Method and apparatus for generating lesion model of target site

Abstract

A method and apparatus for generating a lesion model of a target site, and a computer readable storage medium. The method comprises: acquiring a target lesion type of a target site; on the basis of the target lesion type of the target site, determining a plurality of target sub-anatomical structure models from a model database of the target site; on the basis of the plurality of target sub-anatomical structure models, generating a lesion model of the target site about the target lesion type; and displaying the lesion model of the target site about the target lesion type. According to the present application, a lesion model of a corresponding lesion type can be generated for the target site.

Description

A lesion model generation method and device for a target site TECHNICAL FIELD

[0001] The present application relates to the technical field of medical engineering, in particular to a lesion model generation method and device for a target site. BACKGROUND

[0002] In real clinical practice, many diseases suffered by patients are often very complex, unlike the single disease given in textbooks. Such diseases are often a combination of multiple lesions and are varied. Commonly seen is congenital heart disease, which can be divided into single disease and complex malformation. The disease spectrum is extremely wide, including more than one hundred specific types, and can be combined with multiple malformations.

[0003] Multidisciplinary team (MDT) is the most effective way to treat complex diseases. However, the examination report is mostly in the form of text description, and the text description for complex diseases is obscure and difficult to understand, which makes it difficult to describe the specific situation of the lesion or malformation site of the complex disease, so that the doctor cannot establish a clear relationship with the clinical anatomy. In addition, patients generally lack clinical knowledge and lack proper consultation, and are prone to have a color-changing emotion when hearing about some complex diseases, which often leads to a failure of MDT communication and may cause misjudgment. SUMMARY

[0004] To solve the above problems, the present application provides a lesion model generation method and device for a target site, which will be described in detail below.

[0005] According to a first aspect, a lesion model generation method for a target site is provided in an embodiment, comprising:

[0006] acquiring ultrasound data of a target site;

[0007] displaying a lesion type selection interface of the target site;

[0008] determining a target lesion type of the target site in response to an operation on the lesion type selection interface based on the ultrasound data;

[0009] determining a plurality of target sub-anatomical structure models associated with the target lesion type from a model database of the target site based on the target lesion type of the target site;

[0010] generating a lesion model of the target site about the target lesion type based on the plurality of target sub-anatomical structure models;

[0011] displaying the lesion model of the target site about the target lesion type.

[0012] In one embodiment, the model database of the target site includes a plurality of sub-anatomical models; the plurality of sub-anatomical models includes at least one category of general sub-anatomical models, which are used to define a complete anatomy of the target site;

[0013] wherein:

[0014] at least one category of the general sub-anatomical models includes a plurality of general sub-anatomical models of the same category but different morphologies, which are used to differentiate types of pathologies of the target site;

[0015] and / or,

[0016] the plurality of sub-anatomical models further includes at least one category of special sub-anatomical models, which are used to differentiate types of pathologies of the target site.

[0017] In one embodiment, the target site includes a fetal heart or a heart;

[0018] at least one category of the general sub-anatomical models includes at least one of a chamber, a myocardium, an arterial vessel, a valve, and a venous vessel; and / or, at least one category of the special sub-anatomical models includes at least one of a defect, an oval foramen, and a tumor.

[0019] In one embodiment, the target site includes a blood vessel;

[0020] at least one category of the general sub-anatomical models includes at least one of a coronary artery, a carotid artery, an abdominal aorta, and a superficial vein; and / or, at least one category of the special sub-anatomical models includes at least one of a plaque, a hemangioma, and an embolism.

[0021] In one embodiment, the generating the pathology model of the target site with respect to the target type of pathology based on the plurality of target sub-anatomical models includes: superimposing the plurality of target sub-anatomical models in a certain order to generate the pathology model.

[0022] In one embodiment, the method of generating the pathology model further includes: in response to an editing command on the displayed pathology model, editing the pathology model.

[0023] In one embodiment, the editing the pathology model includes at least one of:

[0024] adding one or more sub-anatomical models in the pathology model;

[0025] deleting one or more sub-anatomical models contained in the pathology model;

[0026] editing the morphology and / or position of one or more sub-anatomical models included in the lesion model.

[0027] In one embodiment, the target site comprises a fetal heart or a heart; and the one or more sub-anatomical models are added to the lesion model, including at least one of:

[0028] adding a blood vessel branch to the lesion model;

[0029] adding a bridging blood vessel to the lesion model;

[0030] adding a tumor to the lesion model;

[0031] adding a defect to the lesion model.

[0032] In one embodiment, the target site comprises a fetal heart or a heart; and the morphology and / or position of one or more sub-anatomical models included in the lesion model are edited, including at least one of:

[0033] editing the thickness of a whole blood vessel or a local blood vessel in the lesion model;

[0034] editing the size, position and / or orientation of a tumor in the lesion model;

[0035] editing the size of an atrium or a ventricle in the lesion model;

[0036] editing the wall thickness of an atrium or a ventricle in the lesion model;

[0037] editing the size of an arterial valve in the lesion model;

[0038] editing the opening size of a mitral valve or a tricuspid valve in the lesion model;

[0039] editing the thickness of a mitral valve or a tricuspid valve in the lesion model;

[0040] editing the thickness of an arterial conus in the lesion model;

[0041] editing the superimposition order of the sub-anatomical models in the lesion model.

[0042] In one embodiment, the method further comprises:

[0043] determining a sub-anatomical model in the displayed lesion model that needs to be highlighted;

[0044] highlighting the determined sub-anatomical model.

[0045] According to a second aspect, one embodiment provides a method for generating a lesion model of a target site, comprising:

[0046] acquire a target lesion type of the target site;

[0047] determine a plurality of target sub-anatomical structure models from a model database of the target site based on the target lesion type of the target site;

[0048] generate a lesion model of the target site with respect to the target lesion type based on the plurality of target sub-anatomical structure models;

[0049] display the lesion model of the target site with respect to the target lesion type.

[0050] In an embodiment, the acquiring the target lesion type of the target site comprises:

[0051] displaying a lesion type selection interface of the target site;

[0052] determining the target lesion type of the target site in response to an operation on the lesion type selection interface.

[0053] In an embodiment, the acquiring the target lesion type of the target site comprises:

[0054] acquiring ultrasound data of the target site;

[0055] automatically identifying the target lesion type of the target site based on the ultrasound data of the target site.

[0056] In an embodiment, the model database of the target site comprises a plurality of sub-anatomical structure models; the plurality of sub-anatomical structure models comprises at least one category of general sub-anatomical structure models, the at least one category of general sub-anatomical structure models being used to define a complete anatomical structure of the target site;

[0057] wherein:

[0058] the at least one category of the general sub-anatomical structure models comprises a plurality of categories of general sub-anatomical structure models that are same in category but different in morphology and are used to distinguish lesion types of the target site;

[0059] and / or,

[0060] the plurality of sub-anatomical structure models further comprises at least one category of special sub-anatomical structure models, the at least one category of special sub-anatomical structure models being used to distinguish lesion types of the target site.

[0061] In an embodiment, the lesion model generation method further comprises: in response to an editing command on the displayed lesion model, editing the lesion model.

[0062] In an embodiment, in the generated lesion model, at least two of the plurality of target sub-anatomical model used to generate the lesion model are located in different layers.

[0063] In an embodiment, a method for generating a lesion model of a target site is provided, the method comprising:

[0064] providing a user interface, wherein the user interface comprises a control directed to a lesion model generation mode;

[0065] receiving an operation on the control on the user interface;

[0066] in response to the operation on the control on the user interface, entering a lesion model generation mode, wherein in the lesion model generation mode:

[0067] displaying a lesion type selection interface of the target site;

[0068] receiving a first selection operation on the lesion type selection interface;

[0069] in response to the first selection operation on the lesion type selection interface, determining a target general lesion type from one or more general lesion types included in the lesion type selection interface;

[0070] displaying one or more sub-lesion types associated with the target general lesion type, wherein the one or more sub-lesion types associated with the target general lesion type are sub-lesion types belonging to the target general lesion type;

[0071] receiving a second selection operation on the displayed one or more sub-lesion types;

[0072] in response to the second selection operation on the displayed one or more sub-lesion types, determining a target lesion type from the displayed one or more sub-lesion types;

[0073] generating a lesion model of the target site with respect to the target lesion type based on the target lesion type;

[0074] displaying the lesion model of the target site with respect to the second target lesion type.

[0075] In an embodiment, a method for generating a lesion model of a target site is provided, the method comprising:

[0076] providing a user interface, wherein the user interface comprises a control directed to a lesion model generation mode;

[0077] receiving an operation on the control on the user interface;

[0078] In response to the operation on the control of the user interaction interface, a lesion model generation mode is entered, wherein in the lesion model generation mode:

[0079] A lesion type selection interface of a target site is displayed, wherein the lesion type selection interface contains one or more lesion types;

[0080] In response to an operation on the lesion type selection interface, a target lesion type of the target site is determined from the one or more lesion types contained in the lesion type selection interface;

[0081] A lesion model of the target site with respect to the target lesion type is generated based on the target lesion type;

[0082] The lesion model of the target site with respect to the target lesion type is displayed.

[0083] In an embodiment, the generated lesion model contains a plurality of target sub-anatomical structure models, wherein at least two of the plurality of target sub-anatomical structure models contained in the lesion model are located in different layers of the lesion model.

[0084] In an embodiment, the method further comprises:

[0085] Determining a sub-anatomical structure model in the displayed lesion model that needs to be highlighted;

[0086] Highlighting the determined sub-anatomical structure model.

[0087] In an embodiment, the method further comprises: in response to an editing command on the displayed lesion model, editing the lesion model.

[0088] In an embodiment, the lesion model includes a blood vessel model, and the method further comprises:

[0089] Determining a first position at the blood vessel model of the lesion model;

[0090] According to the first position, a blood vessel branch model is added at the first position of the lesion model;

[0091] Alternatively,

[0092] Determining a first position at the blood vessel model of the lesion model;

[0093] Determining a second position at the blood vessel model of the lesion model;

[0094] According to the first position and the second position, a bridging blood vessel model connecting the first position and the second position is added in the lesion model.

[0095] In an embodiment, the lesion model comprises a fetal heart model or a heart model, and the method further comprises:

[0096] determining a new defect location in the fetal heart model or the heart model of the lesion model;

[0097] adding an interventricular septum defect model, an atrial septum defect model and / or an atrioventricular septum defect model at the new defect location of the lesion model.

[0098] In an embodiment, the method further comprises:

[0099] determining a new tumor location in the lesion model;

[0100] adding a tumor model at the new tumor location of the lesion model.

[0101] In an embodiment, the lesion model comprises a blood vessel model, and the method further comprises:

[0102] receiving a selection instruction of selecting the blood vessel model in the lesion model;

[0103] determining a target blood vessel model in the lesion model according to the received selection instruction;

[0104] receiving an adjustment instruction;

[0105] adjusting a position of the target blood vessel model, a direction of the target blood vessel model and / or a diameter and / or a radius of the target blood vessel model according to the adjustment instruction.

[0106] In an embodiment, the lesion model comprises a defect model, and the method further comprises:

[0107] receiving a selection instruction of selecting the defect model in the lesion model;

[0108] determining a target defect model in the lesion model according to the received selection instruction;

[0109] receiving an adjustment instruction;

[0110] adjusting a position of the target defect model and / or a size of the target defect model according to the adjustment instruction.

