Intelligent dental image analysis system and method for oral surgery

Abstract

The present application relates to the technical field of dental image analysis, and discloses a dental image intelligent analysis system and method for oral surgery. It first acquires dental image data and extracts multi-scale feature maps through a backbone network, including shallow, middle and deep feature maps. Subsequently, a spatial adaptive module is used to enhance the spatial characteristics of the middle and deep feature maps, while the edge detection branch is used to refine the shallow feature map to capture the details of the tooth edge. Next, these feature maps are fused to generate a multi-scale significant fusion feature map, which is then processed by the classification layer to obtain a tooth voxel probability map. Finally, it is converted into a discrete segmentation label map as a semantic segmentation result. This method aims to improve the accuracy and robustness of dental image analysis by integrating multi-scale features with spatial context information, and provide more reliable support for oral surgery.

Description

Technical Field

[0001] The present application relates to the technical field of dental image analysis, and more specifically, to a dental image intelligent analysis system and method for oral surgery. Background Art

[0002] In the field of oral surgery, accurate dental image analysis is crucial for diagnosis and treatment planning. Traditional dental image analysis methods often rely on the physician's experience and expertise, which is not only time-consuming but also susceptible to subjective factors, resulting in limited consistency and accuracy in diagnostic results. With the development of medical imaging technology, digital image processing has become one of the most important means to improve diagnostic efficiency and accuracy. However, existing digital image processing technologies still face many challenges when faced with complex tooth structures, blurred boundaries, and insufficient feature extraction.

[0003] First, the anatomical structure of teeth and their surrounding tissues is very complex, and there are huge differences between different individuals. Traditional image analysis methods are difficult to capture these detailed information comprehensively and accurately, especially when it comes to detecting subtle lesions. Secondly, due to factors such as shooting angle and equipment resolution, the boundaries in tooth images are often not clear enough, which brings difficulties to subsequent segmentation and recognition. Furthermore, how to effectively extract representative feature information from tooth images is the key to achieving automated analysis. However, most current methods focus on the extraction of shallow features and pay insufficient attention to deep features, resulting in inaccurate positioning of lesions.

[0004] Therefore, an intelligent dental imaging analysis solution for oral surgery is expected. Summary of the Invention

[0005] In view of the problems that the existing technology has in processing dental images, such as being sensitive to noise, difficulty in accurately segmenting complex structures, and ignoring spatial context information, the present invention proposes an intelligent dental image analysis system and method for oral surgery.

[0006] According to one aspect of the present application, a method for intelligent analysis of dental images for oral surgery is provided, comprising: acquiring dental image data for oral surgery; inputting the dental image data into a backbone network to obtain a dental image shallow feature map, a dental image middle feature map, and a dental image deep feature map; inputting the dental image middle feature map and the dental image deep feature map into a spatial adaptive module to obtain a dental image middle spatial enhancement feature map and a dental image deep spatial enhancement feature map; inputting the dental image shallow feature map into an edge detection branch to obtain a dental edge information feature map; fusing the dental image middle spatial enhancement feature map, the dental image deep spatial enhancement feature map, and the dental edge information feature map to obtain a dental image multi-scale significant fusion feature map; inputting the dental image multi-scale significant fusion feature map into a classification layer to obtain a dental voxel probability map; and converting the dental voxel probability map into a dental voxel discrete segmentation label map as a dental image semantic segmentation result.

[0007] In the above-mentioned intelligent analysis method of dental images for oral surgery, the shallow feature map of the dental image is input into the edge detection branch to obtain the tooth edge information feature map, including: inputting the shallow feature map of the dental image into the convolution layer of the edge detection branch to obtain the tooth image edge feature focus feature map; inputting the tooth image edge feature focus feature map into the activation layer of the edge detection branch to obtain the tooth edge feature mask feature map, and the activation layer uses the Sigmoid activation function; calculating the position point multiplication between the tooth edge feature mask feature map and the shallow feature map of the dental image to obtain the tooth edge information feature map.

[0008] In the above-mentioned intelligent analysis method of dental images for oral surgery, the backbone network is a SwinTransformer network.

