A fully computer-intelligent high-similarity prosthesis production method and system

Abstract

The present invention discloses a fully computerized intelligent method and system for producing highly similar prostheses, comprising: a main computer collecting patient information and utilizing a pre-built artificial intelligence model to obtain a 3D model containing segmentation information of each tissue within the patient's missing part, and obtaining a 2D unfolded image of the surface of the 3D model based on the patient information and the 3D model; controlling a 3D printing device to print the 3D model to obtain the various layered parts of the prosthesis body; controlling a film-making device to produce a film on the surface of the missing part; controlling a 2D printing device to print the 2D unfolded image onto the film on the surface of the missing part to form a skin; controlling an assembly device to combine the various parts of the prosthesis, attach the skin to the surface of the prosthesis body, and perform surface treatment to obtain the finished prosthesis. The present invention computerizes and intelligentizes the prosthesis production process, can provide high-precision, highly similar prostheses in a shorter time, and reduces production costs; using artificial intelligence technology for prosthesis design and modeling can improve prosthesis quality and similarity.

Description

Technical Field

[0001] The present invention belongs to the field of prosthesis production and manufacturing, and in particular relates to a fully computer-intelligence-based high-similarity prosthesis production method and system. Background Art

[0002] For disabled patients, good prostheses can not only help users carry out daily activities independently and comfortably, but also help users build confidence and reintegrate into society, which is conducive to the stable development of society.

[0003] Traditional prosthesis production methods generally include the following separate processes: (1) 3D measurement sampling of the human body shape; (2) 3D printing of the negative mold; (3) making the positive mold using human body simulation plastic (such as silicone rubber); (4) manual surface spraying; and (5) surface protection treatment.

[0004] Although the sampling step in current prosthesis production methods can be completed with the help of digital instruments and equipment, the production process still relies extensively on manual operations. Therefore, the entire process is not only time-consuming, but also requires each operator to have rich personal experience and skills. Especially in terms of surface painting, superb skills and exquisite artistic talent are required, similar to treating body art. In addition, when dealing with large-scale loss or bilateral organ loss, it is necessary to rely on the experience mastered by craftsmen through long-term practice to conceive the shape of the patient's missing part. Because it is difficult to imagine the specific shape of the missing part of the body, the reconstruction of asymmetric parts such as lips and noses is difficult to achieve if it relies entirely on manual labor. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, the present invention provides a fully computer-intelligible, highly similar prosthesis production method and system. The technical problems to be solved by the present invention are achieved through the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides a fully computer-intelligent method for producing a highly similar prosthesis, the method comprising:

[0007] The host computer collects patient information and uses a pre-built artificial intelligence model to obtain a 3D model containing segmentation information of each tissue within the patient's missing part. Based on the collected patient information and the 3D model, a 2D unfolded image of the surface layer of the 3D model is obtained; wherein the tissues include bones, muscles, tendons, fat layers, and skin layers;

[0008] The host computer controls the 3D printing device to print the 3D model to obtain the prosthesis body;

[0009] The main computer controls the film-making equipment to make a surface film at the missing part;

[0010] The host computer controls the 2D printing device to print the 2D expanded image onto the surface film of the missing part to form a skin;

[0011] The main computer controls the assembly equipment to attach the skin to the surface of the prosthesis body, and performs surface treatment to obtain a completed prosthesis.

[0012] In one embodiment of the present invention, in the case where the defective part has no symmetrical healthy reference side, the host computer collects patient information and uses a pre-built artificial intelligence model to obtain a 3D model containing segmentation information of each tissue within the patient's defective part. Based on the collected patient information and the 3D model, a 2D unfolded image of the surface of the 3D model is obtained, including:

[0013] The host computer uses a pre-built first model to reconstruct a neural network, collects information about the remaining parts of the patient, and performs a three-dimensional reconstruction of the missing part to obtain a reconstructed three-dimensional structural model and a reconstructed surface image of the missing part; wherein the first model reconstruction neural network is implemented using generative AI technology;

[0014] The three-dimensional structural model is subjected to structural segmentation of internal tissues using a pre-constructed second structural hierarchical neural network, and is modified using model attribute-related information in the collected patient information and a pre-constructed third model modification neural network, to obtain a 3D model after processing;

[0015] The surface image reconstructed from the missing part is subjected to two-dimensional image expansion according to the 3D model to obtain a 2D expanded image.

[0016] In one embodiment of the present invention, in the case where the missing part has a symmetrical healthy reference side, the host computer collects patient information and uses a pre-built artificial intelligence model to obtain a 3D model containing segmentation information of each tissue within the patient's missing part. Based on the collected patient information and the 3D model, a 2D unfolded image of the surface of the 3D model is obtained, including:

[0017] The host computer collects depth information of the healthy reference side and the remaining part of the missing part by means of a depth information collection device, and collects multiple surface images of the healthy reference side at different angles by means of an image collection device;

[0018] The collected depth information is used to perform three-dimensional reconstruction of the missing part to obtain a reconstructed three-dimensional structural model;

[0019] The three-dimensional structural model is subjected to structural segmentation of internal tissues using a pre-constructed second structural hierarchical neural network, and is modified using model attribute-related information in the collected patient information and a pre-constructed third model modification neural network, to obtain a 3D model after processing;

[0020] The collected multiple surface images are expanded in two dimensions according to the 3D model to obtain a 2D expanded image.

[0021] In one embodiment of the present invention, the model attribute related information includes:

[0022] The patient's age, gender, height, weight and skin color; the model modification includes modifying the prosthesis size, shape, surface color and surface texture.

[0023] In one embodiment of the present invention, the process of obtaining a 2D unfolded image is to use a preset reverse filtering algorithm to compare the digital image of the surface of the 3D model with the printed image obtained by using a 2D printing device, compensate for the real visual difference between the digital image and the printed image, and obtain the 2D unfolded image to be printed; wherein, the digital image includes: when the missing part has no symmetrical healthy reference side, the image of the surface image reconstructed of the missing part according to the 3D model is two-dimensionally unfolded, or when the missing part has a symmetrical healthy reference side, the image of multiple collected surface images is two-dimensionally unfolded according to the 3D model.

[0024] In one embodiment of the present invention, the process of the preset reverse filtering algorithm includes:

[0025] Step c1, reading initial parameters; wherein the initial parameters include image matrix size, similarity threshold and maximum number of iterations;

[0026] Step c2, acquiring the digital image;

[0027] Step c3, printing the digital image using a 2D printing device to obtain an original printed image;

[0028] Step c4, using the same image acquisition device used in the process of obtaining the digital image, performing image acquisition on the digital image and the original printed image to obtain corresponding acquired images respectively;

[0029] Step c5, calculating an inverse filtering function using the captured images corresponding to the current digital image and the original printed image;

[0030] Step c6, applying the reverse filtering function to the current digital image to obtain a reverse filtered digital image;

[0031] Step c7, determining whether the current number of iterations is less than the maximum number of iterations; if so, executing step c8; if not, executing step c14;

[0032] Step c8, printing the currently obtained reverse filtered digital image using a 2D printing device to obtain a first printed image;

[0033] Step c9, using the same image acquisition device, performing image acquisition on the currently obtained reverse filtered digital image and the first printed image to obtain corresponding acquired images;

[0034] Step c10, calculating a similarity index for the acquired images corresponding to the currently obtained reverse filtered digital image and the first printed image;

[0035] Step c11, determining whether the similarity index is greater than the similarity threshold; if so, executing step c12; if not, executing step c13;

[0036] Step c12, determining the currently obtained reverse filtered digital image as the 2D expanded image to be printed;

[0037] Step c13, storing the currently obtained reverse filtered digital image and the similarity index, replacing the current digital image with the currently obtained reverse filtered digital image, replacing the acquired image corresponding to the current digital image with the acquired image corresponding to the currently obtained reverse filtered digital image, and replacing the acquired image corresponding to the current original printed image with the acquired image corresponding to the current first printed image;

[0038] Step c14 , searching for the reverse filtered digital image with the largest similarity index in all iterations, and using it as the 2D expanded image to be printed.

[0039] In one embodiment of the present invention, the host computer controls a 3D printing device to print the 3D model to obtain a prosthetic body, including:

[0040] The host computer controls the 3D printing device to print with materials that match the tissues in the 3D model to obtain a prosthesis body; wherein the materials that match the tissues include a variety of optional materials.

[0041] In one embodiment of the present invention, for each tissue, the matching material determines an adapted one from a plurality of optional materials for the tissue according to the collected patient information, so that the matched material has a hardness and viscoelasticity most similar to the patient's real body.

[0042] In one embodiment of the present invention, the main computer controls the film-making equipment to produce the surface film of the missing part, including:

[0043] The main computer controls the film-making device to obtain a sol-state silicone rubber solution, and places the solution on a printing paper with a preset thickness and a preset surface roughness to form a silicone rubber solution layer with a preset thickness;

[0044] The main computer controls the film-making equipment to cover the upper surface of the silicone rubber solution layer with a substrate sheet, and after the silicone rubber solution layer forms a gel, peels off the silicone rubber solution layer to obtain a surface film of the missing part, and the original contact surface between the surface film of the missing part and the printing paper serves as the printing surface; wherein, the bonding strength of the surface of the substrate sheet to the silicone rubber is higher than the bonding strength of the surface of the printing paper to the silicone rubber, and the printing paper is suitable for the 2D printing equipment.

[0045] In one embodiment of the present invention, the preset surface roughness is determined according to the roughness of the patient's skin surface in the collected patient information; and the preset thickness is determined according to the thickness of the patient's skin in the collected patient information.

[0046] In one embodiment of the present invention, before the host computer controls the 2D printing device to print the 2D expanded image onto the surface film of the missing part to form a skin, the method further includes:

[0047] The main computer controls the printing system to treat the surface of the surface film of the missing part with a surface treatment liquid, and evenly prints the pigment ink after the treatment.

[0048] In one embodiment of the present invention, the main computer controls the assembly equipment to attach the skin to the surface of the prosthesis body, including:

[0049] The main computer controls the printing system to apply glue and lubricant to the surface of the prosthesis body, covers the coated surface of the prosthesis body with the skin, and controls the uniform pressurizing instrument to perform pressurization so that the skin and the prosthesis body are tightly fitted to form an integral structure.