[0111] In an embodiment, the lesion model comprises a tumor model, and the method further comprises:

[0112] receiving a selection instruction of selecting the tumor model in the lesion model;

[0113] determining a target tumor model in the lesion model according to the received selection instruction;

[0114] receiving an adjustment instruction;

[0115] According to the adjustment instruction, a size and / or a position of the target tumor model is adjusted.

[0116] In an embodiment, the lesion model comprises an atrium model, a ventricle model, or a myocardium model, and the method further comprises:

[0117] receiving a selection instruction of selecting the atrium model, the ventricle model, or the myocardium model in the lesion model;

[0118] According to the received selection instruction, a target atrium model, a target ventricle model, or a target myocardium model is determined in the lesion model;

[0119] receiving an adjustment instruction;

[0120] According to the adjustment instruction, a size and / or a position of the target atrium model or the target ventricle model is adjusted, or a position and / or a thickness of the target myocardium model is adjusted.

[0121] In an embodiment, the method further comprises:

[0122] According to the adjustment of the target atrium model, the target ventricle model, or the target myocardium model, a position and / or a size of a defect model associated with the target atrium model, the target ventricle model, or the target myocardium model is automatically adjusted so as to match the adjusted defect model with the adjusted target atrium model, the target ventricle model, or the target myocardium model.

[0123] In an embodiment, the lesion model comprises an arterial valve model, and the method further comprises:

[0124] receiving a selection instruction of selecting the arterial valve model in the lesion model;

[0125] According to the received selection instruction, a target arterial valve model is determined in the lesion model;

[0126] receiving an adjustment instruction;

[0127] According to the adjustment instruction, a position and / or a size of the target arterial valve model is adjusted.

[0128] In an embodiment, the method further comprises:

[0129] According to the adjustment of the target arterial valve model, a position and / or a size of a blood vessel model associated with the arterial valve model is automatically adjusted so as to match the adjusted blood vessel model with the adjusted target arterial valve model.

[0130] In an embodiment, the lesion model comprises a mitral valve model or a tricuspid valve model, and the method further comprises:

[0131] receiving a selection instruction of selecting a mitral valve model or a tricuspid valve model in the lesion model;

[0132] determining a target mitral valve model or a target tricuspid valve model in the lesion model according to the received selection instruction;

[0133] receiving an adjustment instruction;

[0134] adjusting a position of the target mitral valve model or the target tricuspid valve model, an opening size of the target mitral valve model or the target tricuspid valve model, and / or a valve thickness of the target mitral valve model or the target tricuspid valve model according to the adjustment instruction.

[0135] In an embodiment, the method further comprises:

[0136] receiving a selection instruction of selecting a sub-anatomical structure model in the lesion model;

[0137] determining a to-be-adjusted sub-anatomical structure model in the lesion model according to the received selection instruction;

[0138] receiving an adjustment instruction;

[0139] adjusting a position, a shape, a size, a direction, and / or a thickness of the to-be-adjusted sub-anatomical structure model according to the adjustment instruction.

[0140] In an embodiment, the foregoing determining the target lesion type of the target site from the one or more lesion types included in the lesion type selection interface in response to the operation performed on the lesion type selection interface, generating the lesion model of the target site with respect to the target lesion type based on the target lesion type can comprise:

[0141] receiving a selection instruction of selecting a first target lesion type on the lesion type selection interface;

[0142] determining the first target lesion type according to the received selection instruction of selecting the first target lesion type;

[0143] determining a first lesion model of the first target lesion type according to the first target lesion type;

[0144] receiving a selection instruction of selecting a second target lesion type on the lesion type selection interface;

[0145] determining the second target lesion type according to the received selection instruction of selecting the second target lesion type;

[0146] determining a second lesion model of the second target lesion type according to the second target lesion type;

[0147] combining the first lesion model and the second lesion model to obtain a lesion model of the target site with respect to the target lesion type.

[0148] In one embodiment, the method further comprises:

[0149] determining a position of a new sub-anatomical structure model in the lesion model;

[0150] adding the sub-anatomical structure model at the position of the new sub-anatomical structure model.

[0151] In one embodiment, a lesion model generation apparatus of a target site is provided, comprising:

[0152] a memory for storing a program;

[0153] a processor for implementing the method of any of the embodiments herein by executing the program stored in the memory.

[0154] In one embodiment, a computer readable storage medium is provided, comprising a program capable of being executed by a processor to implement the method of any of the embodiments herein.

[0155] According to the lesion model generation method and apparatus of a target site and the computer readable storage medium of the above embodiments, a target lesion type of the target site is obtained, and a lesion model of the target site with respect to the target lesion type is generated based on the target lesion type of the target site, for example, a plurality of target sub-anatomical structure models are determined from a model database of the target site, and the lesion model of the target site with respect to the target lesion type is generated based on the plurality of target sub-anatomical structure models. Subsequently, the lesion model of the target site with respect to the target lesion type can be displayed. Through the present application, a lesion model of a corresponding lesion type can be generated for a target site, so that a physician, a patient or other user can intuitively view the lesion condition of the target site, thereby assisting the physician, the patient or other user to easily understand the lesion condition of the target site. BRIEF DESCRIPTION OF DRAWINGS

[0156] FIG. 1 is a flowchart of a lesion model generation method of a target site according to one embodiment;

[0157] FIG. 2 is a structural schematic diagram of a target lesion type identification model according to one embodiment;

[0158] FIG. 3 is a structural schematic diagram of an ultrasound imaging system according to one embodiment;

[0159] FIG. 4 is a flowchart of obtaining a target lesion type of a target site according to one embodiment;

[0160] FIG. 5(a) is a schematic diagram of a lesion type selection interface according to an embodiment; FIG. 5(b) is a schematic diagram of a lesion type selection interface according to an embodiment;

[0161] FIG. 6 is an example diagram of the shape of the superior vena cava corresponding to left superior vena cava lesion type 1 and the shape of the superior vena cava corresponding to left superior vena cava lesion type 2 according to an embodiment;

[0162] FIG. 7(a) is a schematic diagram of superimposition to generate a lesion model of persistent left superior vena cava lesion type 1 according to an embodiment; FIG. 7(b) is a schematic diagram of superimposition to generate a lesion model of persistent left superior vena cava lesion type 2 according to an embodiment;

[0163] FIG. 8 is a schematic diagram of a sub-anatomical structure model shared and distinguished by a lesion model of persistent left superior vena cava lesion type 1 and a lesion model of persistent left superior vena cava lesion type 2 according to an embodiment;

[0164] FIG. 9 is a flowchart of a method for generating a lesion model of a target site according to an embodiment;

[0165] FIG. 10 is a schematic diagram of adding a bridging blood vessel according to an embodiment;

[0166] FIG. 11 is a schematic diagram of adding a blood vessel branch according to an embodiment;

[0167] FIG. 12 is a schematic diagram of adding a defect according to an embodiment;

[0168] FIG. 13 is a schematic diagram of adding a tumor according to an embodiment;

[0169] FIG. 14 is a schematic diagram of local blood vessel thickness editing according to an embodiment;

[0170] FIG. 15 is a schematic diagram of overall blood vessel thickness editing according to an embodiment;

[0171] FIG. 16 is a schematic diagram of tumor editing according to an embodiment;

[0172] FIG. 17 is a schematic diagram of defect editing according to an embodiment;

[0173] FIG. 18 is a schematic diagram of atrium, ventricle, and ventricular wall thickness editing according to an embodiment;

[0174] FIG. 19 is a schematic diagram of arterial valve editing according to an embodiment;

[0175] FIG. 20 is a schematic diagram of mitral and tricuspid valve editing according to an embodiment;

[0176] FIG. 21 is a schematic diagram of arterial conus editing according to an embodiment;

[0177] FIG. 22 is a schematic diagram of superimposition order editing according to an embodiment;

[0178] FIG. 23 is a schematic diagram of a replacement of a sub-anatomical model according to an embodiment;

[0179] FIG. 24 is a schematic diagram of a deletion of a sub-anatomical model according to an embodiment;

[0180] FIG. 25 is a flowchart of a method for generating a lesion model of a target site according to an embodiment;

[0181] FIG. 26 is a schematic diagram of a structure of a device for generating a lesion model of a target site according to an embodiment. DETAILED DESCRIPTION

[0182] The application will be further described in details by specific embodiments with reference to the drawings. In different embodiments, similar elements are denoted by similar reference numerals. In the following embodiments, many details are described in order to make the application better understood. However, one skilled in the art can easily recognize that some features can be omitted in different cases, or can be replaced by other elements, materials, methods. In some cases, some operations related to the application are not shown or described in the specification in order to avoid the core of the application being overwhelmed by too many descriptions, and it is not necessary to describe these operations in detail for one skilled in the art according to the description in the specification and general technical knowledge in the art.

[0183] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various embodiments. Meanwhile, the steps or actions in the method description can also be sequentially adjusted or adjusted in a manner that one skilled in the art can easily see. Therefore, the order in the specification and the drawings is only for the purpose of clearly describing a certain embodiment, and does not mean that it is the necessary order, unless otherwise stated that a certain order must be followed.

[0184] The serial numbers of components in this paper, such as "first", "second", etc., are only used to distinguish the described objects, and have no order or technical meaning. Unless otherwise specified, "connection" and "coupling" in this application include direct and indirect connection (coupling).

[0185] Firstly, since the lesion structure of many parts of the patient is complex, if the doctor diagnoses the lesion site through, for example, ultrasonic images, the doctor needs to have considerable experience to establish a clear relationship between the ultrasonic image and the parameter and the clinical anatomy.

[0186] Secondly, some complex diseases, due to the complex relationship of the anatomical structure, it is not easy to describe the specific situation of the lesion or deformity site, resulting in the MDT communication not in place, which may cause misjudgment.

[0187] Secondly, medical reports (such as ultrasound reports) are mostly in the form of text description, and for complex diseases, the text description is obscure and difficult to understand, which is not conducive to doctors to popularize disease knowledge to patients.

[0188] In some schemes, doctors will describe diseases by drawing the anatomical structure of the lesion of the target site by hand, but hand-drawing requires high skills of doctors to draw and high understanding of different diseases, and the hand-drawing process is also very time-consuming, and few doctors can describe complex diseases by hand.

[0189] Please refer to FIG. 1, some embodiments provide a method for generating a lesion model of a target site, including the following steps:

[0190] Step 110: Obtain the target lesion type of the target site.

[0191] There are various ways to obtain the target lesion type of the target site in step 110, for example, the target lesion type of the target site can be obtained in an automatic manner, or the target lesion type of the target site can be obtained in a manual manner.

[0192] In some embodiments, step 110 obtains ultrasound data of the target site, and automatically identifies the target lesion type of the target site based on the ultrasound data of the target site.

[0193] Step 110 can realize automatic identification of the target lesion type of the target site by means such as machine learning. For example, step 110 inputs the ultrasound data of the target site into a target lesion type identification model to obtain the target lesion type; wherein the target lesion type identification model is used to take the ultrasound data of the target site as input data, and output the target lesion type of the target site after processing.

[0194] Please refer to FIG. 2, in some embodiments, the target lesion type identification model includes an input layer 111, an intermediate layer 112 and an output layer 113, and the intermediate layer 112 includes a convolution layer or a hidden layer; the target lesion type identification model extracts features of the ultrasound data of the target site input through the input layer 111 through the intermediate layer 1112, and outputs the target lesion type of the target site through the output layer 113 based on the extracted features.