[0009] In the above-mentioned intelligent analysis method of dental images for oral surgery, the middle-layer feature map of the dental image and the deep-layer feature map of the dental image are input into the spatial adaptive module to obtain the middle-layer spatial enhancement feature map of the dental image and the deep-layer spatial enhancement feature map of the dental image, including: inputting the middle-layer feature map of the dental image into the offset learning module based on the convolution layer to obtain the middle-layer feature offset map of the dental image; inputting the middle-layer feature map of the dental image into the modulation scalar learning module based on the convolution layer to obtain the middle-layer feature modulation map of the dental image; combining the middle-layer feature offset map of the dental image and the middle-layer feature modulation map of the dental image, and inputting the middle-layer feature map of the dental image into the spatial adaptive enhancement component based on the deformable convolution layer to obtain the middle-layer spatial enhancement feature map of the dental image.

[0010] In the above-mentioned intelligent analysis method of dental images for oral surgery, the dental image mid-layer feature offset map and the dental image mid-layer feature modulation map are combined, and the dental image mid-layer feature map is input into a spatial adaptive enhancement component based on a deformable convolutional layer to obtain the dental image mid-layer spatial enhancement feature map, including: performing bilinear interpolation sampling on the dental image mid-layer feature map based on the dental image mid-layer feature offset map to obtain the dental image mid-layer sampling feature map; calculating the position point multiplication between the dental image mid-layer sampling feature map and the dental image mid-layer feature modulation map to obtain the dental image mid-layer spatial enhancement feature map.

[0011] In the above-mentioned intelligent analysis method of dental images for oral surgery, the mid-layer spatial enhancement feature map of the dental image, the deep-layer spatial enhancement feature map of the dental image and the tooth edge information feature map are fused to obtain a multi-scale significant fusion feature map of the dental image, including: inputting the mid-layer spatial enhancement feature map of the dental image, the deep-layer spatial enhancement feature map of the dental image and the tooth edge information feature map into a skip connection layer to obtain an initial multi-scale significant fusion down-sampling feature map of the dental image; performing complementary fusion optimization of visual semantic features on the initial multi-scale significant fusion down-sampling feature map of the dental image to obtain a multi-scale significant fusion down-sampling feature map of the dental image; and upsampling the multi-scale significant fusion down-sampling feature map of the dental image to obtain the multi-scale significant fusion feature map of the dental image.

[0012] In the above-mentioned intelligent analysis method of dental images for oral surgery, the complementary fusion optimization of visual semantic features is performed on the initial multi-scale significant fusion downsampling feature map of the dental image to obtain the multi-scale significant fusion downsampling feature map of the dental image, including: performing cavity convolution operations with different cavity rates on the dental image mid-layer space enhancement feature map, the dental image deep space enhancement feature map and the dental edge information feature map to obtain the dental image mid-layer space enhancement cavity convolution feature map, the dental image deep space enhancement cavity convolution feature map and the dental edge information cavity convolution feature map; taking the dental image mid-layer space enhancement cavity convolution feature map as a benchmark, calculating The cross-layer geometric feature responses of the deep spatial enhancement cavity convolution feature map of the dental image and the tooth edge information cavity convolution feature map are obtained to obtain a first dental image cross-layer geometric feature response feature map and a second dental image cross-layer geometric feature response feature map; based on the shift window mechanism and the relative position encoding mechanism, the first dental image cross-layer geometric feature response feature map and the second dental image cross-layer geometric feature response feature map are fused and corrected to obtain a dental image multi-scale corrected feature map; the dental image multi-scale corrected feature map is point multiplied with the initial dental image multi-scale significant fusion downsampling feature map to obtain the dental image multi-scale significant fusion downsampling feature map.

[0013] In the above-mentioned intelligent dental image analysis method for oral surgery, the cavity rates of the cavity convolution operation are 3, 5, and 1 respectively.

[0014] In the above-mentioned intelligent analysis method of dental images for oral surgery, the classification layer includes a point convolution layer and a Softmax classification unit.