[0050] In one embodiment of the present invention, the density and adhesion of the glue match the collected patient information; and the lubricant is a biocompatible lubricant.

[0051] In one embodiment of the present invention, the surface treatment comprises:

[0052] The main computer controls the inkjet printing system to coat the surface of the prosthesis body after the skin is attached with a protective layer similar to the surface of human skin.

[0053] In a second aspect, an embodiment of the present invention provides a fully computer-intelligent high-similarity propagation system, the system comprising:

[0054] A main computer and other components; the main computer controls the other components to complete the method steps described in the first aspect; the other components include an image acquisition and storage system, a film-making device, an assembly device, a printing system, a uniform pressurizing instrument, and a printing system; the image acquisition and storage system includes a depth information acquisition device and an image acquisition device; the printing system includes at least a 3D printing device and a 2D printing device.

[0055] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method provided in the first aspect of the present invention are implemented.

[0056] Beneficial effects of the present invention:

[0057] The fully computer-intelligent, high-similarity prosthesis production method and system provided by the embodiments of the present invention uses a main computer to control the remaining components to complete the prosthesis design and production. All of the above processes are computerized and intelligentized as a whole, which can provide high-precision, high-similarity prostheses in a shorter time. The production process is digitized through computer operation, and there is no need to rely on manual experience or skills, which can greatly reduce working time and costs.

[0058] Furthermore, the embodiments of the present invention incorporate artificial intelligence technology and combine it with patient information for prosthesis design and modeling. This technology not only produces a highly accurate and similar 3D model containing segmentation information of the patient's missing tissues for 3D printing, enabling segmentation of internal limb structures (such as bones, muscles, tendons, fat layers, and skin layers) and reconstruction of missing structures, but also produces a highly realistic 2D unfolded image of the 3D model's surface for 2D printing.

[0059] Through artificial intelligence algorithms, not only can prostheses be designed and produced using the healthy reference side as a data reference when the missing part has a symmetrical healthy reference side, but even if two symmetrical body parts (hands, arms, legs, feet, ears, etc.) are missing, limb restoration can be achieved, and a prosthetic model can be generated when a single part (nose, chin, etc.) is missing. Compared with traditional methods, prostheses can be tailored to individual patients more effectively, making the prosthesis highly similar to the patient's body and improving the quality of the prosthetic product. It can also make the production of prostheses for missing parts no longer restricted, meet the needs of more patients, and improve practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A schematic diagram of a process flow of a fully computer-intelligent high-similarity pseudobody production method provided by an embodiment of the present invention;

[0061] Figure 2a Schematic diagram of the training principle of the deep learning neural network model in an embodiment of the present invention;

[0062] Figure 2b A schematic diagram of the use principle of the deep learning neural network model in an embodiment of the present invention;

[0063] Figure 3 Schematic diagram of the reverse filtering algorithm in an embodiment of the present invention;

[0064] Figure 4 Schematic diagram of the process of manufacturing the surface film of the missing part in an embodiment of the present invention;

[0065] Figure 5 Schematic diagram of the working principle of the uniform pressurization instrument in an embodiment of the present invention;

[0066] Figure 6a This is a schematic diagram of the working process of a fully computerized intelligent high-similarity prosthesis production system according to an embodiment of the present invention when the defective part has a symmetrical healthy reference side;

[0067] Figure 6b This is a schematic diagram of the working process of a fully computer-based intelligent high-similarity prosthesis production system when there is no symmetrical healthy reference side for the missing part according to an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The present invention will be further described in detail below with reference to specific examples, but the embodiments of the present invention are not limited thereto.

[0069] In order to computerize and intelligentize the design and production processes of prostheses in an overall and consistent manner, and to more effectively tailor high-similarity prostheses for individual patients compared to traditional methods, the embodiments of the present invention provide a fully computer-intelligent high-similarity prosthesis production method, system and computer-readable storage medium through the interaction between a main computer and other components and the control of the main computer over the other components.

[0070] The prosthesis in the embodiment of the present invention may include various artificial limbs and artificial limbs used by the human body, and the parts involved in the prosthesis may be hands, arms, legs, breasts, ears, etc.

[0071] In the first aspect, the embodiment of the present invention provides a fully computer-intelligent method for producing a highly similar pseudobody, such as Figure 1 As shown, the method may include the following steps:

[0072] S1, a host computer collects patient information and uses a pre-built artificial intelligence model to obtain a 3D model containing segmentation information of each tissue within the patient's missing part. Based on the collected patient information and the 3D model, a 2D unfolded image of the surface layer of the 3D model is obtained; wherein the tissues include bones, muscles, tendons, fat layers, and skin layers;

[0073] The host computer in the embodiment of the present invention can be implemented by any existing computer, which has functions such as communication, control and data processing.

[0074] In an embodiment of the present invention, the main computer can control some acquisition devices to collect patient information, such as information related to the patient's three-dimensional body structure and information related to the skin surface, etc., and use a pre-constructed (the pre-constructed referred to in the present invention refers to pre-established and trained) artificial intelligence model to obtain a 3D model containing the segmentation information of various tissues in the patient's missing part. This 3D model can characterize the external structure and internal 3D structure data of the patient's missing part while segmenting and distinguishing various internal tissues, such as bones, muscles, tendons, fat layers, skin layers, etc., so as to facilitate the subsequent corresponding printing structure of these different internal tissues, realize the tissue differentiation inside the prosthesis body, and make the printed prosthesis body closer to the patient's actual internal structure.

[0075] At the same time, based on the collected patient information and the 3D model, a 2D unfolded image of the surface of the 3D model can be obtained. The 2D unfolded image is an image that simulates the real skin appearance of the surface of the patient's 3D model area, so that the 2D unfolded image can be printed out later to obtain a skin pattern similar to the patient's skin surface.

[0076] Specifically, many parts of the human body are symmetrically distributed, such as arms, legs, and ears. However, some parts are not symmetrically distributed on the other side, such as the mouth and nose, or both symmetrical sides are missing. Therefore, the embodiments of the present invention mainly consider two situations: the situation where the missing part has no symmetrical healthy reference side and the situation where the missing part has a symmetrical healthy reference side. The specific process of implementing S1 for these two situations is described below.

[0077] (1) In the case where the missing part has no symmetrical healthy reference side, the host computer collects patient information and uses a pre-built artificial intelligence model to obtain a 3D model containing the segmentation information of each tissue in the patient's missing part, and obtains a 2D unfolded image of the surface layer of the 3D model based on the collected patient information and the 3D model, including:

[0078] Step a1, the host computer reconstructs a neural network using a pre-built first model, performs three-dimensional reconstruction of the missing part by collecting information of the remaining parts of the patient, obtains a reconstructed three-dimensional structural model, and obtains a reconstructed surface image of the missing part;

[0079] In the case where the missing part has no symmetrical healthy reference side, for example, there is a symmetrical part but both sides are missing or there is no symmetrical part itself, it is impossible to directly use the data of the healthy reference side as a reconstruction reference for the missing side. Therefore, the embodiment of the present invention pre-constructs a first model reconstruction neural network for identifying and restoring the missing body shape. The first model reconstruction neural network collects three-dimensional information of the patient's remaining parts except the missing part, performs three-dimensional reconstruction of the missing part, and obtains a reconstructed three-dimensional structural model. At the same time, step a1 collects surface information of the patient's remaining parts except the missing part, and obtains a reconstructed surface image of the missing part for the reconstructed three-dimensional structural model, that is, an image of the three-dimensional structural surface of the missing part. For example, if the missing part is a forearm, the reconstructed surface image of the missing part is a surface image corresponding to one circumference of the forearm.

[0080] The first model reconstruction neural network is implemented using generative AI (Artificial Intelligence) technology;

[0081] Specifically, the embodiment of the present invention establishes a human anatomy database (or called a human structure database), which contains human external morphological data related to age, gender, height, weight, skin color, etc., as well as three-dimensional structural information of the human body, specifically including three-dimensional information such as external structure and internal structure information, such as the distribution and position of bones, muscles, tendons, fat layers, skin layers, and the viscoelasticity of various parts. By building a deep learning neural network model, using a large amount of information A and information B obtained from the database to match and train the built deep learning neural network model, the trained deep learning neural network model can output corresponding information B based on the input information A for specific purposes. For the training principle and usage principle of the deep learning neural network model, please refer to Figure 2a and Figure 2b A brief overview of .

[0082] Based on the concept of implementing the deep learning neural network model based on the human anatomy database, the embodiments of the present invention can thereby implement deep learning neural network models for various purposes. It should be noted that the training methods of these deep learning neural network models are implemented based on the training methods of existing neural network models. The focus of the embodiments of the present invention is that, for different purposes, that is, different input information A and output information B, corresponding training sets are constructed based on the corresponding data in the human anatomy database (the training samples in the training set correspond to information A as known samples, which carry label data corresponding to information B), thereby realizing the training of the deep learning neural network model for this purpose. After the training is completed, the process of inputting information A and outputting information B can be realized for this purpose.

[0083] For example, the first model reconstructed neural network is one of the deep learning neural network models. When the first model is used to reconstruct the neural network, the input information A of the first model reconstructed neural network includes three-dimensional information of the patient's remaining parts except the missing parts. These three-dimensional information can be collected using depth information acquisition equipment.

[0084] Among them, the depth information acquisition equipment can be an X-ray machine, CT (Computed Tomography) equipment, MRI (Magnetic Resonance Imaging) equipment, ultrasonic echo imaging equipment, etc. These medical imaging devices can collect depth information of the human body through scanning and other methods. From this depth information, the three-dimensional shape of the human body and the structure, composition and corresponding position information of each internal part can be obtained.

