist the doctor in formulating an imaging scheme before surgery, and the scanning radiation dose during subsequent intraoperative X-ray transmission examination is reduced.

[0090] In order to realize the above-mentioned embodiments, the present disclosure further proposes an electronic device, comprising: a processor, and a memory connected with the processor in communication; the memory stores computer execution instructions; and the processor executes the computer execution instructions stored in the memory to realize the method provided by the foregoing embodiments.

[0091] In order to realize the above-mentioned embodiments, the present disclosure further proposes a computer readable storage medium, wherein the computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by a processor to realize the method provided by the foregoing embodiments.

[0092] In order to realize the above-mentioned embodiments, the present disclosure further proposes a computer program product, comprising a computer program, and the computer program is executed by a processor to realize the method provided by the foregoing embodiments.

[0093] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0094] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0095] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of this disclosure pertain.

[0096] The logic and / or steps represented in flow diagrams or otherwise described herein, for example, can be considered as a sequence of instructions to implement logic functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, processor- containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this specification, a "computer-readable medium" can be any means that can contain, store, communicate, propagate or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be a machine-readable storage device (e.g., magnetic, optical or other) a machine-readable storage diskette (e.g., floppy disk, optical disk, CD- ROM, etc.), a machine- readable storage card (e.g., PCMCIA card, etc.), a machine-readable storage tape (e.g., magnetic tape, optical tape, etc.), a machine-readable storage medium (e.g., RAM, ROM, etc.), a machine-readable signal (e.g., electrical, optical, etc.), a machine-readable medium (e.g., carrier wave, etc.) or any other suitable medium or means of embodying the program. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an EPROM, a FLASH memory card, an optical fiber, and a portable compact disc read-only memory (CD-ROM). Additionally, the computer-readable medium can be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and stored in a computer memory.

[0097] It should be understood that portions of the present disclosure can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, the various steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As such, if implemented in hardware, and in another embodiment, any of the following technologies, known in the art, or their combinations can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0098] Those skilled in the art can understand that all or part of the steps carried out by the above-mentioned embodiment methods can be completed by programs instructing related hardware, and the programs can be stored in a computer-readable storage medium. When the programs are executed, they include one of the steps of the method embodiments or a combination thereof.

[0099] In addition, each functional unit in each embodiment of the present disclosure can be integrated in one processing module, or each unit can exist physically separately, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software functional module. When the integrated module is realized in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0100] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present disclosure have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A method of positioning a human body for x-ray transmission examination, characterized by, The method comprises the following steps: obtaining a visible light image of a patient on a hospital bed and a preoperative medical image of the patient obtained by using a perspective examination device; obtaining a corresponding patient parameterized model based on the visible light image, and fitting a standard bone model based on the patient parameterized model; segmenting and reconstructing the preoperative medical image to obtain an individualized bone model; fusing the individualized bone model and the standard bone model based on target bone landmark points to obtain a target fusion image, so as to realize target organ positioning of the patient.

2. The method of positioning a body for x-ray transmission examination according to claim 1, wherein, The step of fitting the standard bone model based on the patient parameterized model comprises the following steps: generating bone wrapping surface data and obtaining skin surface data based on the patient parameterized model; fitting the standard bone model based on the bone wrapping surface data and the skin surface data.

3. The method of positioning a body for x-ray transmission examination according to claim 2, wherein, The step of fitting the standard bone model based on the bone wrapping surface data and the skin surface data using a nonlinear least square method comprises the following steps:

4. The method of positioning a body for x-ray transmission examination according to claim 3, wherein, setting a deformation surface parameter, obtaining energy based on the deformation surface parameter, the bone wrapping surface data and the skin surface data, and minimizing the energy using a nonlinear least square method, so as to obtain the standard bone model. The step of fusing the individualized bone model and the standard bone model based on target bone landmark points to obtain a target fusion image comprises the following steps:

5. The method of positioning a body for x-ray transmission examination according to claim 1, wherein, selecting a first target bone landmark point set of the individualized bone model in a preset order and a preset number; selecting a second target bone landmark point set of the standard bone model in a preset order and a preset number; fusing the first target bone landmark point set and the second target bone landmark point set in a corresponding order to obtain the target fusion image. The step of fusing the first target bone landmark point set and the second target bone landmark point set in a corresponding order to obtain the target fusion image comprises the following steps:

6. The method of positioning a body for x-ray transmission examination according to claim 5, wherein, corresponding each first target bone landmark point in the first target bone landmark point set and the second target bone landmark point set in a corresponding order; obtaining a spatial transformation combination, adjusting the individualized bone model based on the spatial transformation combination, so that the adjusted individualized bone model is consistent with a coordinate system of the standard bone model, thereby obtaining the target fusion image. The method comprises the following steps:

7. A human positioning system for X-ray transmission examination, characterized in that, an image acquisition module is configured to obtain a visible light image of a patient on a hospital bed and a preoperative medical image of the patient obtained by using a perspective examination device; a bone modeling module is configured to obtain a corresponding patient parameterized model based on the visible light image, and fit a standard bone model based on the patient parameterized model; a reconstruction module is configured to segment and reconstruct the preoperative medical image to obtain an individualized bone model; a fusion module is configured to fuse the individualized bone model and the standard bone model based on target bone landmark points to obtain a target fusion image, so as to realize target organ positioning of the patient. ​ 8. The human positioning system for x-ray transmission examination as claimed in claim 7 wherein, The skeleton modeling module, when used for fitting a standard skeleton model based on the patient parameterized model, is specifically configured to: generate skeleton wrapping surface data, and obtain skin surface data based on the patient parameterized model; and fit a standard skeleton model based on the skeleton wrapping surface data and the skin surface data.

9. An electronic device, comprising: The method comprises: a processor, and a memory connected to the processor in communication; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method of any one of claims 1-6.

10. A computer readable storage medium characterized by, The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by the processor to implement the method of any one of claims 1-6.

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