[0015] According to another aspect of the present application, a dental image intelligent analysis system for oral surgery is provided, comprising: a dental image data acquisition module for acquiring dental image data for oral surgery; a dental image multi-scale feature extraction module for inputting the dental image data into a backbone network to obtain a dental image shallow feature map, a dental image middle feature map, and a dental image deep feature map; a dental image spatial enhancement module for inputting the dental image middle feature map and the dental image deep feature map into a spatial adaptive module to obtain a dental image middle spatial enhancement feature map and a dental image deep spatial enhancement feature map; a dental image An edge detection module is used to input the shallow feature map of the dental image into the edge detection branch to obtain a dental edge information feature map; a dental image multi-scale feature fusion module is used to fuse the dental image middle-layer spatial enhancement feature map, the dental image deep-layer spatial enhancement feature map and the dental edge information feature map to obtain a dental image multi-scale significant fusion feature map; a dental image classification processing module is used to input the dental image multi-scale significant fusion feature map into the classification layer to obtain a dental voxel probability map; a dental image semantic segmentation module is used to convert the dental voxel probability map into a dental voxel discrete segmentation label map as a dental image semantic segmentation result.

[0016] Compared with the existing technology, the dental image intelligent analysis system and method for oral surgery provided by the present application first obtains dental image data and extracts multi-scale feature maps through the backbone network, including shallow, middle and deep feature maps. Subsequently, a spatial adaptive module is used to enhance the spatial characteristics of the middle and deep feature maps, while the edge detection branch is used to refine the shallow feature map to capture the details of the tooth edge. Next, these feature maps are fused to generate a multi-scale significant fusion feature map, which is then processed by the classification layer to obtain a tooth voxel probability map. Finally, it is converted into a discrete segmentation label map as a semantic segmentation result. This method aims to improve the accuracy and robustness of dental image analysis by integrating multi-scale features with spatial context information, providing more reliable support for oral surgery. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0018] Figure 1 The figure shows a schematic flow chart of the intelligent analysis method of dental images for oral surgery according to an embodiment of the present application.

[0019] Figure 2 The figure shows a schematic flow chart of S3 in the intelligent analysis method of dental images for oral surgery according to an embodiment of the present application.

[0020] Figure 3 The figure shows a schematic flow chart of S33 in the intelligent analysis method of dental images for oral surgery according to an embodiment of the present application.

[0021] Figure 4 The figure shows a schematic flow chart of S4 in the intelligent analysis method of dental images for oral surgery according to an embodiment of the present application.

[0022] Figure 5 The figure shows a schematic flow chart of S5 in the intelligent analysis method of dental images for oral surgery according to an embodiment of the present application.

[0023] Figure 6 The figure shows a schematic block diagram of a dental image intelligent analysis system for oral surgery according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described herein.

[0025] Figure 1 FIG2 is a schematic flow chart of a method for intelligent analysis of dental images for oral surgery according to an embodiment of the present application. Figure 1As shown, the present application provides a dental image intelligent analysis method for oral surgery, including: S1, obtaining dental image data for oral surgery; S2, inputting the dental image data into the backbone network to obtain a dental image shallow feature map, a dental image middle feature map and a dental image deep feature map; S3, inputting the dental image middle feature map and the dental image deep feature map into the spatial adaptive module to obtain a dental image middle spatial enhancement feature map and a dental image deep spatial enhancement feature map; S4, inputting the dental image shallow feature map into the edge detection branch to obtain a dental edge information feature map; S5, fusing the dental image middle spatial enhancement feature map, the dental image deep spatial enhancement feature map and the dental edge information feature map to obtain a dental image multi-scale significant fusion feature map; S6, inputting the dental image multi-scale significant fusion feature map into the classification layer to obtain a dental voxel probability map; S7, converting the dental voxel probability map into a dental voxel discrete segmentation label map as a dental image semantic segmentation result.

[0026] Specifically, in step S1, dental imaging data for oral surgery is acquired. It should be understood that CBCT has been widely used in the field of oral surgery due to its advantages, such as its ability to provide three-dimensional views, low radiation dose, and high image resolution. Therefore, by scanning a patient using a CBCT device, a three-dimensional imaging dataset containing detailed information about the teeth and surrounding structures can be obtained. This data includes not only the morphological characteristics of the teeth themselves, but also the adjacent bone structure and soft tissue, providing a rich information foundation for subsequent analysis.