[0085] To generate the missing part of the asymmetric healthy reference side, the embodiment of the present invention divides the entire body shape into different parts, such as the ears, eyes, nose, chin, fingers, palms, forearms, upper arms, breasts, buttocks, upper limbs, lower limbs, soles of the feet, etc. In this case, information B is one of the parts, and information A can be the integral of all the remaining parts. Therefore, the shape and three-dimensional information of the missing body part can be obtained by inputting the three-dimensional information of the remaining body parts into the trained deep learning neural network model (i.e., the first model reconstruction neural network), thereby obtaining a reconstructed three-dimensional structural model of the missing part (including the external shape and internal structure). For example, if a patient's left arm is missing, three-dimensional information of the patient's remaining parts can be collected and input into the trained first model reconstruction neural network. For the input information A, the first model reconstruction neural network will output a reconstructed three-dimensional structural model for the left arm as the corresponding information B. This reconstructed three-dimensional structural model represents the three-dimensional structure of the left arm. Since the first model reconstruction neural network is trained using a large number of patient training samples and label data, it establishes a feature mapping relationship between input and output, which enables the output reconstructed three-dimensional structural model to simulate the actual structure of the missing part of the patient to which the input information A belongs to the greatest extent, and achieve a good match with the remaining healthy parts of the patient, thereby improving the authenticity and structural similarity of the reconstruction.

[0086] The first model reconstruction neural network can use existing CNN, PointNet and other networks. It is understandable that for the three-dimensional reconstruction of the missing part, when the first model reconstruction neural network is trained, the training samples are derived from the three-dimensional information of the remaining healthy parts of a patient in the human anatomy database. These three-dimensional information can be preprocessed to a certain extent, such as dimension unification, filtering, calculation of integrals, etc., to obtain training samples. The label data corresponding to the training sample is the three-dimensional information of the missing part, that is, the three-dimensional structure model. It is understandable that the construction of training sample-label data can be derived from the situation where the patient has a real missing part. For example, if the patient does have a missing left hand, the training sample is the three-dimensional information of the remaining parts except the left hand; the label data is the obtained three-dimensional information of the left hand, that is, the three-dimensional structure model. The constructed model can be three-dimensional information collected and retained before the patient's left hand was lost, or it can be three-dimensional information obtained by measuring the patient's left hand prosthesis, or three-dimensional information of the patient's left hand obtained by other means; the construction of training sample-label data can also be derived from data of healthy human bodies. For example, a large amount of three-dimensional information of various parts of healthy human bodies is stored in the human anatomy database. One of these parts can be regarded as a "missing part", such as the left ear. In this way, training samples are obtained from the three-dimensional information of other parts except the left ear, and the three-dimensional information of the left ear is used as label data. In this way, a large number of data combinations of training sample-label data for different missing parts can be obtained. Therefore, at least through the above-mentioned real loss and artificial loss methods, several data combinations of training sample-label data for different missing parts can be constructed, thereby forming a training set for training the first model to reconstruct the neural network. The embodiment of the present invention can combine human anatomy data of healthy human bodies and human bodies with limb loss to construct the human anatomy database, enrich the types of data samples, and improve the robustness of the model.

[0087] When training the first model to reconstruct the neural network using the training set, a loss function can be set. The network output is compared with the labeled data of the training samples. Backpropagation and other methods are used to adjust the network parameters. This ensures that the loss function converges after multiple iterations of training, meaning that the network output continuously approaches the labeled data. Ultimately, the trained first model reconstructs the neural network. The specific training process can be understood in conjunction with conventional neural network training.

[0088] Among them, the deep learning neural network model of the embodiment of the present invention, such as the pre-built first model reconstruction neural network, can be built in the main computer, or can be built in other devices, such as being set in a processor, and the main computer calls the deep learning neural network model through a program. This is all reasonable and is not limited here.

[0089] As described above, in step a1, the host computer uses the pre-constructed first model to reconstruct the neural network, and collects information about the remaining parts of the patient to perform three-dimensional reconstruction of the missing part, thereby obtaining a reconstructed three-dimensional structural model. Similarly, the pre-constructed first model can also be used to reconstruct the neural network to obtain a reconstructed surface image of the missing part.

[0090] To obtain the reconstructed surface image of the missing part, it is necessary to use the pre-built first model to reconstruct the neural network to obtain the reconstructed three-dimensional structural model. At this time, it is also necessary to use the collected information of the rest of the patient's parts. The collected information of the rest of the patient's parts here refers to the collection of surface images of the rest of the patient's parts, that is, body surface information. This acquisition process can be carried out simultaneously with the acquisition before reconstructing the three-dimensional structural model, or it can be carried out separately.

[0091] Among them, collecting surface images of other parts of the patient can be achieved by using an image acquisition device, which can be a camera (such as a digital camera), a video camera, a 3D scanner, etc., and there is no limitation here.

[0092] To improve the accuracy of the model output and obtain high-quality reconstructed surface images that are highly similar to the patient's body surface, surface images of the remaining parts of the patient can be collected from multiple angles to enrich the data volume. Surface images of the remaining parts of the patient can also include images of cross-sectional areas.

[0093] The collected surface images of the remaining parts of the patient can be preprocessed to a certain extent, which is similar to the three-dimensional reconstruction of the missing parts. The preprocessing here can also be image dimension unification, filtering, calculation of the integral of the surface images of all parts, etc. After preprocessing, in this case, the first model reconstructs part of the input information A corresponding to the neural network. At this time, the input information A should also have the three-dimensional information of the reconstruction of the missing part. After inputting the complete input information A into the first model reconstruction neural network, the reconstructed surface image of the missing part can be obtained, that is, the corresponding output information B is obtained.

[0094] That is to say, in an embodiment of the present invention, the neural network can be reconstructed using the pre-constructed first model, and the three-dimensional reconstruction of the missing part can be performed by collecting three-dimensional information of the remaining parts of the patient to obtain a reconstructed three-dimensional structural model. Then, based on the reconstructed three-dimensional structural model and the collected surface images of the remaining parts of the patient, the neural network can be reconstructed using the first model again to obtain a reconstructed surface image of the missing part.

[0095] In the above process, the first model used twice to reconstruct the neural network can have the same network structure, but with different network parameters. The network parameters are determined and saved through their respective training processes. When using the first model to reconstruct the neural network, a set of adapted network parameters can be called according to different input information A, and used for model recognition to output corresponding information B, thereby achieving the corresponding purpose. The above process can be implemented based on existing AI technology, and the process of calling the adapted network parameters can be implemented using a computer program, which will not be elaborated in detail here.

[0096] In order to obtain the surface image of the reconstruction of the missing part, when the first model reconstructs the neural network, the training samples are derived from the surface images of the remaining healthy parts of a patient in the human anatomy database, and also include the three-dimensional information of the reconstruction of the missing part of the patient. Of course, the training samples can be pre-processed. The label data corresponding to the training sample is the surface image of the reconstruction of the missing part of the patient. Similar to the three-dimensional reconstruction of the missing part, for obtaining the surface image of the reconstruction of the missing part, it can be understood that the construction of the training sample-label data can be derived from the data of the patient with a real missing part, or from the data of a healthy human body. At least through the real missing and artificial missing methods, a data combination of several training sample-label data for different missing parts in this situation is constructed to form a training set for training the first model reconstruction neural network at this time. The specific training process can be understood in combination with the reconstruction of the missing part and the conventional neural network training process, and will not be described in detail here.

[0097] In a simplified way, the surface image reconstructed by the missing part can be obtained by utilizing information of the rest of the patient's body. There may only be a surface image near the cross-sectional position of the missing part. It is also reasonable to obtain the surface image reconstructed by the missing part by extending the image.

[0098] The above gives an example of the use of the first model reconstruction neural network, that is, the three-dimensional reconstruction of the missing part and the reconstruction of the surface image of the missing part are carried out successively. In an optional embodiment, these two links can also be completed in one step. That is, the first model reconstruction neural network training is to use the three-dimensional information of the healthy parts of the patient in the human anatomy database and the surface images of the other healthy parts of the patient to construct samples, and use the three-dimensional information of the missing part and the surface image to construct label data for model training, so that after the training is completed, the three-dimensional information of the remaining parts of a patient except the missing part (obtained by using a depth information acquisition device) and the surface images of the remaining parts of the patient collected are input, and the first model reconstruction neural network will output a three-dimensional structural model of the reconstructed missing part, which carries the reconstructed surface image of the missing part. The embodiment of the present invention can adopt any of the above methods to realize the three-dimensional reconstruction of the missing part and the reconstruction of the surface image of the missing part, which is not limited here. However, it should be emphasized that, under different circumstances and with different combinations of input information A and output information B, the training process using a neural network is similar to the existing neural network training process. This is precisely the embodiment of the functional principle of the neural network. The embodiment of the present invention focuses on constructing a large amount of training samples and label data from the human anatomy database based on the purpose represented by the input information A and the output information B to complete the model training process. However, this process can also refer to the existing neural network training principles and be understood in combination with the scenario purpose of the embodiment of the present invention. A more detailed explanation will not be given here.

[0099] Step a2, using a pre-constructed second structural hierarchical neural network to perform structural segmentation of the internal tissue of the three-dimensional structural model, and using the model attribute related information in the collected patient information and a pre-constructed third model modification neural network to perform model modification, to obtain a 3D model after processing;

[0100] In the existing technology, on the one hand, artificially manufactured prosthesis bodies are often limited to making the appearance of the prosthesis close to the real body when it is at rest. Therefore, the prepared prostheses are all drawn on the surface without internal structure, and usually adopt a seamless structure without distinction of internal tissues. This will make the authenticity of the prosthesis body poor. After the prosthesis is installed on the missing part of the patient, the cross-section of the missing part and the prosthesis are only installed in cross-section contact, and the various tissues in the cross-section, such as bones, cannot be continued in the prosthesis, resulting in poor authenticity, flexibility and usability of the prosthesis. On the other hand, in order to facilitate industrialization, the existing prosthesis body design and manufacturing often use the same prosthesis body for all patients, and configure a limited number of prosthesis bodies as sales products for patients to choose the one that is closer. If customization is to be achieved, it needs to be done manually by highly professional personnel, which cannot meet current needs.

[0101] In response to the above-mentioned problem in the first aspect, an embodiment of the present invention performs structural segmentation of the internal tissues of the three-dimensional structural model reconstructed from the missing part, so that the positions of the various tissues in the three-dimensional structural model, such as bones, muscles, tendons, fat layers, skin layers, etc., can be distinguished. The purpose is to subsequently locate the positions of different internal tissues and select materials for corresponding printing structures, so that the internal tissues of the prosthesis body can be distinguished. This not only makes the printed prosthesis body closer to the patient's actual internal structure, but also can achieve precise alignment with the tissue position corresponding to the cross-section of the missing part, so that the prosthesis can be close to the real human body during movement.