[0195] In some embodiments, the target lesion type identification model is obtained by training a training set, and the data of the training set includes first data and second data, the label of the first data is the second data, the first data set is the ultrasound data of the target site, and the second data set is the corresponding target lesion type.

[0196] In some embodiments, in the process of training the target lesion type recognition model based on the first data and the second data, the first data is input into the target lesion type recognition model, and through iteration, the output data of the target lesion type recognition model is constantly close to the second data; for example, the error between the output data of the target lesion type recognition model and the second data is estimated, the parameters of the target lesion type recognition model are updated according to the error, and the above steps are repeatedly repeated to repeatedly update the parameters of the target lesion type recognition model until the error between the output data of the target lesion type recognition model and the second data is within a preset range.

[0197] In some embodiments, the target lesion type recognition model includes a model based on a convolutional neural network, a model based on a recurrent neural network, a model based on an adversarial neural network, a model based on an attention neural network, or a model based on a fully connected network.

[0198] It should be noted that the ultrasound data is obtained based on an ultrasound imaging technology, which is a technology for scanning the tissues and organs of the human body by using ultrasonic waves, and obtaining images of the corresponding regions by receiving and processing reflected signals.

[0199] For example, FIG. 3 is an example of an ultrasound imaging system 100, which can include an ultrasound probe 10, a transmission and reception control circuit 20, and a processor 30; in some embodiments, the ultrasound imaging system 100 can also include a display 40, and the components will be described below.

[0200] In some embodiments, the ultrasound probe 10 is configured to transmit ultrasound waves and receive echo signals of the ultrasound waves. In some embodiments, the ultrasound probe 10 includes a plurality of array elements configured to convert electrical signals and ultrasound waves. In some embodiments, the ultrasound probe 10 includes a plurality of array elements arranged in a linear array. In some embodiments, the ultrasound probe 10 includes a plurality of array elements arranged in a two-dimensional matrix. The array elements are configured to convert electrical signals to ultrasound signals according to a transmit sequence transmitted by the transmit and receive control circuit 20. The transmitted ultrasound waves (ultrasound signals) can include one or more scan pulses, one or more reference pulses, one or more push pulses, and / or one or more Doppler pulses, according to the application. The ultrasound signals can include focused waves, plane waves, and diverging waves, according to the wave pattern. The array elements are configured to transmit ultrasound waves according to the electrical signals, or to convert received ultrasound waves to electrical signals. Thus, each array element can be configured to convert electrical signals and ultrasound waves, to transmit ultrasound waves to a target region, and to receive echo signals of the ultrasound waves reflected by the target region. During an ultrasound examination, the transmit and receive control circuit 20 can control which array elements are used to transmit ultrasound beams (transmitting array elements), which array elements are used to receive ultrasound beams (receiving array elements), or which array elements are used to transmit ultrasound waves or receive echo signals of the ultrasound waves in time slots. The array elements used to transmit ultrasound waves can be excited by electrical signals at the same time, so that the ultrasound waves are transmitted at the same time. Alternatively, the array elements used to transmit ultrasound waves can be excited by electrical signals with time intervals, so that the ultrasound waves are transmitted with time intervals. If a minimum processing region for receiving and reflecting ultrasound waves in a target region is referred to as a location point in the tissue, the ultrasound waves reaching each location point in the target region will be reflected differently due to different acoustic impedances of the tissue at different location points. The reflected ultrasound waves are picked up by the receiving array elements, and each receiving array element can receive echo signals of ultrasound waves from multiple location points. The echo signals of the ultrasound waves from different location points received by each receiving array element form different channel echo data. For a receiving array element, the distances to different location points in the target region are different, so the times at which the echo signals of the ultrasound waves from different location points reach the receiving array element are different. The correspondence between the echo signals of the ultrasound waves and the location points can be identified according to the times at which the echo signals of the ultrasound waves reach the receiving array element.

[0201] The transmit and receive control circuit 20 is configured to control the ultrasound probe 10 to perform the transmission of the ultrasound wave and the reception of the echo signal of the ultrasound wave. For example, the transmit and receive control circuit 20 is configured to control the ultrasound probe 10 to transmit the ultrasound wave to the target region on one hand, and to control the ultrasound probe 10 to receive the ultrasound echo reflected by the tissue on the other hand. In some embodiments, the transmit and receive control circuit 20 is configured to generate a transmit sequence and a receive sequence, and output to the ultrasound probe 10. The transmit sequence is configured to control a part or all of the plurality of array elements in the ultrasound probe 10 to transmit the ultrasound wave to the target region, and the parameters of the transmit sequence include the number of array elements used for transmission and the parameters of the ultrasound wave (e.g. pulse amplitude, transmission voltage, transmission frequency, transmission times, transmission interval, transmission angle, transmission waveform, transmission aperture, line density, point density, and / or focal position, etc.). The receive sequence is configured to control a part or all of the plurality of array elements to receive the echo of the ultrasound wave after being reflected by the tissue, and the parameters of the receive sequence include the number of array elements used for reception and the parameters of the echo (e.g. reception angle, depth, etc.). The parameters of the ultrasound wave in the transmit sequence and the parameters of the echo in the receive sequence are different for different uses of the ultrasound echo or different images generated from the ultrasound echo.

[0202] The processor 30 is configured to process the ultrasound echo signal (i.e. the echo signal of the ultrasound wave) received by the ultrasound probe 10, for example, one or more links of processing of the echo signal / channel echo data of the ultrasound wave, which can include analog-to-digital conversion, signal demodulation, amplification, filtering, down-sampling, beamforming, modulus taking, logarithmic compression, and / or gray scale transformation, etc. The processor 30 can finally obtain the ultrasound image data by processing the echo signal of the ultrasound wave, for display by the display 40.

[0203] In some embodiments, the processor 30 includes, but is not limited to, a central processing unit (CPU), a micro controller unit (MCU), a field-programmable gate array (FPGA), a digital signal processing (DSP), and other devices for interpreting computer instructions and processing data in computer software.

[0204] The above is some description of the ultrasound imaging system 100.

[0205] It should be noted that the step 110 of obtaining the ultrasound data of the target region can be data at any one link from the echo signal / channel echo data of the ultrasound wave to the ultrasound image.

[0206] The step 110 of obtaining the ultrasound data of the target site can be obtained in real time by the ultrasound imaging device, or can be obtained by reading the ultrasound data obtained in advance and stored in the memory. The ultrasound data obtained in advance and stored in the memory can be stored locally, or can be stored on a remote server or a cloud server or another ultrasound device or other electronic device.

[0207] The above is some description of automatically identifying the target lesion type through the ultrasound data of the target site.

[0208] As described above, the step 110 can also obtain the target lesion type of the target site in a manual manner. Please refer to FIG. 4, in some embodiments, the step 110 includes the following steps:

[0209] Step 114: Display a lesion type selection interface of the target site.

[0210] Step 115: Determine the target lesion type of the target site in response to an operation on the lesion type selection interface based on the ultrasound data.

[0211] In some examples, the lesion type selection interface contains multiple options, each of which corresponds to a category of lesion types of the target site.

[0212] FIGS. 5(a) and 5(b) are two examples of the lesion type selection interface of the target site displayed in the case where the target site is the heart. In some cases, after the doctor determines the structural malformations of the heart of the patient and the degree of malformation based on the obtained ultrasound data, such as echocardiogram, the doctor can diagnose the patient with a congenital heart disease (i.e., the doctor determines the target lesion type of the target site); after the doctor has a diagnosis, the doctor can select the congenital heart disease and the corresponding type through the illustrated lesion type selection interface, so as to select the target lesion type of the heart on the interface. The lesion model generation device (e.g., an ultrasound imaging device, an ultrasound workstation, other electronic devices capable of implementing the lesion model generation method, etc.) receives the operation of the doctor on the selection interface, and in response to the operation, the lesion model generation device can determine (know) the target lesion type of the target site.

[0213] Step 130: Determine one or more target sub-anatomical structure models from the model database of the target site based on the target lesion type of the target site. For example, the step 130 determines one or more target sub-anatomical structure models associated with the target lesion type from the model database of the target site based on the target lesion type of the target site.

[0214] In some embodiments, the model database of the target site includes a plurality of sub-anatomical models, which includes at least one category of general sub-anatomical models, the at least one category of general sub-anatomical models being used to define the complete anatomy (also referred to as general anatomy) of the target site.

[0215] In some embodiments, the at least one category of general sub-anatomical models includes a plurality of categories of general sub-anatomical models with different morphologies, the plurality of categories of general sub-anatomical models being used to differentiate the types of pathologies of the target site.

[0216] In some embodiments, the plurality of sub-anatomical models further includes at least one category of special sub-anatomical models, the at least one category of special sub-anatomical models being used to differentiate the types of pathologies of the target site.

[0217] It is appreciated that the complete (or general) anatomical model of the target site includes one or more general sub-anatomical models, and the general sub-anatomical models can be obtained based on anatomical general knowledge of the target site, and thus the complete anatomy of the target site can be defined by the general sub-anatomical models.

[0218] Further, based on different types of pathologies of the target site, the general sub-anatomical models of the same category can have different morphologies, and thus the general sub-anatomical models of the same category with different morphologies can be used to differentiate the types of pathologies of the target site. That is, when the types of pathologies of the target site involve a category of general sub-anatomical models, the general sub-anatomical models of the category have a corresponding morphology for each type of pathology associated therewith. It is appreciated that when the types of pathologies of the target site do not involve a category of general sub-anatomical models, the general sub-anatomical models of the category have a normal, non-pathological morphology for the type of pathology.

[0219] Thus, for at least one category of general sub-anatomical models, the general sub-anatomical models can include a normal, non-pathological morphology, and can include a pathological morphology for a type of pathology of the target site associated therewith.

[0220] In addition, the complete (or general) anatomical model of the target site can further include special sub-anatomical models, which are not present in the normal, non-pathological anatomical model of the target site, and thus the special sub-anatomical models are used to differentiate the types of pathologies of the target site. An example of the special sub-anatomical models can be a tumor, which is further described below.

[0221] It can be seen that a complete anatomical model of the target site (general anatomical model) can be constructed by the general sub-anatomical structure models; on this basis, different lesion types of the target site can be defined by the general sub-anatomical structure models of the same category but different morphologies, and / or, on this basis, different lesion types of the target site can be defined by the special sub-anatomical structure models.