[0027] In a specific embodiment, the doctor or technician will select appropriate scanning parameters based on clinical needs, such as field of view (FOV), scanning time, and resolution, to ensure that high-quality images that meet diagnostic requirements while minimizing the amount of radiation received by the patient are obtained. Once the settings are completed, the patient will be guided to the correct position and instructed to remain still to complete the scanning process smoothly. The generated raw image data will then be transmitted to a dedicated image processing workstation, where technicians use specific software tools to pre-process the data, including noise reduction, artifact correction, and other steps, to improve image quality and prepare for further analysis.

[0028] Specifically, in step S2, the dental image data is input into the backbone network to obtain a shallow feature map of the dental image, a mid-level feature map of the dental image, and a deep feature map of the dental image. It should be understood that although traditional convolutional neural networks perform well in many visual tasks, they have limitations in processing long-distance dependencies and global information. In contrast, the backbone network utilizes the self-attention mechanism within the local window, combined with the moving window strategy to capture information exchange across windows, which can maintain computational efficiency and effectively capture global dependencies. This is crucial for parsing subtle structural differences in dental images. In addition, by constructing feature maps hierarchically, it can ensure that the model can not only recognize obvious anatomical structures, but also deeply understand the deep semantic information hidden behind them. This method helps to improve the working performance of subsequent spatial adaptive enhancement processing and edge detection branches, laying a solid foundation for the ultimate realization of accurate tooth segmentation and classification.

[0029] Specifically, in one embodiment, the backbone network is a Swin Transformer network. As an improved Transformer architecture, Swin Transformer introduces a hierarchical feature representation method that can effectively capture feature information at different scales. In practical applications, Swin Transformer gradually extracts shallow feature maps, mid-level feature maps, and deep feature maps of dental images from the input dental image data through a series of stages, each of which contains multiple levels of self-attention mechanisms.

[0030] In a specific embodiment, first, Swin Transformer performs preliminary feature extraction on the input dental image data to generate relatively basic shallow feature maps of the dental image. These feature maps contain basic information such as edges and color contrast. As the depth of the network increases, in the intermediate stage, the model begins to focus on more abstract structural features, such as the relative position relationship between teeth or the morphological characteristics of certain specific areas, thereby generating a mid-level feature map of the dental image. In the last one or several stages, the model will further refine more complex and representative deep feature maps of the dental image. These feature maps often correspond to higher-level concepts, such as the three-dimensional shape of the entire tooth or its relationship with other tissues.

[0031] Specifically, in step S3, the middle-layer feature map of the dental image and the deep-layer feature map of the dental image are input into the spatial adaptive module to obtain the middle-layer spatial enhancement feature map of the dental image and the deep-layer spatial enhancement feature map of the dental image. It should be understood that although traditional convolutional neural networks perform well in many visual tasks, they have limitations in processing medical images with significant scale changes and non-uniform distribution characteristics. Especially in the field of oral surgery, the morphology of teeth and their surrounding tissues is different and intertwined, which puts higher demands on feature extraction. By introducing the spatial adaptive module, the sensitivity and robustness of the model to detail features can be greatly improved without significantly increasing the computational cost. In addition, this method also allows the model to automatically adjust its internal parameters according to the characteristics of the input data, thereby improving its adaptability to various types of image data.

[0032] Specifically, the processing of the spatial adaptive module is described by taking the middle layer feature map of the tooth image as an example. Figure 2 As shown, in step S3, the dental image mid-layer feature map and the dental image deep-layer feature map are input into the spatial adaptive module to obtain the dental image mid-layer spatial enhancement feature map and the dental image deep-layer spatial enhancement feature map, including: S31, inputting the dental image mid-layer feature map into the offset learning module based on the convolution layer to obtain the dental image mid-layer feature offset map; S32, inputting the dental image mid-layer feature map into the modulation scalar learning module based on the convolution layer to obtain the dental image mid-layer feature modulation map; S33, combining the dental image mid-layer feature offset map and the dental image mid-layer feature modulation map, inputting the dental image mid-layer feature map into the spatial adaptive enhancement component based on the deformable convolution layer to obtain the dental image mid-layer spatial enhancement feature map. The processing process of inputting the dental image deep-layer feature map into the spatial adaptive module can also refer to the above-mentioned processing process for the dental image mid-layer feature map.