[0102] Specifically, the embodiment of the present invention pre-constructs a second structural hierarchical neural network for structural segmentation of internal tissues. This second structural hierarchical neural network is similar to the previous first model reconstruction neural network, and both are trained using data from the human anatomy database. The selected training samples contain a three-dimensional structural model (i.e., three-dimensional information) of the reconstructed missing part, in which the external morphology and internal structure can be seen, but the bones, muscles and other tissues cannot be distinguished. The selected training samples also contain at least three-dimensional information of the cross-sectional position of the missing part, or further three-dimensional information of the part connected to the missing part. For example, if the patient's left side is missing below the elbow joint, the training sample contains a three-dimensional structural model of the reconstructed missing part below the left elbow joint, and also contains three-dimensional information of the left elbow joint cross-section and even the upper arm part above the cross-section. The label data corresponding to the training sample is the segmentation information of each tissue in the training sample, that is, the 3D information obtained after the internal tissue structure of the 3D structural model reconstructed for the missing part is segmented. This part of the information represents the 3D shape of the internal structure. In the above example, the 3D information of each internal tissue is obtained after manual labeling or other segmentation and labeling of the 3D structural model of the missing part below the left elbow joint. The 3D information of each segmented tissue is obtained by referencing the cross section and even further referencing the 3D information of the remaining part of the upper arm above the elbow joint connected to the cross section. In this way, the shape and position of each segmented tissue in the 3D structural model of the missing part below the left elbow joint match the shape and position of the corresponding tissue in the cross section and the shape and position of the corresponding tissue in the remaining part of the upper arm connected to the cross section. For example, for bones, the segmented 3D information includes 3D information such as the thickness, shape, and direction of the bone. Any bone is aligned with the corresponding bone in the cross section and the corresponding bone in the upper arm above the cross section, so that the direction, shape, and size of the same bone from top to bottom conform to the human anatomy and avoid abnormal mutations and dislocations.

[0103] Similarly, in the process of training the second structure hierarchical neural network, the construction of training sample-label data can be derived from data of patients with real missing parts, or from data of healthy human bodies. At least through real missing parts and artificial missing parts, a data combination of several training sample-label data for different missing parts in this situation is constructed to form a corresponding training set for training the second structure hierarchical neural network at this time. The specific details can be combined with the relevant description of the first model to reconstruct the neural network and the principles and training process of the existing neural network, which will not be explained in detail here.

[0104] It should be noted that the second hierarchical neural network structure of the present invention is applicable to cases where the missing part has no symmetrical healthy reference side, such as a missing nose or both arms, and is also applicable to cases where the missing part has symmetrical healthy reference sides, such as one arm intact and the other missing. In both cases, the training mechanism of the second hierarchical neural network structure is the same, and the model can be completely identical, or the model parameters can be optimized specifically for different situations, which is reasonable.

[0105] Regarding the second aspect of the problem mentioned above, given that the shape of the prosthesis should be adjusted according to the patient's specific situation, in order to accurately restore individual differences, the embodiment of the present invention once again uses an artificial intelligence algorithm, using the model attribute-related information collected from the patient information and a pre-constructed third model modification neural network to perform model modification. In other words, the embodiment of the present invention performs two processes, namely, internal tissue structure segmentation and model modification, on the three-dimensional structural model reconstructed from the missing part, ultimately obtaining a 3D model.

[0106] Similarly, the third model modified neural network of the embodiment of the present invention can be applied to the case where the missing part has no symmetrical healthy reference side and the case where the missing part has a symmetrical healthy reference side.

[0107] The embodiment of the present invention does not limit the order of the two aspects of processing, namely, the internal tissue structure segmentation and model modification. For example, ① the three-dimensional structure model reconstructed from the missing part can be firstly segmented by using the pre-constructed second structure hierarchical neural network, and then the model attribute related information in the collected patient information and the pre-constructed third model modification neural network can be used for model modification, and finally a 3D model can be obtained after processing; or ② the three-dimensional structure model reconstructed from the missing part can be firstly modified by using the model attribute related information in the collected patient information and the pre-constructed third model modification neural network, and then the internal tissue structure segmentation can be obtained after processing, and finally a 3D model can be obtained after processing; or ③ the two aspects of processing can be performed in parallel, and the models processed by the two aspects can be fused and processed to finally obtain a 3D model. These three different participation times of the third model modification neural network are all reasonable. The embodiment of the present invention can adopt any one of the methods, or select the best one from the three methods.

[0108] The model attribute related information includes:

[0109] The patient's age, gender, height, weight and skin color; the model modification includes modifying the prosthesis size, shape, surface color and surface texture. The purpose of model modification is to make the 3D model more consistent with the patient's personal situation.

[0110] Specifically, when using the third model to modify the neural network, the input information A contains the three-dimensional structural model to be processed and information related to the model attributes. The output information B is modification factors for the prosthesis size, shape, surface color, and surface texture, such as modification factors for bone thickness, muscle volume, fat layer thickness, skin color, and texture. The obtained modification factors are used to further modify the three-dimensional structural model to achieve the above-mentioned modification objectives. Embodiments of the present invention can utilize a large amount of data to pre-define modification standards, such as modification factor standards for various aspects of the human body under different attributes such as age and skin color. Modification factors are used to modify external and internal structures. For example, model attribute information may include the patient being a 70-year-old Asian female. Modification factors can be described by numbers or words, such as bone height ML_height_of_bone = 0.82, bone width ML_width_of_bone = 0.95, skin color Mcolor_RGB = 0.9, 1.2, 1.1, etc. The modification factors can then be used in the prosthesis manufacturing process, such as 3D printing and inkjet printing.

[0111] In the process of using the third model to modify the neural network processing, the three-dimensional structure model to be processed is different according to the different timing of the third model modification neural network participation. Accordingly, the training samples and label data of the training process are also different.

[0112] For example, with respect to the participation timing indicated by ① above, the three-dimensional structural model to be processed by the third model modification neural network is a three-dimensional structural model obtained by performing structural segmentation of the internal tissue of the three-dimensional structural model reconstructed by the missing part using the pre-built second structural hierarchical neural network; the third model modification neural network outputs a modification factor, and a 3D model can be obtained by adjusting the three-dimensional structural model to be processed using the modification factor.

[0113] In the above-mentioned case ①, the sample data of the training process includes a large number of three-dimensional structural models obtained based on the human anatomy database and reconstructed from missing parts, which are obtained after the internal tissue structure is segmented. The sample data also includes the model attribute related information in the corresponding patient information; the label data includes the corresponding modification factor; the construction of the training sample-label data can be derived from the data of the patient with real missing parts, or from the data of a healthy human body. At least through the real missing and artificial missing methods, a data combination of several training sample-label data for different missing parts in this situation is constructed to form a training set for training the third model modified neural network at this time. The network selection of the third model modified neural network is not limited. It can be specifically combined with the relevant description of the first model reconstruction neural network and the principles and training process of the existing neural network. It will not be explained in detail here.

[0114] With respect to the participation timing indicated by ② above, the three-dimensional structural model to be processed by the third model modification neural network is a three-dimensional structural model reconstructed from the missing part; the third model modification neural network outputs a modification factor, and after adjusting the three-dimensional structural model to be processed using the modification factor, a three-dimensional structural model after model modification is obtained, and the internal tissue structure of the model is segmented using the pre-constructed second structural hierarchical neural network to obtain the 3D model. In this case, the sample data and label data of the training process can be understood in accordance with the model usage process, and can be combined with the above ① situation and the relevant description of the first model reconstruction neural network and other networks, the principles of existing neural networks, and the understanding of the training process, which will not be explained in detail here.

[0115] For the participation opportunity indicated by ③ above, the three-dimensional structural model to be processed by the third model modification neural network is the three-dimensional structural model reconstructed from the missing part; the third model modification neural network outputs a modification factor, and after adjusting the three-dimensional structural model to be processed using the modification factor, a three-dimensional structural model after model modification is obtained; the three-dimensional structural model after model modification is combined with the three-dimensional structural model reconstructed from the missing part using the pre-built second structural hierarchical neural network to perform structural segmentation of the internal tissue to obtain the three-dimensional structural model, and the 3D model is obtained through model fusion and other processing methods. The specific method of the fusion processing of the two models can be implemented based on existing technology. In this case, the sample data and label data of the training process can be understood according to the model usage process, and can be combined with the above ① situation and the relevant description of the first model reconstruction neural network and other networks, the principles of existing neural networks and the understanding of the training process, which will not be explained in detail here.

[0116] Step a3: performing two-dimensional image expansion on the surface image reconstructed from the missing part according to the 3D model to obtain a 2D expanded image.

[0117] As previously mentioned, the surface image reconstructed from the missing part is a surface image of the missing part's three-dimensional structure. Therefore, it represents a three-dimensional surface image. This three-dimensional surface image can be directly unfolded two-dimensionally using existing technologies, such as Adobe Illustrator or common software like 3D printers. This is similar to unfolding a scroll, resulting in a two-dimensional image. After certain processing, a 2D unfolded image is obtained. This 2D unfolded image is then used to print with a 2D printer.

[0118] Furthermore, to produce a more realistic 2D expanded image for printing, embodiments of the present invention employ a reverse filtering algorithm for both cases where the missing part lacks a symmetrical healthy reference side and cases where the missing part has a symmetrical healthy reference side. This section is explained below for clarity.

[0119] (2) In the case where the missing part has a symmetrical healthy reference side, the host computer collects patient information and uses a pre-built artificial intelligence model to obtain a 3D model containing segmentation information of each tissue in the patient's missing part, and based on the collected patient information and the 3D model, obtains a 2D unfolded image of the surface layer of the 3D model, including:

[0120] Step b1, the host computer uses a depth information acquisition device to acquire depth information of the healthy reference side and the remaining portion of the missing part, and uses an image acquisition device to acquire multiple surface images of the healthy reference side at different angles;

[0121] Specifically, the host computer controls the depth information acquisition device to acquire depth information of the patient's healthy reference side and the remaining portion of the missing part, ie, three-dimensional information.

[0122] Because the 3D structure of the missing part is reconstructed using a symmetrical healthy reference side, depth information from the healthy reference side is required as a reference. Similar to the case of a missing part without a symmetrical healthy reference side, 3D information is also required for at least the cross-sectional location of the missing part, and further 3D information (i.e., depth information) of the area connected to the missing part. This allows the structure of the boundary area between the remaining and missing parts of the patient's body to be determined, which is used to design the wear portion of the prosthesis.