[0222] For example, the model database of the heart / tetal heart includes a plurality of sub-anatomical structure models, which include at least one category of general sub-anatomical structure models, or at least one category of general sub-anatomical structure models and at least one category of special sub-anatomical structure models. The at least one category of general sub-anatomical structure models is used to define the complete anatomical structure of the heart / tetal heart (general anatomical structure); in some examples, the at least one category of general sub-anatomical structure models includes at least one of chambers (left and right atrium and ventricle), myocardium, arterial vessels (main and pulmonary arteries and their branches, arterial conus, ductus arteriosus, etc.), valves (mitral and tricuspid valves and aortic and pulmonary valves), and venous vessels (superior vena cava, inferior vena cava, pulmonary veins, and innominate vein). Further, the at least one category of general sub-anatomical structure models includes a plurality of general sub-anatomical structure models of the same category but different morphologies for distinguishing lesion types of the heart / tetal heart, for example, in the case of persistent left superior vena cava lesion, it is generally divided into persistent left superior vena cava lesion type 1 and persistent left superior vena cava lesion type 2, so for the general sub-anatomical structure model of the superior vena cava category, it includes two general sub-anatomical structure models of the superior vena cava of the same category (both are superior vena cava) but different morphologies for distinguishing between left superior vena cava lesion type 1 and persistent left superior vena cava lesion type 2, and FIG. 6 shows an example of the morphology of the superior vena cava corresponding to left superior vena cava lesion type 1 and the morphology of the superior vena cava corresponding to left superior vena cava lesion type 2. Thus, the general sub-anatomical structure models of chambers (left and right atrium and ventricle), myocardium, arterial vessels (main and pulmonary arteries and their branches, arterial conus, ductus arteriosus, etc.), valves (mitral and tricuspid valves and aortic and pulmonary valves), and venous vessels (superior vena cava, inferior vena cava, pulmonary veins, and innominate vein) can include normal, non-lesion morphologies, and can also include lesion morphologies for lesion types related to the heart / tetal heart. In some examples, the at least one category of special sub-anatomical structure models includes at least one of defects, oval foramen, and tumors.

[0223] For example, the model database of the target site includes a plurality of sub-anatomical models, which include at least one category of general sub-anatomical models, or at least one category of general sub-anatomical models and at least one category of special sub-anatomical models; the at least one category of general sub-anatomical models are used to define the complete anatomical structure of the blood vessels (general anatomical structure); some examples, the at least one category of general sub-anatomical models include at least one of coronary artery, carotid artery, abdominal aorta and superficial vein; further, the at least one category of general sub-anatomical models include a plurality of general sub-anatomical models of the same category but different morphologies for distinguishing the lesion types of the blood vessels. Thus, the general sub-anatomical models of the coronary artery, carotid artery, abdominal aorta and superficial vein, etc. can include normal, non-lesion morphologies, and can also include lesion morphologies for the lesion types related to the blood vessels. In addition, some examples, the at least one category of special sub-anatomical models include at least one of plaque, angioma and embolism.

[0224] In summary, the model database of the target site includes a plurality of sub-anatomical models, which include at least one category of general sub-anatomical models, or at least one category of general sub-anatomical models and at least one category of special sub-anatomical models; therefore, step 130 determines one or more sub-anatomical models associated with the target lesion type of the target site from the model database of the target site as target sub-anatomical models based on the target lesion type of the target site, through which the complete anatomical structure of the target site can be defined, and the target lesion type of the target site can be embodied.

[0225] Step 150: generating a lesion model of the target site about the target lesion type based on the plurality of target sub-anatomical models determined in step 130. For example, step 150 superimposes the plurality of target sub-anatomical models determined in step 130 in a certain order to generate the lesion model.

[0226] Step 170: displaying the lesion model of the target site about the target lesion type.

[0227] Fig. 7(a) is an example of a lesion model display of persistent left superior vena cava lesion type 1, first, the target lesion type of the heart / fetal heart is acquired as persistent left superior vena cava lesion type 1, then based on the persistent left superior vena cava lesion type 1 of the heart / fetal heart, a plurality of target sub-anatomical structure models are determined from the model database of the heart / fetal heart, the determined target sub-anatomical structure models include left atrium, right atrium, left ventricle, right ventricle, oval foramen, myocardium, inferior vena cava and superior vena cava, wherein the left atrium, right atrium, left ventricle, right ventricle, oval foramen, myocardium and inferior vena cava are normal, non-lesion morphology, and the superior vena cava is the lesion morphology corresponding to the persistent left superior vena cava lesion type 1, by sequentially superimposing these target sub-anatomical structure models, the generation of the lesion model of persistent left superior vena cava lesion type 1 is realized. Similarly, Fig. 7(b) is an example of a lesion model display of persistent left superior vena cava lesion type 2, first, the target lesion type of the heart / fetal heart is acquired as persistent left superior vena cava lesion type 2, then based on the persistent left superior vena cava lesion type 2 of the heart / fetal heart, a plurality of target sub-anatomical structure models are determined from the model database of the heart / fetal heart, the determined target sub-anatomical structure models include left atrium, right atrium, left ventricle, right ventricle, oval foramen, myocardium, inferior vena cava and superior vena cava, wherein the left atrium, right atrium, left ventricle, right ventricle, oval foramen, myocardium and inferior vena cava are normal, non-lesion morphology, and the superior vena cava is the lesion morphology corresponding to the persistent left superior vena cava lesion type 2, by sequentially superimposing these target sub-anatomical structure models, the generation of the lesion model of persistent left superior vena cava lesion type 1 is realized. As can be seen from Fig. 8, for persistent left superior vena cava lesions, the lesion models of type 1 and type 2 have the structures of left atrium, right atrium, left ventricle, right ventricle, oval foramen, myocardium and inferior vena cava in common (all normal, non-lesion morphology), the only difference is the morphology of the superior vena cava, therefore, by the scheme of the present application, the modeling time is reduced while the consistency of the models between the types is improved.

[0228] Therefore, from another perspective, the model database of the target site in some embodiments includes N different categories of general sub-anatomical structure models, which can define the complete / general anatomical structure of the target site. In some cases, at least one of the N different categories of general sub-anatomical structure models has only one form, i.e., a normal, non-diseased form; in some cases, at least one of the N different categories of general sub-anatomical structure models has at least two forms, one of which is a normal, non-diseased form, and the other of which is a form specific to a particular target lesion type; in some cases, the model database of the target site further includes M special sub-anatomical structure models, which are not present in the normal, non-diseased anatomical model of the target site, and thus are used to distinguish the lesion type of the target site. One example of a special sub-anatomical structure model can be a tumor. These different cases are mainly determined by the types of target lesion types to which the model database of the target site is directed / associated.

[0229] Therefore, in some examples, after obtaining the target lesion type of the target site, the corresponding form of each general sub-anatomical structure model corresponding to the target lesion type is determined from each general sub-anatomical structure model in the model database of the target component based on the target lesion type of the target site, and the general sub-anatomical model corresponding to the corresponding form is taken as the target general sub-anatomical model; when the target lesion type also involves a special sub-anatomical structure model, the special sub-anatomical structure model associated / corresponding to the target lesion type is determined from the model database of the target component, or the special sub-anatomical structure model associated / corresponding to the target lesion type and its corresponding form are determined, to serve as the target general sub-anatomical model; finally, the lesion model of the target site with respect to the target lesion type is generated based on the determined target sub-anatomical structure models and displayed.

[0230] The above is an explanation of how to generate lesion models of a target site corresponding to different lesion types. In some examples, the user can also edit the generated lesion model of the target site, such as adding, deleting, and / or adjusting sub-anatomical structure models, etc., which can greatly improve the scope of application of the lesion model, while also reducing the data volume and complexity of the model database of the target site.

[0231] Please refer to FIG. 9, some embodiments provide a method for generating a lesion model of a target site, further comprising step 180: in response to an editing command for the displayed lesion model, editing the lesion model.

[0232] In some examples, the step 180 edits the lesion model in units of sub-anatomical structure models; for example, a user operates the lesion model by using a mouse or the like to select a sub-anatomical structure model to be edited, and based on the category of the selected sub-anatomical structure model to be edited, displays editing items contained in the category corresponding to the sub-anatomical structure model; different categories of sub-anatomical structure models can have different contents that can be edited, for example, atrium and ventricle can be adjusted in wall thickness, tumor can be adjusted in size and direction, etc.; the user edits the sub-anatomical structure model of the corresponding category by operating the editing items displayed based on the category corresponding to the sub-anatomical structure model.

[0233] In some embodiments, the step 180 edits the lesion model, including at least one of the following:

[0234] 1. Adding one or more sub-anatomical structure models in the lesion model; for example, when the target site includes a fetal heart or a heart, adding one or more sub-anatomical structure models in the lesion model, including at least one of the following: adding a blood vessel branch in the lesion model, adding a bridging blood vessel in the lesion model, adding a tumor in the lesion model, adding a defect in the lesion model;

[0235] 2. Deleting one or more sub-anatomical structure models contained in the lesion model;

[0236] 3. Editing the shape and / or position of one or more sub-anatomical structure models contained in the lesion model; for example, when the target site includes a fetal heart or a heart, editing the shape and / or position of one or more sub-anatomical structure models contained in the lesion model, including at least one of the following: editing the thickness of the overall blood vessel or the local blood vessel in the lesion model; editing the size, position and / or direction of the tumor in the lesion model; editing the size of the atrium or ventricle in the lesion model; editing the wall thickness of the atrium or ventricle in the lesion model; editing the size of the arterial valve in the lesion model; editing the opening size of the mitral valve or tricuspid valve in the lesion model; editing the thickness of the mitral valve or tricuspid valve in the lesion model; editing the thickness of the arterial conus in the lesion model; editing the superposition order of the sub-anatomical structure models in the lesion model.

[0237] The following will be described by taking the target site as a heart or a fetal heart as an example and in conjunction with the accompanying drawings.

[0238] (1) Editing of newly added blood vessels

[0239] The newly added blood vessels can include newly added bridging blood vessels and / or newly added blood vessel branches; the newly added bridging blood vessels refer to adding a blood vessel to connect two existing blood vessels in the lesion model; the newly added blood vessel branches refer to branching out a separate blood vessel from an existing blood vessel in the lesion model.

[0240] FIG. 10 is an example of adding a bridging blood vessel, which mainly includes the following steps:

[0241] The positions of the two blood vessels in the lesion model are determined, for example, by clicking the positions on the two blood vessels respectively to determine the connection positions of each blood vessel; in addition, the position of the bridging blood vessel is determined by the connection positions on the blood vessels, specifically, when one of the connection positions is determined by clicking the position on one of the blood vessels, it is determined that the bridging blood vessel is connected to which side of the wall of the current clicked blood vessel; after determining which side of the wall of the original blood vessel the bridging blood vessel is connected to, the connection path of the bridging blood vessel is determined based on the information and the determined two connection positions, and the corresponding blood vessel is added to the lesion model based on the connection path of the bridging blood vessel. The method of determining the connection path can be solved by an interpolation method, which can use spline interpolation or Lagrange interpolation, etc., which are not listed one by one.

[0242] FIG. 11 is an example of adding a blood vessel branch, which mainly includes the following steps:

[0243] The blood vessel to be branched and the blood vessel branch position are determined, for example, by clicking a position on a blood vessel to determine the blood vessel as the blood vessel to be branched, and the clicked position on the blood vessel is determined as the blood vessel branch position; the direction of the blood vessel branch is determined by clicking a blank area; in addition, the blood vessel branch is determined to be on which side of the wall of the blood vessel by the blood vessel branch position on the blood vessel, specifically, when the blood vessel to be branched and the blood vessel branch position are determined by clicking a position on a blood vessel, it is determined that the blood vessel branch to be added is connected to which side of the wall of the current clicked blood vessel; after determining which side of the wall of the original blood vessel the blood vessel branch is connected to, the connection path of the blood vessel branch is determined based on the information, the blood vessel branch position, and the clicked blank area position, and the corresponding blood vessel branch is added to the lesion model based on the connection path of the blood vessel branch. The method of determining the connection path can be solved by an interpolation method, which can use spline interpolation or Lagrange interpolation, etc., which are not listed one by one.