[0033] Specifically, the spatial adaptation module consists of two main components: a convolutional offset learning module and a convolutional modulation scalar learning module. The offset learning module, typically composed of a series of convolutional layers, learns an offset vector for each pixel location, indicating the direction in which the center of the local region of interest should be adjusted. The key to this step is that it allows the model to dynamically adjust its receptive field to better adapt to irregular shapes or boundaries in the image. For example, when processing dental images, this mechanism helps more accurately locate the tooth-gum junction or other subtle structures. Meanwhile, the modulation scalar learning module also employs a convolutional layer structure, but its goal is to generate a scalar value for each pixel location, which is used to adjust the feature response strength at that location. This means that in addition to shifting the receptive field, the information weights of various components in the feature map can be dynamically adjusted based on the importance of the image content. For example, when emphasizing key anatomical landmarks, the response strength of the relevant regions can be increased, while that of background regions can be appropriately weakened, thereby improving the effectiveness and pertinence of the overall feature representation.

[0034] In a specific embodiment, for a convolutional layer-based offset learning module, for example, there is an input feature map F with a size (height, width, number of channels). In order to generate the mid-level feature offset map of the dental image, a network consisting of several convolutional layers is designed in this application. First, a standard convolutional layer (including ReLU activation function) is used, and the number of output channels is set to 2C (because each position requires two values to represent the offset on the x-axis and y-axis). Then, another 1x1 convolutional layer is used to reduce the number of channels to 2, so that the final offset map O is obtained, with a size of Each element here represents the offset that should be applied to the corresponding position.

[0035] For the convolutional layer-based modulation scalar learning module, continuing with the above input feature map F as an example, in order to generate the mid-level feature modulation map of the dental image, this application constructs a similar network structure. First, a convolutional layer (which may also include ReLU activation) is used to process the input feature map, but this time the number of output channels is set to C. Then, a 1x1 convolutional layer with a Sigmoid activation function is passed to ensure that the output value is between 0 and 1, and a modulation scalar map M is obtained with a size of Each value in this scalar map M represents the magnification or reduction ratio of the feature map at the corresponding position.

[0036] Subsequently, the offset map and modulation map generated by the above two modules are fed into the spatial adaptive enhancement component based on the deformable convolution layer. In one embodiment, Figure 3As shown, in step S33, the dental image mid-layer feature offset map and the dental image mid-layer feature modulation map are combined, and the dental image mid-layer feature map is input into the spatial adaptive enhancement component based on the deformable convolution layer to obtain the dental image mid-layer spatial enhancement feature map, including: S331, based on the dental image mid-layer feature offset map, bilinear interpolation sampling is performed on the dental image mid-layer feature map to obtain the dental image mid-layer sampling feature map; S332, the position point multiplication between the dental image mid-layer sampling feature map and the dental image mid-layer feature modulation map is calculated to obtain the dental image mid-layer spatial enhancement feature map.

[0037] Specifically, in step S4, the shallow feature map of the tooth image is input into the edge detection branch to obtain a tooth edge information feature map. The edge detection branch is a network used for edge detection. It should be understood that the edge detection branch can significantly improve the accuracy of edge detection without sacrificing detail, and provide strong support for subsequent multi-scale feature fusion and tooth voxel segmentation tasks.

[0038] In one embodiment, Figure 4 As shown, in step S4, the tooth image shallow feature map is input into the edge detection branch to obtain a tooth edge information feature map, including: S41, inputting the tooth image shallow feature map into the convolution layer of the edge detection branch to obtain a tooth image edge feature focus feature map; S42, inputting the tooth image edge feature focus feature map into the activation layer of the edge detection branch to obtain the tooth edge feature mask feature map, and the activation layer uses a Sigmoid activation function; S43, calculating the position point multiplication between the tooth edge feature mask feature map and the tooth image shallow feature map to obtain the tooth edge information feature map.