[0123] In step b1, the host computer controls an image acquisition device to acquire multiple surface images of the healthy reference side at different angles. The image acquisition device is as described above.

[0124] Step b2, using the collected depth information to perform three-dimensional reconstruction of the missing part to obtain a reconstructed three-dimensional structure model;

[0125] For example, if a patient's left arm is missing below the elbow joint, while the right arm is intact, the right arm is used to reconstruct a three-dimensional structural model of the missing part of the left arm below the elbow joint.

[0126] Then the complete three-dimensional information of the right arm can be collected, because this part of the information reflects the complete three-dimensional shape structure of the right arm. At least this part of the three-dimensional information can be used to reconstruct the left arm in three dimensions, and a reconstructed three-dimensional structural model of the left arm can be obtained.

[0127] In the above three-dimensional reconstruction process, since there is a symmetrical reference side, based on the principle of symmetry and combined with human anatomy, we can use existing algorithms and models to construct a three-dimensional structural model of the reconstructed left arm. This reconstructed three-dimensional structural model is symmetrical with the right side in terms of appearance and structure.

[0128] Step b3, using a pre-constructed second structural hierarchical neural network to perform structural segmentation of the internal tissue of the three-dimensional structural model, and using the model attribute related information in the collected patient information and a pre-constructed third model modification neural network to perform model modification, to obtain a 3D model after processing;

[0129] In step b3, the three-dimensional structural model reconstructed on the missing side and the three-dimensional information of at least the cross section of the missing part are further processed to obtain the input information A of the second structural hierarchical neural network at that location, and the output information B is the three-dimensional structural model after the internal tissue structure segmentation of the input three-dimensional structural model.

[0130] The processing process of step b3 is similar to that of step a2. For the specific process, please refer to step a2 for understanding.

[0131] It should be noted that for the case where the missing part has no symmetrical healthy reference side and the case where the missing part has a symmetrical healthy reference side, the second structure hierarchical neural network can have the same network structure but different network parameters. The network parameters are determined and saved through the training processes for the two cases respectively. For these two different cases, when using the second structure hierarchical neural network, the network parameters of the corresponding case can be called to output the corresponding information B for the input information A of the case. The process of calling and adapting the network parameters can be implemented using a computer program.

[0132] Similar to the second structure hierarchical neural network, the third model modified neural network can also be the same network structure but with different network parameters for the case where the missing part has an asymmetric healthy reference side and the case where the missing part has a symmetric healthy reference side, and can be called accordingly when used.

[0133] Step b4: performing two-dimensional image expansion on the plurality of collected surface images according to the 3D model to obtain a 2D expanded image.

[0134] In the case where the missing part has a symmetrical healthy reference side, the multiple surface images collected can be used to obtain a surface image of the three-dimensional structure of the missing part based on the 3D model and the principle of symmetry using existing methods. This three-dimensional surface image is then directly two-dimensionally unfolded using the same two-dimensional image unfolding method as step a3 to obtain a two-dimensional image. After certain processing, a 2D unfolded image is obtained. The 2D unfolded image is used to print the image using a 2D printing device. The 2D printing device can be a 2D printer, for example.

[0135] One of the features of the present application is that it provides an automatic surface printing technology. In this technology, the surface image of the missing part will be printed onto the surface membrane of the manufactured missing part using a 2D printing device to form a skin, and the surface membrane of the missing part is the simulated skin membrane. Due to the difference in color filters between the camera and other equipment that obtains the surface image and the 2D printing equipment, it is inevitable that the printed color, texture and other aspects will be slightly different from the real one. In order to output a more realistic 2D digital image for printing, the embodiment of the present invention jointly designs a reverse filtering algorithm for the case where the missing part has no symmetrical healthy reference side and the case where the missing part has a symmetrical healthy reference side. The color difference problem between the printed image and the real object is solved by applying the reverse filtering algorithm to the digital image before printing. The main implementation principle of the algorithm is briefly described below.

[0136] 1. Use a digital camera to capture an image of the test sample and obtain a test image, which can be expressed as:

[0137] I test =i test *f camera ;

[0138] The test sample herein generally refers to a physical object, which may be a human body. It is understood that the test image is a digital image.

[0139] In the embodiment of the present invention, i(x,y) is used to represent the visual image, I(x,y) is used to represent the digital image, (x,y) refers to the coordinates of the pixel; f(x,y) is used to represent the filter function; in the above formula, I test represents the test image; i test Represents the visual image of the test sample; f camera represents the filter function of a digital camera;

[0140] 2. Test image I test Printing with a 2D printing device yields the corresponding printed image, which can be expressed as follows:

[0141] i test,print =I test *f printer =i test *f camera *f printer ;

[0142] Among them, i test,print Represents the test image I test Printed image obtained by printing with 2D printing equipment; f printer Represents the filter function of the 2D printing device.

[0143] 3. The test image and its printed image are taken simultaneously using the same digital camera, and the following relationship can be obtained:

[0144] I test =i test *f camera ;

[0145] I test,print =i test,print *f camera =i test *f printer *f camera 2

[0146] Among them, I test,print It is a digital image obtained by photographing the printed image of the test image with a digital camera;

[0147] 4. Using the digital images obtained by taking the test image and its printed image with a digital camera, calculate the correction coefficient, which is expressed as:

[0148]

[0149] in, represents a two-dimensional deconvolution operation, and * represents a two-dimensional convolution operation.

[0150] It can be understood that the test image and its printed image are both digital images obtained after being photographed with a digital camera. The correction coefficient is calculated by calculating the ratio of the two. α(x, y) represents the correction coefficient corresponding to the pixel (x, y).

[0151] 5. Use the same digital camera to capture the target human body part image, expressed as:

[0152] I body =i body *f camera ;

[0153] Among them, I body Represents the target human body part image, which is also a digital image; i body Indicates the corresponding visual image.

[0154] 6. Use the correction coefficient to transform the target human body part image I body After correction, the corresponding printed image is obtained by printing with a 2D printing device, which is expressed as:

[0155] i print,out =I body *α(x,y)*f printer ;

[0156] Among them, i print,out For the target human body part image, the final printed image is i print,out The compensation by the correction factor is equal to i body .

[0157] It can be seen that during the digital image acquisition and printing process, deviations between the printout and the true original image are inevitable. However, the embodiments of the present invention measure the correction coefficients in the digital image and then multiply the correction coefficients in the computer before printing. The printout then becomes a true image that has pre-compensated for these deviations.

[0158] Based on the above main implementation principles, the reverse filtering algorithm is described in detail.

[0159] In the embodiment of the present invention, the process of obtaining the 2D unfolded image is to use a preset reverse filtering algorithm to compare the digital image of the surface layer of the 3D model with the printed image obtained by using a 2D printing device, and compensate for the real visual difference between the digital image and the printed image to obtain the 2D unfolded image to be printed;

[0160] The digital image includes: when the missing part has no symmetrical healthy reference side, the image is obtained by two-dimensionally expanding the surface image reconstructed from the missing part according to the 3D model; or when the missing part has a symmetrical healthy reference side, the image is obtained by two-dimensionally expanding multiple collected surface images according to the 3D model.

[0161] For details, see Figure 3 In the embodiment of the present invention, the process of the preset reverse filtering algorithm includes:

[0162] Step c1, reading initial parameters;

[0163] Wherein, the initial parameters include image matrix size, similarity threshold and maximum number of iterations;

[0164] The image matrix size refers to the image matrix size of the digital image and the printed image, which is represented by IM; the similarity threshold is represented by ST; the number of iterations is represented by a positive integer i, and in the first iteration, i=1; the maximum number of iterations is represented by IL, and IL≥2.

[0165] Step c2, acquiring the digital image;

[0166] As mentioned above, in the case where the missing part has no symmetrical healthy reference side, the digital image is an image obtained by performing a two-dimensional image expansion on the surface image reconstructed from the missing part according to the 3D model;

[0167] In the case where the missing part has a symmetrical healthy reference side, the digital image is an image obtained by performing two-dimensional image expansion on the multiple surface images collected according to the 3D model.

[0168] For ease of understanding, Figure 3 In the figure, the digital image is represented by (aa1).

[0169] Step c3, printing the digital image using a 2D printing device to obtain an original printed image;

[0170] For ease of understanding, Figure 3 In FIG, the original printed image is represented by (AA1).

[0171] Step c4, using the same image acquisition device used in the process of obtaining the digital image, performing image acquisition on the digital image and the original printed image to obtain corresponding acquired images respectively;

[0172] The image acquisition device can be a digital camera, and the front and back images are the same.

[0173] For ease of understanding, Figure 3In the embodiment, the digital image (aa1) and the original printed image (AA1) are captured to obtain corresponding captured images (aa2) and (b), respectively.

[0174] Step c5, calculating an inverse filtering function using the captured images corresponding to the current digital image and the original printed image;

[0175] That is, use (aa2) and (b) to calculate the reverse filtering function, which can be expressed as:

[0176]

[0177] in, Represents a two-dimensional deconvolution operation, Figure 3 In the relevant description of the part, * represents a two-dimensional convolution operation, and (c) represents the inverse filtering function.

[0178] Combined with the introduction of the main implementation principle of the reverse filtering algorithm in the previous article, the role of the reverse filtering function calculated in step c5 is equivalent to the correction coefficient in the previous article.

[0179] Step c6, applying the reverse filtering function to the current digital image to obtain a reverse filtered digital image;

[0180] Specifically, the reverse filtering function (c) is applied to the current digital image (aa1) to obtain the reverse filtered digital image (d1), which is represented by:

[0181] (aa1)*(c)=(d1);

[0182] Step c7, determining whether the current number of iterations is less than the maximum number of iterations;

[0183] Specifically, determine whether the current number of iterations i is less than the maximum number of iterations IL, if so, execute step c8, if not, execute step c14;

[0184] Step c8, printing the currently obtained reverse filtered digital image using a 2D printing device to obtain a first printed image;

[0185] See also Figure 3 , the currently obtained reverse filtered digital image (d1) is printed using a 2D printing device to obtain a first printed image, denoted as (D1).