[0244] It can be seen that, whether it is a bridging blood vessel or a blood vessel branch, the essence is to add a blood vessel, so from the user's operation, the user only needs to determine two positions, for the bridging blood vessel, the connection positions on the two blood vessels are determined, and for the blood vessel branch, the blood vessel branch position on a blood vessel and another position (the clicked blank area position) are determined, so that the user only needs to click two positions, the first position and the second position, by means such as a mouse, and the device can add a blood vessel based on the first position and the second position.

[0245] In addition, the user can edit the thickness of the blood vessels, whether they are original blood vessels or newly added blood vessels. For example, a pull bar control or a knob control is provided to change the thickness of the newly added blood vessels. In one example, after the user selects one or more blood vessels, the device thickens or thins the selected blood vessels based on the operation instruction of the thickness editing control (e.g., a pull bar control or a knob control). This will be further described below.

[0246] (2) Editing of newly added defects

[0247] The newly added defects include, but are not limited to, newly added interventricular septal defects, newly added atrial septal defects, and newly added atrioventricular septal defects. FIG. 12 is an example of newly added defects, which mainly includes the following steps:

[0248] The user determines the location of the newly added defect, for example, the user clicks on the atrial septum for a newly added atrial septal defect, or clicks on the interventricular septum for a newly added interventricular septal defect; a newly added defect sub-anatomical structure model is added, and the defect sub-anatomical structure model is placed on the two side structures (which include, but are not limited to, atria, ventricles, etc.).

[0249] The purpose of the newly added defect is to connect the two sub-anatomical structure models by adding a defect.

[0250] In addition, the user can edit the size (dimension) of the defect, whether it is an original defect or a newly added defect. In one example, after the user selects one or more defects, the device increases or decreases the size of the selected defect based on the operation instruction of the size editing control (e.g., a pull bar control or a knob control). This will be further described below.

[0251] (3) Newly added tumors

[0252] The newly added tumors include, but are not limited to, newly added ventricular aneurysms, newly added auricular aneurysms, and newly added rhabdomyomas. FIG. 13 is an example of newly added tumors, which mainly includes the following steps:

[0253] The user determines the location of the newly added tumor by clicking to select the location of the newly added tumor, such as clicking on the ventricle for a newly added ventricular aneurysm or clicking on the atrium for a newly added auricular aneurysm; a newly added tumor sub-anatomical structure model is added, and the tumor sub-anatomical structure model is placed on the determined location of the newly added tumor.

[0254] The purpose of the newly added tumor is to generate a model of a cardiac chamber bulging lesion or a cardiac tumor.

[0255] In addition, the user can edit the size and / or orientation of the tumor, whether it is the original tumor or the newly added tumor. In one example, after the user selects the tumor, the device enlarges or reduces the selected tumor based on the operation instruction of the size editing control (e.g., a pull bar control or a knob control), and rotates the selected tumor along the center of the tumor in a clockwise or counterclockwise direction based on the operation instruction of the orientation editing control (e.g., a pull bar control or a knob control). This will be further described below.

[0256] (4) Local vessel thickness editing

[0257] The local vessel thickness editing is mainly used for the thickness (narrowing or widening) of a local area in a blood vessel. FIG. 14 is an example of local vessel thickness editing, which mainly includes the following steps:

[0258] The local blood vessel to be edited is determined, or the blood vessel to be edited and the local position to be edited thereof are determined; the user can determine the local blood vessel to be edited, or the blood vessel to be edited and the local position to be edited thereof by clicking a position on a blood vessel; in one example, the blood vessel includes but is not limited to the aorta, the pulmonary artery, the superior vena cava, the pulmonary vein, etc.

[0259] The thickness of the local blood vessel is edited through the operation instruction of the blood vessel local thickness editing control (e.g., a pull bar control or a knob control); in addition, the range size of the local area to be edited in the blood vessel can also be edited through the operation instruction of the local range editing control (e.g., a pull bar control or a knob control).

[0260] In one example, the local range editing control is used to smooth the connection range after the blood vessel thickness change, and the larger the editing range is, the more over-smoothing. A specific implementation process is as follows: a plurality of points (e.g., 7) are selected on the blood vessel wall on both sides of the blood vessel corresponding to the position clicked on the blood vessel, assuming that each side wall has pi to p7, wherein p4 is the blood vessel wall point closest to the position clicked on the blood vessel (i.e., the middle point of pi-p7), this point and the corresponding point on the other side wall are narrowed or widened according to the direction of the two-point line, and only the 1 / 3 between two p3 and two p5 is widened or narrowed, and the rest remains unchanged; then the part of the line is refitted by the interpolation method, thereby realizing the editing of the local blood vessel thickness.

[0261] (5) Whole blood vessel thickness editing

[0262] The whole blood vessel thickness editing is mainly used for the thickness (narrowing or widening) of the whole area of a blood vessel. FIG. 15 is an example of whole blood vessel thickness editing, which mainly includes the following steps:

[0263] Determine the blood vessel to be edited; in one example, the blood vessel includes but is not limited to aorta, pulmonary artery, superior vena cava, pulmonary vein, etc.; the user can select the blood vessel to be edited by clicking; in one example, after the user selects one or more blood vessels, the device changes the thickness of the selected blood vessels based on the operation instruction of the thickness editing control (such as a pull bar control or a knob control). One implementation algorithm can be: calculating the nearest point of each discretized point in a single blood vessel wall to be moved on the blood vessel to the opposite blood vessel wall, and determining the moving direction and moving distance of the first point in the single blood vessel wall to be moved relative to the nearest point of the opposite blood vessel wall based on the fine editing, so as to achieve the editing of the thickness of the whole blood vessel.

[0264] In some examples, during the thickness editing of a whole blood vessel, the changes on both sides of the blood vessel may, together with the connected sub-anatomical structure models, such as other blood vessels, arterial valves, and arterial conus, etc. For example, the whole thickness editing of the aortic blood vessel may need to be linked with the descending aorta, the arterial duct, the aortic valve, etc., the whole thickness editing of the pulmonary artery blood vessel may need to be linked with the pulmonary valve, the arterial conus, the pulmonary artery branch, etc., and the whole thickness editing of the pulmonary artery branch blood vessel may need to be linked with the main pulmonary artery. In some examples, the blood vessels that are linked can be edited according to the thickness editing of the original blood vessel (such as proportional thickening or thinning), and the sub-anatomical structure models other than blood vessels that are linked can be proportionally enlarged or reduced.

[0265] (6) Tumor size, position, and direction editing

[0266] Tumor editing mainly includes editing the size, position, and direction of the tumor, and the editable tumors include but are not limited to ventricular aneurysm, atrial aneurysm, rhabdomyoma, etc. FIG. 16 is an example of tumor editing, mainly including the following steps:

[0267] Determine the tumor to be edited, for example, by clicking to select the tumor to be edited; move the tumor by dragging the tumor to change its position, change the size of the selected tumor by the size editing control (such as a pull bar control or a knob control) operation instruction, and rotate the selected tumor along its center in a clockwise or counterclockwise direction based on the operation instruction of the direction editing control (such as a pull bar control or a knob control).

[0268] In addition, the tumor editing process may be accompanied by changes in the outer myocardium (ventricular aneurysm and atrial aneurysm).

[0269] (7) Defect size and position editing

[0270] Defect editing mainly includes editing the size and position of the defect, and the editable defects include but are not limited to interventricular septal defect, atrial septal defect, atrioventricular septal defect, etc. FIG. 17 is an example of defect editing, mainly including the following steps:

[0271] Determine the defect to be edited, for example, by clicking to select the defect to be edited; move the defect by dragging to change its position, or change the size of the selected defect by operating the size editing control (such as a pull bar control or a knob control).

[0272] (8) Atrial, ventricular and wall thickness editing

[0273] Atrial, ventricular and wall thickness editing is mainly used to edit the atrial size, ventricular size and wall thickness (myocardial size). FIG. 18 is an example of atrial, ventricular and wall thickness editing, mainly including the following steps:

[0274] Determine the atrial, ventricular or myocardial structure to be edited, for example, the user selects the atrial, ventricular or myocardial structure to be edited by clicking; keep the two endpoints fixed, scale the edge points of the structure to the distance between the two endpoints, and change the size of the structure by the pull bar or knob.

[0275] In addition, due to the presence of ventricular septal defects, atrial septal defects and the like, the corresponding structure linkage needs to be considered. Taking ventricular editing as an example, if there is a ventricular septal defect, the structure of the ventricular septal defect needs to be linked together, so that the area of the ventricular septal defect is attached to the two ventricles.

[0276] (9) Arterial valve size editing

[0277] Arterial valve editing is mainly used to edit the size of the pulmonary valve and the aortic valve. FIG. 19 is an example of arterial valve editing, mainly including the following steps:

[0278] Determine the arterial valve (such as the aortic valve or the pulmonary valve) to be edited, for example, the user selects the arterial valve to be edited by clicking; change the size of the selected valve by operating the size editing control (such as a pull bar control or a knob control). One implementation algorithm can be: calculate the center of the elliptical arterial valve, and enlarge or reduce the arterial valve as a whole with the center as the origin, thereby changing the size of the arterial valve.

[0279] In addition, the arterial valve sub-anatomical structure model is generally connected to the aorta (the aortic valve is connected to the aorta, and the pulmonary valve is connected to the pulmonary artery) above, and may also be connected to the arterial conus below. In order to ensure the connectivity between different structures while changing the size of the arterial valve, the linkage of the connected blood vessels also needs to be considered. The linkage of the related structures first changes the connection point with the arterial valve, and then in order to ensure the smoothness of the connection point and the whole blood vessel, a certain distance needs to be inserted to generate a linkage structure, and the interpolation method can be selected, for example, spline interpolation.

[0280] (10) Mitral and tricuspid valve opening size and thickness editing

[0281] The editing of the mitral and tricuspid valves mainly includes editing the opening size and thickness of the mitral and tricuspid valves. FIG. 20 is an example of editing the mitral and tricuspid valves, mainly including the following steps:

[0282] The mitral or tricuspid valve to be edited is determined, for example, the user selects the mitral or tricuspid valve to be edited by clicking; the opening size of the selected mitral or tricuspid valve is increased or decreased through the operation instruction of the opening size editing control (such as a pull bar control or a knob control); and the thickness of the selected mitral or tricuspid valve is increased or decreased through the operation instruction of the valve thickness editing control (such as a pull bar control or a knob control).

[0283] It can be understood that the opening size mainly changes the distance between the valve cusps, thereby reflecting whether the valve is stenotic; and the valve thickness mainly changes the thickness of the valve cusps, thereby simulating the thickening of the mitral and tricuspid valves. The opening size is the distance between the valve cusps, and the greater the distance, the greater the opening. The editing process can be to take the line connecting the two valve cusps as the editing direction, so that the overall editing realizes the change of the opening size.

[0284] (11) Editing of the thickness of the arterial conus

[0285] The editing of the arterial conus is mainly used to edit the thickness of the local arterial conus, thereby simulating the muscle type stenosis of the lower segment of the arterial valve. FIG. 21 is an example of editing the arterial conus, mainly including the following steps:

[0286] The arterial conus to be edited is determined, for example, the user selects the arterial conus to be edited by clicking; and the thickness of the arterial conus is increased or decreased through the operation instruction of the arterial conus thickness editing control (such as a pull bar control or a knob control). A specific process can be to take the center of the arterial conus as the thickening center, and to highlight the local area of the center, thereby realizing the change of the thickness of the arterial conus.