[0039] Specifically, in step S5, the middle-layer spatial enhancement feature map of the dental image, the deep-layer spatial enhancement feature map of the dental image, and the tooth edge information feature map are fused to obtain a multi-scale significant fusion feature map of the dental image. It is often difficult to fully capture all the important information in the dental image by relying solely on a feature map of a certain level. For example, a shallow feature map may contain rich detail information, but lacks understanding of the overall structure; on the contrary, although a deep feature map is good at identifying large-scale structural patterns, it may have defects in detail description. Therefore, by fusing feature maps at different levels and using jump connections, these problems can be effectively overcome to ensure that the generated multi-scale significant fusion feature map has sufficient detail clarity and can accurately reflect global structural features.

[0040] In one embodiment, Figure 5As shown, in step S5, the middle-layer spatial enhancement feature map of the dental image, the deep-layer spatial enhancement feature map of the dental image and the tooth edge information feature map are fused to obtain a multi-scale significant fusion feature map of the dental image, including: S51, inputting the middle-layer spatial enhancement feature map of the dental image, the deep-layer spatial enhancement feature map of the dental image and the tooth edge information feature map into a skip connection layer to obtain an initial multi-scale significant fusion down-sampling feature map of the dental image; S52, performing complementary fusion optimization of visual semantic features on the initial multi-scale significant fusion down-sampling feature map of the dental image to obtain a multi-scale significant fusion down-sampling feature map of the dental image; S53, upsampling the multi-scale significant fusion down-sampling feature map of the dental image to obtain the multi-scale significant fusion feature map of the dental image.

[0041] Specifically, the feature maps from the encoder (corresponding to the mid-level and deep-level feature maps output by the backbone network) are directly passed to the decoder (i.e., the feature fusion stage) via skip connections. This connection method not only helps alleviate the gradient vanishing problem, but also ensures that important details of the original image are preserved while restoring the image resolution. Within the skip connection layer, addition or splicing may be used to fuse feature maps from different sources. For example, a splicing operation can be performed on the mid-level spatial enhancement feature map and the deep-level spatial enhancement feature map to form a new feature map, which is then added to the tooth edge information feature map to obtain the initial tooth image multi-scale significant fusion down-sampled feature map.

[0042] Preferably, considering that when fusing the dental image mid-layer spatial enhancement feature map, the dental image deep-layer spatial enhancement feature map and the dental edge information feature map, since the dental image mid-layer spatial enhancement feature map and the dental image deep-layer spatial enhancement feature map are respectively enhanced by offset and modulation scalar based on the mid-layer visual feature space representation and the deep visual feature space representation of the dental image data, and the dental edge information feature map is also enhanced by edge feature activation in the shallow visual feature space representation of the dental image data, it is expected that the complementary fusion of visual semantic features can be achieved while maintaining the integrity of spatial details during fusion.

[0043] Based on this, in this application, the complementary fusion optimization of visual semantic features is performed on the initial multi-scale significant fusion down-sampling feature map of the dental image to obtain the multi-scale significant fusion down-sampling feature map of the dental image, including: first, for the mid-layer spatial enhancement feature map of the dental image , the deep spatial enhancement feature map of the tooth image and the tooth edge information feature map , respectively perform dilated convolution operations with dilation rates of {3, 5, 1} , and , in order to obtain the convolution feature map of the middle-layer space enhancement cavity of the tooth image, the convolution feature map of the deep-layer space enhancement cavity of the tooth image and the convolution feature map of the cavity of the tooth edge information, which are expressed as:

[0044] ;

[0045] ;

[0046] ;

[0047] in, Represents the tooth edge information cavity convolution feature map, Represents the spatial enhancement of the cavity convolution feature map in the middle layer of the tooth image, Represents the deep spatial enhancement of the dental image dilated convolution feature map. Here, the dilated convolution operation is used to achieve multi-scale cross-layer structure decomposition association with spatial invariance.