[0186] Step c9, using the same image acquisition device, performing image acquisition on the currently obtained reverse filtered digital image and the first printed image to obtain corresponding acquired images;

[0187] Figure 3In the embodiment, the currently obtained reverse filtered digital image and the first printed image are subjected to image acquisition to obtain corresponding acquired images (aa3) and (d2), respectively.

[0188] Step c10, calculating a similarity index for the acquired images corresponding to the currently obtained reverse filtered digital image and the first printed image;

[0189] Specifically, a similarity index SI is calculated for (aa3) and (d2). The similarity index SI can be implemented using any existing method for calculating image similarity, such as PSNR (Peak Signal-to-Noise Ratio), SSIM (structural similarity), MSE (Mean Squared Error), cosine similarity, histogram, mutual information, hash similarity, etc., without limitation. For example, the similarity index SI can be a two-dimensional correlation coefficient.

[0190] Step c11, determining whether the similarity index is greater than the similarity threshold;

[0191] Specifically, determine whether the similarity index SI is greater than the similarity threshold ST; if so, execute step c12; if not, execute step c13;

[0192] Step c12, determining the currently obtained reverse filtered digital image as the 2D expanded image to be printed;

[0193] Specifically, determining the currently obtained reverse filtered digital image (d1) as the 2D expanded image to be printed means that the currently obtained first printed image (D1) meets the requirements and (D1) can be used as the final output.

[0194] Step c13: storing the currently obtained reverse-filtered digital image and the similarity index, replacing the current digital image with the currently obtained reverse-filtered digital image, replacing the captured image corresponding to the current digital image with the captured image corresponding to the currently obtained reverse-filtered digital image, and replacing the captured image corresponding to the current original print image with the captured image corresponding to the current first print image, and then returning to step c5 for a new iteration;

[0195] Specifically, store the currently obtained reverse filtered digital image (d1), similarity index SI and current iteration number i; then make the following changes: (aa1) = (d1); (aa2) = (aa3); (b) = (d2); i = i + 1; then return to step c5 for a new iteration.

[0196] Step c14 , searching for the reverse filtered digital image with the largest similarity index in all iterations, and using it as the 2D expanded image to be printed.

[0197] After step c12 and step c14, the 2D expanded image to be printed can be printed using a 2D printing device. It can be understood that the obtained printed image is the corresponding (D1).

[0198] The reverse filtering algorithm designed by the embodiment of the present invention can eliminate the color difference between the printed image and the real object, making the printed image more realistic.

[0199] S2, the host computer controls the 3D printing device to print the 3D model to obtain the prosthesis body;

[0200] The 3D printing device may be a 3D printer or the like.

[0201] As mentioned above, the 3D model contains three-dimensional information of the missing part, and the internal tissues such as bones, muscles, tendons, fat layers, skin layers, etc. have been segmented. Therefore, the three-dimensional information of each tissue, such as shape, structure, size, and position, is known, and 3D printing can be achieved using 3D printing equipment; each part can be printed separately, and the main computer controls the assembly equipment to assemble the prosthesis body, that is, the prosthesis body contains the printing results of each layered part of the missing part.

[0202] In an optional implementation, S2 may include:

[0203] The host computer controls the 3D printing device to print with materials that match the tissues in the 3D model to obtain a prosthesis body; wherein the materials that match the tissues include a variety of optional materials.

[0204] Taking into account the use of the prosthesis body, the materials of each tissue should be carefully selected. The embodiment of the present invention can refer to the general (mechanical) parameter values ​​of the human body provided in the manual and literature in advance, such as the physical parameter values ​​of a large number of human tissues (hardness and viscoelasticity, etc.), and pre-set a variety of optional materials for each tissue. These materials must also be able to be reused for a long time and have biocompatibility. When printing a 3D model, for each tissue, a material is selected from the multiple optional materials for each tissue to print, for example, one can be randomly selected. When all tissues are printed, the printed prosthesis body is obtained.

[0205] Furthermore, the material of each tissue can be selected in a targeted manner in combination with the patient's personal information. Specifically, for each tissue, the matching material is determined to be suitable from a variety of optional materials for the tissue based on the collected patient information, so that it has the hardness and viscoelasticity that are most similar to the current patient's real body. In this case, the physical parameter values ​​such as hardness and viscoelasticity of each tissue of the patient can be collected in any step before printing the 3D model. Then, for each tissue, a suitable material can be selected from a variety of optional materials for each tissue using the collected patient's physical parameter values ​​to print. For example, bones, muscles, fat layers and other parts can be printed with plastic materials, but different tissues can select different plastic materials based on the patient's physical parameter values. Moreover, bones, muscles, tendons, and fat layers are subcutaneous parts, and their size can be adjusted according to the thickness of the skin.

[0206] S3, the main computer controls the film-making equipment to make a surface film at the missing part;

[0207] The surface membrane of the missing part is a simulated skin membrane, that is, a membrane that simulates real human skin. The mechanical properties of the membrane (such as hardness and viscoelasticity) must match those of the remaining part of the patient's limb and be biocompatible. Silicone rubber is one of the candidate materials.

[0208] In an optional implementation, see Figure 4 As shown in the schematic diagram of the process for manufacturing the surface membrane of the missing part, S3 may include the following steps:

[0209] S31, the main computer controls the film-forming device to obtain a sol-state silicone rubber solution, and places it on a printing paper with a preset thickness and a preset surface roughness to form a silicone rubber solution layer with a preset thickness;

[0210] The film-making equipment can be implemented based on existing process equipment and has a platform for placing printing paper. The printing paper has a preset thickness and a preset surface roughness. The preset thickness can be a thickness value determined based on the thickness of a large number of human skins, and the preset surface roughness can also be a roughness value determined based on the roughness of a large number of human skins, so as to be suitable for most human bodies.

[0211] In an alternative embodiment, to customize the prosthesis for the patient currently requiring it, the preset surface roughness is determined based on the roughness of the patient's skin surface as collected from the patient information, and the preset thickness is determined based on the thickness of the patient's skin as collected from the patient information. In this case, the patient's skin surface roughness and skin thickness need to be collected before S3 to serve as the preset surface roughness and preset thickness, respectively.

[0212] After the film-making equipment is controlled to obtain the sol-state silicone rubber solution, it is placed on the above-mentioned printing paper, covering the surface of the printing paper with a uniform thickness to form a silicone rubber solution layer of a set thickness. Figure 4 In the lower part of the leftmost image, the burrs on the printed paper indicate a rough surface, and the blue part is the formed silicone rubber solution layer.

[0213] S32, the main computer controls the film-making equipment to cover the upper surface of the silicone rubber solution layer with a substrate sheet, and after the silicone rubber solution layer forms a gel, peels off the silicone rubber solution layer to obtain a surface film of the missing part, and the original contact surface between the surface film of the missing part and the printing paper serves as the printing surface; wherein, the bonding strength of the surface of the substrate sheet to the silicone rubber is higher than the bonding strength of the surface of the printing paper to the silicone rubber, and the printing paper is suitable for the 2D printing equipment.

[0214] Figure 4 The bottom paper liner is the backing sheet. The surface of the backing sheet has a higher bonding strength to the silicone rubber than the surface of the printing paper, which makes it easier to peel off and remove the printing paper.

[0215] After the upper surface of the silicone rubber solution layer is covered with a substrate sheet, a Figure 4 The structure of the middle part. After the silicone rubber solution layer turns into gel, the silicone rubber solution layer is peeled off from the surface of the printing paper and turned over to obtain Figure 4 The structure in the right image shows the surface membrane of the missing portion. The surface of the missing portion, which originally contacted the printed paper, serves as the printing surface. This process produces a silicone rubber membrane with the same surface roughness as the printed paper. Subsequently, the missing portion can be patterned and colored using standard 2D printing equipment, forming the cover.

[0216] S4, the host computer controls the 2D printing device to print the 2D expanded image onto the surface film of the missing part to form a skin;

[0217] In an optional embodiment, before executing S4, the method of the present invention further includes:

[0218] The main computer controls the printing system to treat the surface of the surface film of the missing part with a surface treatment liquid, and evenly prints the pigment ink after the treatment.

[0219] The printing system may include 2D printing equipment and 3D printing equipment, and may also include some other equipment. It can be implemented based on a combination of some existing equipment and can achieve the functions described in this application. It will not be described in detail here.

[0220] Treating the surface of the surface film at the missing portion with a surface treatment liquid is a physical / chemical treatment of the surface of the surface film at the missing portion to increase its affinity with the ink. The surface treatment liquid can be any existing liquid used for surface treatment and is not limited here.

[0221] After treatment, the printing system will evenly print pigment ink on the surface of the surface film of the missing part after the surface treatment liquid is treated, so that the 2D expanded image can be printed on the surface film of the missing part with pigment ink in the subsequent S4 to form a skin with a pattern.

[0222] When S4 is specifically executed, the 2D printing device can be an ordinary inkjet printer. It can be seen that the above process of the embodiment of the present invention makes it possible to use an ordinary inkjet printer to print on the silicone film, which greatly reduces the production cost.

[0223] Furthermore, due to the low ink permeability of silicone rubber, pigment inks are more suitable for general printing than dye inks. After S4 printing, a protective ink layer is applied to the printed surface, ensuring that the texture is consistent with real human skin. This is the final coloring process, aimed at achieving a similarity to the real body.

[0224] Silicone rubber is widely used as a prosthetic material due to its physical, chemical and biological inertness, and its stability in daily life environments. However, due to its inertness, it is not easy for ordinary inkjet printers or laser printers to print directly on the surface of silicone rubber. The embodiment of the present invention proposes a simple and practical technology to achieve high-quality silicone rubber film printing. By treating the surface of the silicone rubber film with a surface treatment liquid and evenly applying pigment ink (primer coating), a 2D unfolded image of the missing part of the patient's body can be printed on it, which can simplify the process and reduce process costs.

[0225] S5, the main computer controls the assembly equipment to attach the skin to the surface of the prosthesis body, and performs surface treatment to obtain a completed prosthesis.

[0226] As mentioned above, the various parts of the prosthesis body have been assembled. At this time, the skin is directly attached to the surface of the prosthesis body to obtain a completed prosthesis, that is, a finished prosthesis.