[0287] (12) Editing of the superimposition order of the sub-anatomical structure model

[0288] The editing of the superimposition order of the sub-anatomical structure model is used for the structural order of each sub-anatomical structure model in the lesion model when superimposed. FIG. 22 is an example of editing the superimposition order. First, the sub-anatomical structure model whose order is to be changed is selected, and then the sub-anatomical structure model located above it is clicked again, so that the order of the selected structure is changed to above the second clicked structure, thereby realizing the change of the structure superimposition order.

[0289] (13) Replacement of the sub-anatomical structure model

[0290] Sub-anatomic structure model replacement is used to modify the structure of the lesion model. Different morphologies of the same category of sub-anatomic structure model can be replaced, which can be used to combine some complex deformities. FIG. 23 is an example of sub-anatomic structure model replacement. First, the sub-anatomic structure model to be replaced is selected, and then other sub-anatomic structure models that replace the sub-anatomic structure model are selected through, for example, a pull-down control or the like.

[0291] (14) Sub-anatomic structure model deletion

[0292] Sub-anatomic structure model deletion can delete any one sub-anatomic structure model in the lesion model, for example, including but not limited to atrium, ventricle, blood vessel, tumor, etc. FIG. 24 is an example of sub-anatomic structure model deletion. The process of structural deletion first selects the sub-anatomic structure model to be deleted, and then the selected sub-anatomic structure model can be deleted in response to the user's deletion instruction.

[0293] The above is some description of editing of the lesion model or sub-anatomic structure model.

[0294] In some embodiments, in response to an editing command for the displayed lesion model, one or more sub-anatomic structure models of the lesion model are edited; in addition, it is determined whether there are other sub-anatomic structure models associated based on the sub-anatomic structure model to be edited and its editing content; when it is determined that there are other sub-anatomic structure models associated, the associated editing content of the other sub-anatomic structure models is determined based on the sub-anatomic structure model to be edited and its editing content, and the other sub-anatomic structure models associated are edited based on the associated editing content. For example, in the editing process of the overall thickness of a blood vessel, changes occur on both sides of the blood vessel, and it is possible to associate the connected sub-anatomic structure models, such as other blood vessels, arterial valves, and arterial conus, etc.

[0295] In some embodiments, based on the editing command on the displayed lesion model, the sub-anatomical structure model to be edited and the editing content are determined; the sub-anatomical structure model is edited based on the sub-anatomical structure model to be edited and the editing content; and the editing type is determined based on the sub-anatomical structure model to be edited and the editing content, the editing type including a non-association editing type and an association editing type; wherein the non-association editing type refers to that after the corresponding content editing of the sub-anatomical structure model to be edited, other sub-anatomical structure models also need to be adaptively updated or edited; the association editing type refers to that after the corresponding content editing of the sub-anatomical structure model to be edited, other sub-anatomical structure models also need to be adaptively updated or edited, therefore, when the editing type is the association editing type, the other sub-anatomical structure model to be linked and the linked editing content are determined based on the sub-anatomical structure model to be edited and the editing content, and the other sub-anatomical structure model to be linked is edited based on the linked editing content. For example, in the editing process of the overall thickness of a blood vessel, the changes on both sides of the blood vessel may link the connected sub-anatomical structure models such as other blood vessels, arterial valves and arterial conus, etc.

[0296] Referring to FIG. 25, the lesion model generation method of the target site of some embodiments can further include step 191 of determining the sub-anatomical structure model to be highlighted in the displayed lesion model, and step 192 of highlighting the determined sub-anatomical structure model.

[0297] The structure highlight can highlight any one or more sub-anatomical structure models in the lesion model.

[0298] Referring to FIG. 26, some embodiments further disclose a lesion model generation device 101 of a target site, which includes a memory 01 and a processor 02; the memory 01 is used to store a program; the processor 02 is used to realize the method described in any embodiment of the present disclosure by executing the program stored in the memory 01. For example, the processor 02 acquires the target lesion type of the target site, determines one or more target sub-anatomical structure models from the model database of the target site based on the target lesion type of the target site, for example, determines one or more target sub-anatomical structure models associated with the target lesion type from the model database of the target site based on the target lesion type of the target site; the processor 02 generates a lesion model of the target site about the target lesion type based on the determined above-mentioned multiple target sub-anatomical structure models, for example, superimposes the determined above-mentioned multiple target sub-anatomical structure models in a certain order to generate a lesion model, and the generated lesion model is used to be displayed.

[0299] In the foregoing embodiments, in the generated lesion model, the plurality of target sub-anatomical structure models used to generate the lesion model can be located in different layers. For example, at least two of them are located in different layers. In this way, the sub-anatomical structure models located in different layers can be conveniently adjusted, and the adjustment operation will not affect the sub-anatomical structure models located in other layers.

[0300] In an embodiment (not shown) of the present application, a lesion model generation method for a target site is provided, which can include the following steps.

[0301] First, a user interaction interface can be provided, wherein the user interaction interface can include a control pointing to a lesion model generation mode. Here, the user interface can be a software interface displayed on a display device, or a physical hardware interface such as a control panel, etc. Here, the "control" can be a software control on the software interface, such as an option in the menu, a soft key button, etc., or a hardware control such as a physical button, a voice control device, a gesture control device, etc. Here, the control "points to" the lesion model generation mode, which means that the control is associated with the lesion model generation mode, and through the control, the lesion model generation mode can be controlled, such as starting or closing the lesion model generation mode, etc.

[0302] The lesion model generation device can receive an operation on the control on the user interaction interface, and in response to the operation on the control on the user interaction interface, enter the lesion model generation mode. Here, the operation on the control on the user interaction interface can be a user operation, a lesion model generation device operation, or a remote operation on the control of the lesion model generation device through other devices. The operation can be various types of suitable operations, such as a user click operation, a user voice input, a user gesture operation, an instruction signal sent by the lesion model generation device or other devices, etc.

[0303] Here, the "entering the lesion model generation mode" means that the lesion model generation device enters a state capable of generating a lesion model, but does not mean that the state or user interface of the lesion model generation device must change or switch completely. For example, it can be based on the original working state or original user interface to start the corresponding subprogram or start the corresponding user interface, as long as the lesion model generation device can execute the lesion model generation function based on this.

[0304] In one embodiment, in the lesion model generation mode, the lesion model generation apparatus can display a lesion type selection interface of the target site, wherein the lesion type selection interface comprises one or more lesion types, and determine a target lesion type of the target site from the one or more lesion types comprised in the lesion type selection interface in response to an operation performed on the lesion type selection interface. Subsequently, generate a lesion model of the target site with respect to the target lesion type based on the target lesion type, and display the lesion model of the target site with respect to the target lesion type.

[0305] In one embodiment, the process of determining the target lesion type from the lesion type selection interface can also be determining a general lesion type first, and then determining a sub-lesion type according to the general lesion type, and taking the sub-lesion type as the target lesion type.

[0306] For example, referring to FIG. 5(a), the lesion model generation apparatus can display a lesion type selection interface of the target site, receive a first selection operation performed on the lesion type selection interface, and determine a target general lesion type from one or more general lesion types comprised in the lesion type selection interface in response to the first selection operation performed on the lesion type selection interface. According to the determined target general lesion type, display one or more sub-lesion types associated with the target general lesion type, wherein the one or more sub-lesion types associated with the target general lesion type are sub-lesion types belonging to the target general lesion type. Subsequently, receive a second selection operation performed on the displayed one or more sub-lesion types, and determine a target lesion type from the displayed one or more sub-lesion types in response to the second selection operation performed on the displayed one or more sub-lesion types. Subsequently, generate a lesion model of the target site with respect to the target lesion type based on the target lesion type, and display the lesion model of the target site with respect to the second target lesion type.

[0307] In the foregoing embodiments, the lesion type selection interface of the target site can be displayed in various suitable manners, such as by text, graphics, lists, and the like. The foregoing first operation and second operation, and the operation performed on the lesion type selection interface, can be various types of operations, such as a user’s click operation, a user’s drag operation, a user’s voice input, a user’s gesture operation, an instruction signal issued by the lesion model generation apparatus or other devices, and the like.

[0308] In some cases, the types of pathologies of some target sites (e.g., fetal heart or adult heart) are very diverse, and different types of pathologies have multiple different sub-pathology types. In this case, if all possible types of pathologies are displayed for selection, a very large display area is required in the display space of the limited display device, so that other necessary information cannot be displayed; or the font of the display needs to be very small, which is not convenient for the user to view; or it needs to be divided into multiple pages, and the user may need to flip through many pages to find the type of pathology he wants, which is not convenient for the user to operate and reduces the user's work efficiency. In the foregoing embodiments, by first displaying the total pathology type and then displaying and determining the sub-pathology type after determining the total pathology type, the number of alternative pathology types that need to be displayed at the same time can be effectively reduced, thereby making it more convenient for the user to operate.

[0309] In one embodiment, the foregoing process of determining the target pathology type of the target site from the one or more pathology types contained in the pathology type selection interface in response to the operation on the pathology type selection interface, generating the pathology model of the target site about the target pathology type based on the target pathology type, can be a combination of pathology models of two or more target pathology types. For example, in this embodiment, the process can include: receiving a selection instruction for selecting a first target pathology type on the pathology type selection interface, determining the first target pathology type according to the received selection instruction for selecting the first target pathology type, and determining a first pathology model of the first target pathology type according to the first target pathology type; and receiving a selection instruction for selecting a second target pathology type on the pathology type selection interface, determining the second target pathology type according to the received selection instruction for selecting the second target pathology type, and determining a second pathology model of the second target pathology type according to the second target pathology type. Then, the first pathology model and the second pathology model are combined to obtain the pathology model of the target site about the target pathology type. In this way, the user can more conveniently and flexibly generate the pathology model he expects.

[0310] In the foregoing embodiments, the generated pathology model contains multiple target sub-anatomical structure models, wherein at least two of the multiple target sub-anatomical structure models contained in the pathology model are located in different layers of the pathology model.

[0311] In the foregoing embodiments, the displayed sub-anatomical structure model that needs to be highlighted in the pathology model can also be determined, and the determined sub-anatomical structure model is highlighted.

[0312] In one embodiment, the generated lesion model can be operated to add a sub-anatomical model. In this embodiment, a position of the added sub-anatomical model can be determined in the lesion model, and a sub-anatomical model is added at the position of the added sub-anatomical model. Here, the position of the added sub-anatomical model can be determined by receiving an input of a user and in response to the input of the user, or can be determined by the lesion model generation device automatically or according to a received remote instruction, etc. The type of the added sub-anatomical model can be determined by receiving an input of a user and in response to the input of the user, or can be determined by the lesion model generation device automatically or according to a received remote instruction, etc.

[0313] For example, in one embodiment, the lesion model can include a blood vessel model. Referring to FIGS. 10 and 11, in this embodiment, the blood vessel model can be operated to add a blood vessel branch model or a bridging blood vessel model. In this embodiment, a first position can be determined in the blood vessel model of the lesion model, and a blood vessel branch model is added at the first position of the lesion model according to the first position. Alternatively, a first position can be determined in the blood vessel model of the lesion model, and a second position can be determined in the blood vessel model of the lesion model, and a bridging blood vessel model connecting the first position and the second position is added in the lesion model according to the first position and the second position. Here, the first position and the second position can be determined by receiving an input of a user and in response to the input of the user, or can be determined by the lesion model generation device automatically or according to a received remote instruction, etc.