[0048] Then, the cross-layer geometric feature response is established with the middle-layer feature as the benchmark, that is, the cross-layer geometric feature response of the deep-layer spatial enhancement cavity convolution feature map of the tooth image and the tooth edge information cavity convolution feature map is calculated to obtain the first tooth image cross-layer geometric feature response feature map and the second tooth image cross-layer geometric feature response feature map, which are expressed as:

[0049] ;

[0050] ;

[0051] in, Indicates subtraction by position, Indicates point multiplication by position, Indicates the inverse of the feature value at each position of the tooth edge information cavity convolution feature map. Indicates the inverse of the eigenvalue of each position of the convolution feature map of the deep spatial enhancement cavity of the tooth image. Represents the first tooth image cross-layer geometric feature response feature map, Represents the cross-layer geometric feature response feature map of the second tooth image. In other words, the depth semantic confidence variation is compensated by the depth reference geometric response, which is equivalent to the depth confidence semantic space alignment for the mid-layer features.

[0052] In this way, based on the shift window mechanism and relative position encoding mechanism of the Swin Transformer network, the first tooth image cross-layer geometric feature response feature map and the second tooth image cross-layer geometric feature response feature map can be fused and corrected to obtain a multi-scale corrected feature map of the tooth image, which can be expressed as:

[0053] ;

[0054] here, Indicates that the feature map The feature matrix of is shifted along the width and height directions , Indicates that the feature map The feature matrix of is shifted along the width and height directions , and specifically, circular shift can be used, for example, the first row is shifted up one position to fill the last row, It means adding by position. Represents the multi-scale corrected feature map of dental images.

[0055] Finally, the multi-scale corrected feature map of the dental image is fused and optimized by performing a dot product with the initial multi-scale fused downsampled feature map of the dental image to obtain the multi-scale fused downsampled feature map of the dental image. In this way, based on the more essential cross-layer interactive representation of the feature set space by the depth semantic confidence, the multi-scale decomposition response coordination and depth semantic space alignment are used to achieve complementary fusion of the visual feature space representations of different depth features.

[0056] Then, after completing the fusion optimization, it is necessary to perform an upsampling operation on the multi-scale significant fusion downsampled feature map of the dental image to restore the spatial resolution of the original image. In this application, this can be achieved through methods such as deconvolution or bilinear interpolation. Deconvolution is a commonly used upsampling technique that increases the spatial size of the feature map by learning an inverse convolution kernel; while bilinear interpolation performs interpolation calculations based on adjacent pixel values. Although simple, it can also provide a good smoothing effect. The choice of which method depends on the specific task requirements and the desired level of accuracy.

[0057] Specifically, in step S6, the multi-scale saliency fusion feature map of the dental image is input into a classification layer, which comprises a point convolution layer and a softmax classification unit, to generate a dental voxel probability map. It should be understood that the point convolution layer effectively captures subtle anatomical features while maintaining high resolution; while the softmax classification unit maps these features to specific class labels, providing doctors with intuitive and easy-to-interpret results.

[0058] In a specific embodiment, the multi-scale significant fusion feature map of the dental image is first input into the point convolution layer. The point convolution layer extracts a more abstract feature representation by applying a set of learnable filters to perform convolution operations on small areas around each voxel. Since these filters can reduce the number of parameters without losing spatial information, they help improve computational efficiency and reduce the risk of overfitting. Next, the feature map processed by the point convolution layer is sent to the Softmax classification unit. The Softmax function can convert the original output into a probability distribution, so that each voxel has a probability value corresponding to a specific category. Specifically, the Softmax function is used to determine which voxels belong to the tooth structure and which do not.

[0059] Specifically, in step S7, the dental voxel probability map is converted into a dental voxel discrete segmentation label map as the dental image semantic segmentation result. It should be understood that relying solely on the original dental voxel probability map often fails to directly achieve ideal segmentation results, as the probability values themselves do not always perfectly reflect the actual anatomical structure boundaries. Therefore, further converting the dental voxel probability map into a dental voxel discrete segmentation label map as the dental image semantic segmentation result can not only significantly improve the quality of the segmentation results, but also enhance the model's adaptability to various complex situations.