[0227] In an optional embodiment, the main computer controls the assembly equipment to attach the skin to the surface of the prosthesis body, which may include:

[0228] The main computer controls the printing system to apply glue and lubricant to the surface of the prosthesis body, covers the coated surface of the prosthesis body with the skin, and controls the uniform pressurizing instrument to perform pressurization so that the skin and the prosthesis body are tightly fitted to form an integral structure.

[0229] In the embodiment of the present invention, the skin is bonded to the prosthesis body by using an assembly device, and the assembly device may include a robotic arm, etc.

[0230] To simulate the adhesion of real human skin, the glue's density and adhesion are matched to the collected patient data. Specifically, the glue has the same viscoelasticity as the surface membrane of the defective area and the patient's body. The glue can be a resin glue, and the density and adhesion of the resin glue can be pre-adjusted to match the patient's body data.

[0231] The lubricant is a biocompatible lubricant, such as inert oil, to achieve the natural lubrication effect of the human body.

[0232] In order to achieve a high degree of restoration of the human skin surface, the present invention has designed a uniform pressure device for applying uniform pressure to each part of the prosthesis body wrapped with the skin, so as to ensure a firm fit between the skin and the prosthesis body and avoid internal gaps and surface wrinkles. Figure 5 understand.

[0233] Figure 5 In the description, Air flow refers to air flow; Air-tight container refers to airtight container; Flexible membrane refers to flexible membrane, i.e., the skin in the embodiment of the present invention; Air-through plate refers to air-through plate; Air / Water compressor refers to air / water compressor;

[0234] The working process of the uniform pressurization instrument may include:

[0235] 1. Place the solid model on a breathable plate and prepare a flexible film above the model. They are placed in a container with an inlet and outlet for air (or water) flow. Apply a layer of adhesive to the surface of the solid model or under the flexible film.

[0236] 2. By blowing air or flowing water at high pressure from the inlet to change the internal air pressure, the flexible membrane is pressed against the surface of the solid model.

[0237] 3. Since pressure is evenly applied to the film in this structure, the film can be evenly and tightly adhered to the surface of the solid model.

[0238] The solid model refers to the prosthesis body, and the flexible membrane refers to the skin.

[0239] After the skin is attached to the surface of the prosthesis body, surface treatment is required to obtain the completed prosthesis.

[0240] Wherein, the surface treatment herein includes:

[0241] The main computer controls the inkjet printing system to coat the surface of the prosthesis body after the skin is attached with a protective layer similar to the surface of human skin.

[0242] The inkjet printing system can be implemented based on existing equipment or a combination thereof, as long as it can achieve the above functions, and there is no limitation here.

[0243] The protective layer, which resembles the surface of human skin, can be pre-determined based on information about the patient's skin surface. After application, the outermost layer of the prosthesis has the same texture, diffuse reflectance, and water / oil affinity as real patient skin, while also being durable enough for daily use. The protective layer can be made of any material and can include silicone-based resins, for example.

[0244] It can be understood that in the embodiment of the present invention, the main computer controls the operation of the remaining components, performs data flow monitoring and control, and realizes the automation of the following process: construction of a 3D model containing the segmentation information of each tissue in the patient's missing part → 2D unfolded image of the surface of the 3D model → printing the 3D model to obtain the prosthesis body → manufacturing the surface membrane of the missing part → printing the 2D unfolded image onto the surface membrane of the missing part to form a skin → attaching the skin to the surface of the prosthesis body, and obtaining the prepared prosthesis after surface treatment.

[0245] During this process, a robotic arm controlled by a main computer can be used to move and manipulate necessary equipment, tools, and various parts of the prosthesis. The remaining components controlled by the main computer include, but are not limited to, the robotic arm, as well as the aforementioned printing systems, including 3D printing equipment and 2D printing equipment, film-making equipment, assembly equipment, depth information acquisition equipment, image acquisition equipment, uniform pressurization equipment, and inkjet printing systems. The main computer can also control the data flow transmission of RGBD images, 3D internal structure point datasets, data required for artificial intelligence networks (such as training data), data required for 3D printing (OBJ, STL, and other engineering files), and data required for robotic arm control, assembly, and surface treatment.

[0246] The fully computer-intelligent high-similarity prosthesis production method provided by the embodiment of the present invention computerizes and intelligentizes all the above processes as a whole, can provide high-precision, high-similarity prostheses in a shorter time, and realizes the digitization of the production process through computer operation, no longer requiring reliance on manual experience or skills, which can greatly reduce working time and costs.

[0247] Furthermore, the embodiments of the present invention incorporate artificial intelligence technology and combine it with patient information for prosthesis design and modeling. This technology not only produces a highly accurate and similar 3D model containing segmentation information of the patient's missing tissues for 3D printing, enabling segmentation of internal limb structures (such as bones, muscles, tendons, fat layers, and skin layers) and reconstruction of missing structures, but also produces a highly realistic 2D unfolded image of the 3D model's surface for 2D printing.

[0248] Through artificial intelligence algorithms, not only can prosthesis design and production be completed using the healthy reference side as a data reference when there is a symmetrical healthy reference side for the missing part, but also, even if two symmetrical body parts (hands, arms, legs, feet, ears, etc.) are missing, limb restoration can be achieved, and a prosthetic model can be generated when a single part (nose, chin, etc.) is missing. Compared with traditional methods, prostheses can be tailored to individual patients more effectively, making the prosthesis highly similar to the patient's body and improving the quality of the prosthetic product. It can make the production of prostheses for missing parts no longer restricted, and can meet the needs of more patients, thereby improving the practicality of this method.

[0249] In a second aspect, corresponding to the above method embodiment, an embodiment of the present invention further provides a fully computer-intelligent high-similarity propagation system, comprising:

[0250] A main computer and other components; the main computer controls the other components to complete the steps of the fully computer-intelligent high-similarity pseudobody production method described in the first aspect; the other components include an image acquisition and storage system, film-making equipment, assembly equipment, a printing system, a uniform pressurizing instrument, and a printing system; the image acquisition and storage system includes a depth information acquisition device and an image acquisition device; the printing system includes at least a 3D printing device and a 2D printing device.

[0251] In combination with the above, it can be understood that the embodiments of the present invention can complete the production of prostheses for the missing part in the case where the missing part has a symmetrical healthy reference side and in the case where the missing part has no symmetrical healthy reference side. In order to more intuitively understand the working process of the fully computerized intelligent high-similarity prosthesis production system in these two cases, the following is combined with the content of the first aspect above to Figure 6a and Figure 6b For example, a brief description is given. Figure 6a and Figure 6b The entire system for the digital production of prostheses (hands, arms, legs, breasts, ears, etc.) is shown. Figure 6a For cases where there is a symmetrical healthy reference side for the missing part, that is, when one side of the symmetrical body part is preserved; Figure 6bThis is the case when the missing part has no symmetrical healthy reference side, that is, the corresponding situation when both sides are missing or there is no symmetrical part.

[0252] against Figure 6a and Figure 6b The computer in the upper diagram represents the main computer. In the workflow below, each step involves other components, which are not shown for simplicity. Throughout the system's operation, the main computer controls the other components and coordinates the corresponding data flows.

[0253] against Figure 6a and Figure 6b The steps after digital 3D modeling are the same. The following is a brief description of each step.

[0254] Digital 3D modeling: This process generates a 3D model containing segmented information about the patient's missing tissues, which is then fed to a 3D printer for subsequent 3D skeletal printing. Furthermore, it generates a 2D unfolded image of the 3D model's surface, which is then fed to a 2D printer for subsequent 2D surface printing. For details on this process, please refer to S1 above.

[0255] Among them, for Figure 6a , digital 3D modeling is completed using depth images and surface images. The depth image here refers to the depth information collected from the patient's healthy reference side and the remaining part of the missing part; the surface image refers to the collection of multiple surface images of the healthy reference side at different angles. The collected depth information is then used to perform three-dimensional reconstruction of the missing part to obtain a reconstructed three-dimensional structural model;

[0256] against Figure 6b , digital 3D modeling is completed using generative AI. Here, generative AI mainly refers to using the pre-built first model to reconstruct the neural network, and by collecting information from the rest of the patient's body parts to perform three-dimensional reconstruction of the missing part, obtain a reconstructed three-dimensional structural model, and obtain a reconstructed surface image of the missing part;

[0257] In the traditional prosthesis production process, designers will refer to the remaining body parts to create symmetrical parts, such as arms, legs, hands, feet, breasts and ears. However, a completely accurate symmetrical appearance does not look natural. Therefore, designers will make slight asymmetrical modifications to the appearance to make it more natural. Such modifications require the craftsman's superb skills and long-term experience. In the embodiment of the present invention, such modifications can be made using the principles of deep learning. In the data management of the digital system, the present invention uses a large human anatomy database to train a neural network system. The network can output a natural-looking prosthesis based on the asymmetry of the body parts. In addition, the network of the present invention has a significant advantage. Using the principles of generative artificial intelligence, the network can generate missing body parts without referring to the remaining parts. Therefore, prostheses without symmetrical structures, such as noses and chins, can be generated. In addition, prostheses with both sides missing, such as missing legs, can also be generated.

[0258] Then, for both cases, the internal tissue structure segmentation of the three-dimensional model of the reconstructed missing part is performed using the pre-constructed second structure hierarchical neural network, and the model is modified using the model attribute related information in the collected patient information and the pre-constructed third model modification neural network to obtain a 3D model after processing; and, the corresponding surface image of the missing part in each case is two-dimensionally expanded to obtain a 2D expanded image.

[0259] For specific details, please refer to the relevant description above and will not be explained in detail here.

[0260] 3D skeleton structure printing: Please refer to the previous section S2 for this part, in which the host computer controls the 3D printing device to print the 3D model to obtain the prosthesis body.

[0261] 2D surface printing: Please refer to Sections S3 and S4 above. First, the host computer controls the film-making device to produce a surface film of the missing part. Then, the host computer controls the 2D printing device to print the 2D expanded image onto the surface film of the missing part to form a skin.

[0262] Overall structural combination and surface treatment: For this part, please refer to the previous section S5. The main computer controls the assembly equipment to attach the skin to the surface of the prosthesis body, and performs surface treatment to obtain the completed prosthesis.

[0263] For the specific processing procedures of each module of the system, please refer to the relevant content of the first aspect and will not be elaborated here.