[0314] For example, in one embodiment, the lesion model includes a fetal heart model or a heart model. Referring to FIG. 12, in this embodiment, the fetal heart model or the heart model can be operated to add a defect. In this embodiment, a position of the added defect can be determined in the fetal heart model or the heart model of the lesion model, and a defect model is added at the position of the added defect of the lesion model. The defect model can be an interventricular septum defect model, an atrial septum defect model, and / or an atrioventricular septum defect model, etc. Here, the position of the added defect can be determined by receiving an input of a user and in response to the input of the user, or can be determined by the lesion model generation device automatically or according to a received remote instruction, etc.

[0315] For example, in one embodiment, the added sub-anatomical structure can be a tumor model. Referring to FIG. 13, in this embodiment, a position of the added tumor can be determined in the lesion model, and a tumor model is added at the position of the added tumor of the lesion model. Here, the position of the added tumor can be determined by receiving an input of a user and in response to the input of the user, or can be determined by the lesion model generation device automatically or according to a received remote instruction, etc.

[0316] In one embodiment, various adjustment operations can be performed on the sub-anatomical structure models in the generated lesion model. Referring to FIGS. 14-24, in this embodiment, a selection instruction for selecting a sub-anatomical structure model in the lesion model can be received, a sub-anatomical structure model to be adjusted is determined in the lesion model according to the received selection instruction, and then an adjustment instruction is received and the position, shape, size, orientation, and / or thickness, etc. of the sub-anatomical structure model to be adjusted are adjusted according to the adjustment instruction. Here, the received selection instruction or adjustment instruction can be input by a user, can be automatically generated by the lesion model generation device, or can be received from other remote devices.

[0317] For example, in one embodiment, a blood vessel model can be included in the lesion model. In this embodiment, a selection instruction for selecting a blood vessel model in the lesion model can be received, and a target blood vessel model is determined in the lesion model according to the received selection instruction. Then, an adjustment instruction is received and the position of the target blood vessel model, the orientation of the target blood vessel model, and / or the diameter and / or radius of the target blood vessel model, etc. are adjusted according to the adjustment instruction.

[0318] For example, in one embodiment, a defect model is included in the lesion model. In this embodiment, a selection instruction for selecting a defect model in the lesion model can be received, and a target defect model is determined in the lesion model according to the received selection instruction. Then, an adjustment instruction is received and the position of the target defect model and / or the size of the target defect model, etc. are adjusted according to the adjustment instruction.

[0319] For example, in one embodiment, a tumor model is included in the lesion model. In this embodiment, a selection instruction for selecting a tumor model in the lesion model can be received, and a target tumor model is determined in the lesion model according to the received selection instruction. Then, an adjustment instruction is received and the position of the target tumor model, the size of the target tumor model, and / or the orientation of the target tumor model are adjusted according to the adjustment instruction.

[0320] For example, in one embodiment, an atrium model, a ventricle model, or a myocardium model can be included in the lesion model. In this embodiment, a selection instruction for selecting an atrium model, a ventricle model, or a myocardium model in the lesion model can be received, and a target atrium model, a target ventricle model, or a target myocardium model is determined in the lesion model according to the received selection instruction. Then, an adjustment instruction is received and the size and / or position of the target atrium model or target ventricle model, or the position and / or thickness of the target myocardium model, etc. are adjusted according to the adjustment instruction.

[0321] In this embodiment, the position and / or size of the defect model associated with the target atrium model, the target ventricle model or the target myocardium model can also be automatically adjusted according to the adjustment of the target atrium model, the target ventricle model or the target myocardium model, so that the adjusted defect model matches the adjusted target atrium model, the target ventricle model or the target myocardium model.

[0322] For example, in one embodiment, the lesion model can include an aortic valve model. In this embodiment, a selection instruction for selecting the aortic valve model in the lesion model can be received, and a target aortic valve model can be determined in the lesion model according to the received selection instruction. Then, an adjustment instruction can be received, and the position of the target aortic valve model and / or the size of the target aortic valve model, etc. can be adjusted according to the adjustment instruction.

[0323] In this embodiment, the position and / or size of the blood vessel model associated with the aortic valve model can also be automatically adjusted according to the adjustment of the target aortic valve model, so that the adjusted blood vessel model matches the adjusted target aortic valve model.

[0324] For example, in one embodiment, the lesion model can include a mitral valve model or a tricuspid valve model. In this embodiment, a selection instruction for selecting the mitral valve model or the tricuspid valve model in the lesion model can be received, and a target mitral valve model or a target tricuspid valve model can be determined in the lesion model according to the received selection instruction. Then, an adjustment instruction can be received, and the position of the target mitral valve model or the target tricuspid valve model, the opening size of the target mitral valve model or the target tricuspid valve model, and / or the valve thickness of the target mitral valve model or the target tricuspid valve model, etc. can be adjusted according to the adjustment instruction.

[0325] Similarly, more addition, deletion, editing, adjustment, etc. operations can also be performed on the generated lesion model, so as to facilitate the user to more conveniently and flexibly generate the lesion model he desires, and enable the physician, patient or other user to intuitively view the lesion condition of the target site, thereby assisting the physician, patient or other user to more easily understand the lesion condition of the target site.

[0326] In some embodiments, the processor 02 includes, but is not limited to, a central processing unit (CPU), a micro controller unit (MCU), a field-programmable gate array (FPGA), a digital signal processing (DSP), etc. for interpreting computer instructions and processing data in computer software.

[0327] In some embodiments, the ultrasound imaging system 100 herein can also be used to perform and implement the method described in any of the embodiments herein; for example, the processor 30 is configured to perform the method described in any of the embodiments herein; in some examples, the processor 30 obtains a target lesion type of a target site, determines one or more target sub-anatomical structure models from a model database of target sites based on the target lesion type of the target site, for example, determines one or more target sub-anatomical structure models associated with the target lesion type from the model database of target sites based on the target lesion type of the target site; the processor 30 generates a lesion model of the target site with respect to the target lesion type based on the determined plurality of target sub-anatomical structure models, for example, superimposes the determined plurality of target sub-anatomical structure models in a certain order to generate the lesion model, and the processor 30 controls the display 40 to display the lesion model of the target site with respect to the target lesion type.

[0328] The method and apparatus for generating a lesion model of a target site of some examples of the present application build a new communication bridge between doctors (e.g., sonographers and clinicians, etc.) and patients, and improve the accuracy of disease (e.g., congenital heart disease) description.

[0329] Various exemplary embodiments are described herein. However, those skilled in the art will recognize that changes and modifications can be made thereto without departing from the scope of the present disclosure. For example, various steps in the methods described can be performed in a different order, or can be performed concurrently, and some steps can be deleted, modified, or combined with other steps. Similarly, the components described can be configured differently, or in different combinations, depending on the needs of the system.

[0330] In the above embodiments, all or part of the above embodiments can be implemented by software, hardware, firmware or any combination thereof. In addition, as understood by those skilled in the art, the principles herein can be reflected in a computer program product on a computer readable storage medium preloaded with computer readable program code. Any tangible, non-transitory computer readable storage medium can be used, including magnetic storage devices (hard disk, floppy disk, etc.), optical storage devices (CD-ROM, DVD, Blu Ray disk, etc.), flash memory and / or the like. These computer program instructions can be loaded onto a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to form a machine, so that these instructions executed on the computer or other programmable data processing apparatus can generate a device that implements the specified function. These computer program instructions can also be stored in a computer readable storage medium, which can instruct the computer or other programmable data processing apparatus to operate in a specific way, so that the instructions stored in the computer readable storage medium can form a manufactured product, including an implementation device that implements the specified function. Computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so as to execute a series of operation steps on the computer or other programmable data processing apparatus to generate a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus can provide steps for implementing the specified function.

[0331] Although the principles herein have been shown in various embodiments, many modifications in structure, arrangement, proportions, elements, materials, and components specially adapted to specific environments and operational requirements can be used without departing from the principles and scope of the present disclosure. The above modifications and other changes or modifications will be included within the scope of the principles herein.

[0332] The foregoing detailed description has been described with reference to various embodiments. However, those skilled in the art will recognize that various modifications and changes can be made without departing from the scope of the present disclosure. Accordingly, the present disclosure is intended to be illustrative and not limiting, and all such modifications are intended to be included within the scope of the present disclosure. Also, advantages, other advantages, and solutions to problems have been described above as related to various embodiments. However, the benefits, advantages, solutions to problems and any element(s) that can cause these to occur, or to become more pronounced, are not to be construed as critical, required or essential features of the present disclosure. The terms "comprises", "comprising", or any other variations thereof, as used in the herein are of a non-exclusive inclusion, such that a process, method, article or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, system, article or apparatus. In addition, the terms "coupled" and any other variations thereof, as used herein, refer to physical connection, electrical connection, magnetic connection, optical connection, communicative connection, functional connection and / or any other connection.

[0333] One skilled in the art will recognize that the foregoing preferred embodiments can be readily adapted for numerous alterations without departing from the central teachings of the present application. Accordingly, the scope of the present application should be determined by the following claims.

Claims

1. A method for generating a lesion model of a target site, characterized in that: include: Acquiring ultrasound data of the target site; Display the lesion type selection interface of the target site; determining a target lesion type of the target site in response to an operation performed on the lesion type selection interface based on the ultrasound data; Based on the target lesion type of the target site, determining a plurality of target sub-anatomical structure models associated with the target lesion type from a model database of the target site; generating a lesion model of the target site with respect to the target lesion type based on the multiple target sub-anatomical structure models; A lesion model of the target site with respect to the target lesion type is displayed.

2. The lesion model generation method according to claim 1, wherein: The model database of the target part includes a plurality of sub-anatomical structure models; the plurality of sub-anatomical structure models include at least one category of general sub-anatomical structure models, and the at least one category of general sub-anatomical structure models is used to define the complete anatomical structure of the target part; in: At least one category of the universal sub-anatomical structure models includes a plurality of universal sub-anatomical structure models of the same category but different morphologies for distinguishing lesion types of the target site; and / or, The plurality of sub-anatomical structure models further include at least one category of special sub-anatomical structure models, and the at least one category of special sub-anatomical structure models is used to distinguish the lesion type of the target site.

3. The lesion model generation method according to claim 2, wherein: The target site includes the fetal heart or heart; At least one category of the general sub-anatomical structure models includes at least one of a chamber, a myocardium, an artery, a valve, and a vein; and / or at least one category of the special sub-anatomical structure models includes at least one of a defect, a foramen ovale, and a tumor.

4. The lesion model generation method according to claim 2, wherein: The target site includes a blood vessel; At least one category of the general sub-anatomical structure models includes at least one of the coronary arteries, carotid arteries, abdominal aorta, and superficial veins; and / or at least one category of the special sub-anatomical structure models includes at least one of plaques, hemangiomas, and emboli.

5. The lesion model generation method according to claim 1, wherein: Generating the lesion model of the target part with respect to the target lesion type based on the multiple target sub-anatomical structure models includes: superimposing the multiple target sub-anatomical structure models in a certain order to generate the lesion model.

6. The lesion model generation method according to any one of claims 1 to 5, wherein: Also includes: In response to an edit command for the displayed lesion model, the lesion model is edited.

7. The lesion model generation method according to claim 6, wherein: The editing of the lesion model includes at least one of the following: adding one or more sub-anatomical structure models to the lesion model; deleting one or more sub-anatomical structure models included in the lesion model; The one or more sub-anatomical structure models included in the lesion model are edited in terms of morphology and / or position.

8. The lesion model generation method according to claim 7, wherein: The target site includes a fetal heart or a heart; and one or more sub-anatomical structure models are added to the lesion model, including at least one of the following: adding vascular branches to the lesion model; adding a bridging vessel to the lesion model; adding a tumor to the disease model; A defect was added to the lesion model.

9. The lesion model generation method according to claim 7, wherein: The target site includes a fetal heart or a heart; and editing the morphology and / or position of one or more sub-anatomical structure models included in the lesion model includes at least one of the following: Editing the thickness of the entire blood vessel or a local blood vessel in the lesion model; Editing the size, location and / or orientation of a tumor in the lesion model; Editing the size of the atrium or ventricle in the lesion model; Editing the wall thickness of the atrium or ventricle in the lesion model; Editing the size of the aortic valve in the lesion model; Editing the opening size of the mitral valve or tricuspid valve in the lesion model; Editing the thickness of the mitral valve or tricuspid valve in the lesion model; Editing the thickness of the arterial cone in the lesion model; The superposition order of the sub-anatomical structure models in the lesion model is edited.

10. The lesion model generation method according to any one of claims 1 to 5, wherein: Also includes: determining a sub-anatomical structure model that needs to be highlighted in the displayed lesion model; The determined sub-anatomical structure model is highlighted.

11. A method for generating a lesion model of a target site, characterized in that: include: obtaining a target lesion type at the target site; Based on the target lesion type of the target site, determining a plurality of target sub-anatomical structure models from a model database of the target site; generating a lesion model of the target site with respect to the target lesion type based on the multiple target sub-anatomical structure models; A lesion model of the target site with respect to the target lesion type is displayed.

12. The lesion model generation method according to claim 11, wherein: The obtaining of the target lesion type of the target site includes: Displaying a lesion type selection interface for the target site; In response to an operation on the lesion type selection interface, a target lesion type of the target site is determined.

13. The lesion model generation method according to claim 11, wherein: The obtaining of the target lesion type of the target site includes: acquiring ultrasound data of the target site; Based on the ultrasound data of the target site, the target lesion type of the target site is automatically identified.

14. The lesion model generation method according to any one of claims 11 to 13, wherein: The model database of the target part includes a plurality of sub-anatomical structure models; the plurality of sub-anatomical structure models include at least one category of general sub-anatomical structure models, and the at least one category of general sub-anatomical structure models is used to define the complete anatomical structure of the target part; in: At least one category of the universal sub-anatomical structure models includes a plurality of universal sub-anatomical structure models of the same category but different morphologies for distinguishing lesion types of the target site; and / or, The plurality of sub-anatomical structure models further include at least one category of special sub-anatomical structure models, and the at least one category of special sub-anatomical structure models is used to distinguish the lesion type of the target site.

15. The lesion model generation method according to any one of claims 11 to 14, wherein: Also includes: In response to an edit command for the displayed lesion model, the lesion model is edited.

16. The lesion model generation method according to any one of claims 1 to 15, wherein: In the generated lesion model, at least two of the multiple target sub-anatomical structure models used to generate the lesion model are located in different layers.

17. A method for generating a lesion model of a target site, characterized in that: include: Providing a user interaction interface, wherein the user interaction interface includes a control pointing to a lesion model generation mode; receiving an operation on the control on the user interaction interface; In response to the operation on the control of the user interaction interface, a lesion model generation mode is entered, wherein in the lesion model generation mode: Display the lesion type selection interface of the target site; receiving a first selection operation on the lesion type selection interface; In response to the first selection operation performed on the lesion type selection interface, determining a target total lesion type from one or more total lesion types included in the lesion type selection interface; displaying one or more sub-lesion types associated with the target overall lesion type, wherein the one or more sub-lesion types associated with the target overall lesion type are sub-lesion types belonging to the overall target lesion type; receiving a second selection operation on the displayed one or more sub-lesion types; In response to the second selection operation performed on the displayed one or more sub-lesion types, determining a target lesion type from the displayed one or more sub-lesion types; generating a lesion model of the target part with respect to the target lesion type based on the target lesion type; A lesion model of the target site with respect to the second target lesion type is displayed.

18. A method for generating a lesion model of a target site, characterized in that: include: Providing a user interaction interface, wherein the user interaction interface includes a control pointing to a lesion model generation mode; receiving an operation on the control on the user interaction interface; In response to the operation on the control of the user interaction interface, a lesion model generation mode is entered, wherein in the lesion model generation mode: Displaying a lesion type selection interface for a target site, wherein the lesion type selection interface includes one or more lesion types; In response to an operation performed on the lesion type selection interface, determining a target lesion type of the target site from the one or more lesion types included in the lesion type selection interface; generating a lesion model of the target part with respect to the target lesion type based on the target lesion type; A lesion model of the target site with respect to the target lesion type is displayed.

19. The lesion model generation method according to claim 17 or 18, wherein: The generated lesion model includes multiple target sub-anatomical structure models, wherein at least two of the multiple target sub-anatomical structure models included in the lesion model are located in different layers of the lesion model.

20. The lesion model generation method according to claim 19, wherein: Also includes: determining a sub-anatomical structure model that needs to be highlighted in the displayed lesion model; The determined sub-anatomical structure model is highlighted.

21. The lesion model generation method according to any one of claims 17 to 20, wherein: Also includes: In response to an edit command for the displayed lesion model, the lesion model is edited.

22. The lesion model generation method according to any one of claims 17 to 20, wherein: The lesion model includes a blood vessel model, and the method further includes: determining a first position at the blood vessel model of the lesion model; According to the first position, adding a blood vessel branch model at the first position of the lesion model; or, determining a first position at the blood vessel model of the lesion model; determining a second position at the blood vessel model of the lesion model; According to the first position and the second position, a bridging blood vessel model connecting the first position and the second position is added to the lesion model.

23. The lesion model generation method according to any one of claims 17 to 20, wherein: The lesion model includes a fetal heart model or a heart model, and the method further includes: Determining a newly added defect location in the fetal heart model or the heart model of the lesion model; A ventricular septal defect model, an atrial septal defect model and / or an atrioventricular septal defect model is added at the newly added defect position of the lesion model.

24. The lesion model generation method according to any one of claims 17 to 20, wherein: The method further comprises: determining a new tumor location in the lesion model; A tumor model is added at the newly added tumor position of the lesion model.

25. The lesion model generation method according to any one of claims 17 to 20, wherein: The lesion model includes a blood vessel model, and the method further includes: receiving a selection instruction for selecting a blood vessel model in the lesion model; determining a target blood vessel model in the lesion model according to the received selection instruction; receiving adjustment instructions; According to the adjustment instruction, the position of the target blood vessel model, the direction of the target blood vessel model and / or the diameter and / or radius of the target blood vessel model are adjusted.

26. The lesion model generation method according to any one of claims 17 to 20, wherein: The lesion model includes a defect model, and the method further includes: receiving a selection instruction for selecting a defect model in the lesion model; determining a target defect model in the lesion model according to the received selection instruction; receiving adjustment instructions; According to the adjustment instruction, the position of the target defect model and / or the size of the target defect model are adjusted.

27. The lesion model generation method according to any one of claims 17 to 20, wherein: The lesion model includes a tumor model, and the method further includes: receiving a selection instruction for selecting a tumor model from the lesion model; determining a target tumor model in the lesion model according to the received selection instruction; receiving adjustment instructions; According to the adjustment instruction, the position of the target tumor model, the size of the target tumor model and / or the direction of the target tumor model are adjusted.

28. The lesion model generation method according to any one of claims 17 to 20, wherein: The lesion model includes an atrial model, a ventricular model or a myocardial model, and the method further includes: receiving a selection instruction for selecting an atrial model, a ventricular model, or a myocardial model in the lesion model; determining a target atrial model, a target ventricular model, or a target myocardial model in the lesion model according to the received selection instruction; receiving adjustment instructions; According to the adjustment instruction, the size and / or position of the target atrial model or the target ventricular model is adjusted, or the position of the target myocardial model and / or the thickness of the target myocardial model is adjusted.

29. The lesion model generation method according to any one of claims 28, wherein: The method further comprises: According to the adjustment of the target atrial model, the target ventricular model or the target myocardial model, the position and / or size of the defect model associated with the target atrial model, the target ventricular model or the target myocardial model is automatically adjusted so that the adjusted defect model matches the adjusted target atrial model, the target ventricular model or the target myocardial model.

30. The lesion model generation method according to any one of claims 17 to 20, wherein: The lesion model includes an aortic valve model, and the method further includes: receiving a selection instruction for selecting an aortic valve model in the lesion model; determining a target aortic valve model in the lesion model according to the received selection instruction; receiving adjustment instructions; According to the adjustment instruction, the position of the target aortic valve model and / or the size of the target aortic valve model are adjusted.

31. The lesion model generation method according to any one of claims 30, wherein: The method further comprises: According to the adjustment of the target aortic valve model, the position and / or size of the blood vessel model associated with the aortic valve model is automatically adjusted so that the adjusted blood vessel model matches the adjusted target aortic valve model.

32. The lesion model generation method according to any one of claims 17 to 20, wherein: The lesion model includes a mitral valve model or a tricuspid valve model, and the method further includes: receiving a selection instruction to select a mitral valve model or a tricuspid valve model in the lesion model; determining a target mitral valve model or a target tricuspid valve model in the lesion model according to the received selection instruction; receiving adjustment instructions; According to the adjustment instruction, the position of the target mitral valve model or the target tricuspid valve model, the opening size of the target mitral valve model or the target tricuspid valve model and / or the valve thickness of the target mitral valve model or the target tricuspid valve model are adjusted.

33. The lesion model generation method according to any one of claims 17 to 20, wherein: The method further comprises: receiving a selection instruction for selecting a sub-anatomical structure model in the lesion model; Determining a sub-anatomical structure model to be adjusted in the lesion model according to the received selection instruction; receiving adjustment instructions; According to the adjustment instruction, the position, shape, size, direction and / or thickness of the sub-anatomical structure model to be adjusted is adjusted.

34. The lesion model generation method according to any one of claims 17 to 20, wherein: In response to an operation performed on the lesion type selection interface, determining a target lesion type of the target site from the one or more lesion types included in the lesion type selection interface, and generating a lesion model of the target site with respect to the target lesion type based on the target lesion type, including: receiving a selection instruction for selecting a first target lesion type on the lesion type selection interface; determining the first target lesion type according to the received selection instruction for selecting the first target lesion type; determining a first lesion model of the first target lesion type according to the first target lesion type; receiving a selection instruction for selecting a second target lesion type on the lesion type selection interface; determining a second target lesion type according to a received selection instruction for selecting a second target lesion type; determining a second lesion model of the second target lesion type according to the second target lesion type; The first lesion model and the second lesion model are combined to obtain a lesion model of the target part with respect to the target lesion type.

35. The lesion model generation method according to any one of claims 17 to 20, wherein: The method further comprises: Determining a location of a newly added sub-anatomical structure model in the lesion model; A sub-anatomical structure model is added at the location of the newly added sub-anatomical structure model.

36. A device for generating a lesion model of a target site, characterized in that: include: Memory, used to store programs; A processor, configured to implement the method according to any one of claims 1 to 35 by executing the program stored in the memory.

37. A computer-readable storage medium, characterized in that The method comprises a program which can be executed by a processor to implement the method according to any one of claims 1 to 35.

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