[0264] The fully computerized, intelligent, high-similarity prosthesis production system provided by the embodiments of the present invention computerizes and intelligentizes the entire prosthesis production process, enabling the production of high-precision, high-similarity prostheses in a shorter time. Furthermore, the production process is digitized through computer operation, eliminating the need for manual experience or skills, significantly reducing work time and costs. Furthermore, the system incorporates artificial intelligence technology and combines patient information for prosthesis design and modeling. This system not only enables prosthesis design and production when a symmetrical healthy reference side is available for the missing part, but also enables limb restoration even when two symmetrical body parts (hands, arms, legs, feet, ears, etc.) are missing, and can generate prosthetic models when a single part (nose, chin, etc.) is missing. Compared to traditional methods, prostheses can be more effectively tailored to individual patients, ensuring a high degree of similarity between the prosthesis and the patient's body, thereby improving the quality of prosthetic products. This system can eliminate the limitations of prosthesis production for missing parts, meeting the needs of more patients and improving practicality.

[0265] In the third aspect, corresponding to the fully computer-intelligent high-similarity pseudo-body production method provided in the first aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of any fully computer-intelligent high-similarity pseudo-body production method provided in the first aspect of the embodiment of the present invention.

[0266] For specific details, please refer to the fully computer-intelligent high-similarity pseudobody production method provided in the first aspect, which will not be described in detail here.

[0267] It should be noted that, in the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0268] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification.

[0269] Each embodiment in this specification is described in a related manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system and storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For related portions, refer to the descriptions of the method embodiments.

[0270] The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A fully computerized intelligent method for producing a highly similar pseudobody, characterized in that: include: The host computer collects patient information and uses a pre-built artificial intelligence model to obtain a 3D model containing segmentation information of each tissue within the patient's missing part. Based on the collected patient information and the 3D model, a 2D unfolded image of the surface layer of the 3D model is obtained; wherein the tissues include bones, muscles, tendons, fat layers, and skin layers; The host computer controls the 3D printing device to print the 3D model to obtain the prosthesis body; The main computer controls the film-making equipment to make a surface film at the missing part; The host computer controls the 2D printing device to print the 2D expanded image onto the surface film of the missing part to form a skin; The main computer controls the assembly equipment to attach the skin to the surface of the prosthesis body and perform surface treatment to obtain a completed prosthesis; The main computer controls the film-making equipment to produce the surface film of the missing part, including: The main computer controls the film-forming device to obtain a sol-state silicone rubber solution and place it on a printing paper having a preset thickness and a preset surface roughness to form a silicone rubber solution layer of the preset thickness; the preset surface roughness is determined based on the roughness of the patient's skin surface as collected from the patient information; and the preset thickness is determined based on the thickness of the patient's skin as collected from the patient information; The main computer controls the film-making equipment to cover the upper surface of the silicone rubber solution layer with a substrate sheet, and after the silicone rubber solution layer forms a gel, peels off the silicone rubber solution layer to obtain a surface film of the missing part, and the original contact surface between the surface film of the missing part and the printing paper serves as the printing surface; wherein, the bonding strength of the surface of the substrate sheet to the silicone rubber is higher than the bonding strength of the surface of the printing paper to the silicone rubber, and the printing paper is suitable for the 2D printing equipment.

2. The fully computerized intelligent method for producing a highly similar pseudobody according to claim 1, characterized in that: In the case where the missing part has no symmetrical healthy reference side, the host computer collects patient information and uses a pre-built artificial intelligence model to obtain a 3D model containing the segmentation information of each tissue within the patient's missing part. Based on the collected patient information and the 3D model, a 2D unfolded image of the surface of the 3D model is obtained, including: The host computer uses a pre-built first model to reconstruct a neural network, collects information about the remaining parts of the patient, and performs a three-dimensional reconstruction of the missing part to obtain a reconstructed three-dimensional structural model and a reconstructed surface image of the missing part; wherein the first model reconstruction neural network is implemented using generative AI technology; The three-dimensional structural model is subjected to structural segmentation of internal tissues using a pre-constructed second structural hierarchical neural network, and is modified using model attribute-related information in the collected patient information and a pre-constructed third model modification neural network, to obtain a 3D model after processing; The surface image reconstructed from the missing part is subjected to two-dimensional image expansion according to the 3D model to obtain a 2D expanded image.

3. The fully computer-intelligent high-similarity pseudobody production method according to claim 1, characterized in that: In the case where the missing part has a symmetrical healthy reference side, the host computer collects patient information and uses a pre-built artificial intelligence model to obtain a 3D model containing segmentation information of each tissue within the patient's missing part. Based on the collected patient information and the 3D model, a 2D unfolded image of the surface of the 3D model is obtained, including: The host computer collects depth information of the healthy reference side and the remaining part of the missing part by means of a depth information collection device, and collects multiple surface images of the healthy reference side at different angles by means of an image collection device; The collected depth information is used to perform three-dimensional reconstruction of the missing part to obtain a reconstructed three-dimensional structural model; The three-dimensional structural model is subjected to structural segmentation of internal tissues using a pre-constructed second structural hierarchical neural network, and is modified using model attribute-related information in the collected patient information and a pre-constructed third model modification neural network, to obtain a 3D model after processing; The collected multiple surface images are expanded in two dimensions according to the 3D model to obtain a 2D expanded image.

4. The fully computer-based intelligent high-similarity prosthesis production method according to claim 2 or 3, characterized in that: The model attribute related information includes: The patient's age, gender, height, weight and skin color; the model modification includes modifying the prosthesis size, shape, surface color and surface texture.

5. The fully computer-based intelligent high-similarity prosthesis production method according to claim 2 or 3, characterized in that: The process of obtaining a 2D unfolded image is to use a preset reverse filtering algorithm to compare the digital image of the surface of the 3D model with the printed image obtained by using a 2D printing device, compensate for the real visual difference between the digital image and the printed image, and obtain the 2D unfolded image to be printed; wherein, the digital image includes: when the missing part has no symmetrical healthy reference side, the image of the surface image reconstructed of the missing part according to the 3D model is expanded in a two-dimensional image, or when the missing part has a symmetrical healthy reference side, the image of multiple surface images collected are expanded in a two-dimensional image according to the 3D model.

6. The fully computer-based intelligent high-similarity pseudobody production method according to claim 5, characterized in that: The process of the preset reverse filtering algorithm includes: Step c1, reading initial parameters; wherein the initial parameters include image matrix size, similarity threshold and maximum number of iterations; Step c2, acquiring the digital image; Step c3, printing the digital image using a 2D printing device to obtain an original printed image; Step c4, using the same image acquisition device used in the process of obtaining the digital image, performing image acquisition on the digital image and the original printed image to obtain corresponding acquired images respectively; Step c5, calculating an inverse filtering function using the captured images corresponding to the current digital image and the original printed image; Step c6, applying the reverse filtering function to the current digital image to obtain a reverse filtered digital image; Step c7, determining whether the current number of iterations is less than the maximum number of iterations; if so, executing step c8; if not, executing step c14; Step c8, printing the currently obtained reverse filtered digital image using a 2D printing device to obtain a first printed image; Step c9, using the same image acquisition device, performing image acquisition on the currently obtained reverse filtered digital image and the first printed image to obtain corresponding acquired images; Step c10, calculating a similarity index for the acquired images corresponding to the currently obtained reverse filtered digital image and the first printed image; Step c11, determining whether the similarity index is greater than the similarity threshold; if so, executing step c12; if not, executing step c13; Step c12, determining the currently obtained reverse filtered digital image as the 2D expanded image to be printed; Step c13, storing the currently obtained reverse filtered digital image and the similarity index, replacing the current digital image with the currently obtained reverse filtered digital image, replacing the acquired image corresponding to the current digital image with the acquired image corresponding to the currently obtained reverse filtered digital image, and replacing the acquired image corresponding to the current original printed image with the acquired image corresponding to the current first printed image; Step c14 , searching for the reverse filtered digital image with the largest similarity index in all iterations, and using it as the 2D expanded image to be printed.

7. The fully computerized intelligent method for producing a highly similar pseudobody according to claim 1, characterized in that: The host computer controls the 3D printing device to print the 3D model to obtain the prosthesis body, including: The host computer controls the 3D printing device to print with materials that match the tissues in the 3D model to obtain a prosthesis body; wherein the materials that match the tissues include a variety of optional materials.

8. The fully computer-based intelligent high-similarity prosthesis production method according to claim 7, characterized in that: For each tissue, the matching material determines a suitable one from a variety of optional materials for the tissue based on the collected patient information, so that it has the hardness and viscoelasticity most similar to the patient's real body.

9. The fully computerized intelligent high-similarity prosthesis production method according to claim 1, characterized in that: Before the host computer controls the 2D printing device to print the 2D expanded image onto the surface film of the missing part to form a skin, the method further includes: The main computer controls the printing system to treat the surface of the surface film of the missing part with a surface treatment liquid, and evenly prints the pigment ink after the treatment.

10. The fully computerized intelligent high-similarity pseudobody production method according to claim 1, characterized in that: The main computer controls the assembly equipment to attach the skin to the surface of the prosthesis body, including: The main computer controls the printing system to apply glue and lubricant to the surface of the prosthesis body, covers the coated surface of the prosthesis body with the skin, and controls the uniform pressurizing instrument to perform pressurization so that the skin and the prosthesis body are tightly fitted to form an integral structure.

11. The fully computer-based intelligent high-similarity prosthesis production method according to claim 10, characterized in that: The density and adhesiveness of the glue match the collected patient information; and the lubricant is a lubricant with biocompatibility.

12. The fully computerized intelligent high-similarity pseudobody production method according to claim 1, characterized in that: The surface treatment comprises: The main computer controls the inkjet printing system to coat the surface of the prosthesis body after the skin is attached with a protective layer similar to the surface of human skin.

13. A fully computerized intelligent high-similarity propulsion production system, characterized in that: include: Main computer and other components; The main computer controls the remaining components to complete the method steps described in any one of claims 1 to 12; the remaining components include an image acquisition and storage system, a film-making device, an assembly device, a printing system, a uniform pressurizing instrument, and a printing system; the image acquisition and storage system includes a depth information acquisition device and an image acquisition device; the printing system includes at least a 3D printing device and a 2D printing device.

14. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps described in any one of claims 1 to 12 are implemented.

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