[0060] Specifically, after obtaining the tooth voxel probability map, a threshold is first applied to each voxel to determine its class. Specifically, a probability threshold can be set (for example, 0.5, which can be adjusted based on actual conditions and is not specifically limited in this embodiment). If the probability value of a voxel for a certain class exceeds this threshold, the voxel is labeled as that class; otherwise, it may be considered part of the background or another class. This method is simple and straightforward, but it may sometimes be insufficient for complex boundary conditions or unevenly distributed probability values. Therefore, in practical applications, more sophisticated strategies may be necessary, such as dynamic threshold adjustment or combining multiple criteria for judgment, to improve segmentation accuracy. Furthermore, to ensure the consistency and accuracy of the segmentation results, connected component analysis can be performed. Connected component analysis can help identify and separate different objects or regions in the image. This is particularly important for distinguishing closely adjacent teeth or teeth from surrounding tissue. Connected component analysis can effectively remove isolated small noise points and group voxels with similar attributes together, resulting in clearer and more accurate segmentation results. Finally, morphological operations can be performed. Morphological operations include dilation, erosion, opening, and closing, which can help correct holes, breaks, or other irregularities that may appear during the segmentation process. For example, appropriate dilation can fill segmentation gaps caused by noise or low probability values, while erosion can be used to remove unnecessary edge extensions, ensuring that the final segmentation contours are as close to the actual anatomical structure as possible.

[0061] In summary, the present application provides an intelligent dental image analysis method for oral surgery, which first acquires dental image data and extracts multi-scale feature maps through a backbone network, including shallow, middle and deep feature maps. Subsequently, a spatial adaptive module is used to enhance the spatial characteristics of the middle and deep feature maps, while the edge detection branch is used to refine the shallow feature map to capture the details of the tooth edges. Next, these feature maps are fused to generate a multi-scale significant fusion feature map, which is then processed by the classification layer to obtain a tooth voxel probability map. Finally, it is converted into a discrete segmentation label map as a semantic segmentation result. This method aims to improve the accuracy and robustness of dental image analysis by integrating multi-scale features with spatial context information, providing more reliable support for oral surgery.

[0062] This application also provides a dental imaging intelligent analysis system for oral surgery, such as Figure 6As shown, the dental image intelligent analysis system 100 for oral surgery includes: a dental image data acquisition module 11 for acquiring dental image data for oral surgery; a dental image multi-scale feature extraction module 12 for inputting the dental image data into a backbone network to obtain a dental image shallow feature map, a dental image middle feature map and a dental image deep feature map; a dental image space enhancement module 13 for inputting the dental image middle feature map and the dental image deep feature map into a space adaptation module to obtain a dental image middle space enhancement feature map and a dental image deep space enhancement feature map; a dental image edge detection module 14, used to input the shallow feature map of the dental image into the edge detection branch to obtain a dental edge information feature map; a dental image multi-scale feature fusion module 15, used to fuse the dental image middle-layer spatial enhancement feature map, the dental image deep-layer spatial enhancement feature map and the dental edge information feature map to obtain a dental image multi-scale significant fusion feature map; a dental image classification processing module 16, used to input the dental image multi-scale significant fusion feature map into the classification layer to obtain a dental voxel probability map; a dental image semantic segmentation module 17, used to convert the dental voxel probability map into a dental voxel discrete segmentation label map as a dental image semantic segmentation result.

[0063] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this application are merely illustrative and not restrictive, and it should not be assumed that these advantages, strengths, and effects are required of each embodiment of this application. In addition, the specific details disclosed above are merely illustrative and facilitating understanding, and are not restrictive. The above details do not limit this application to necessarily being implemented using the above specific details.

[0064] The block diagrams of the devices, devices, equipment, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0065] It should also be noted that in the apparatus, device, and method of the present application, each component or each step can be decomposed and / or recombined, and such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0066] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0067] The above description has been provided for the purpose of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A dental image intelligent analysis method for oral surgery, characterized in that: include: Acquiring dental imaging data for oral surgery; Inputting the dental image data into a backbone network to obtain a dental image shallow feature map, a dental image middle feature map, and a dental image deep feature map; Inputting the middle-layer feature map of the tooth image and the deep-layer feature map of the tooth image into a spatial adaptive module to obtain a middle-layer spatial enhancement feature map of the tooth image and a deep-layer spatial enhancement feature map of the tooth image; Inputting the shallow feature map of the tooth image into the edge detection branch to obtain a tooth edge information feature map; fusing the middle-layer spatial enhancement feature map of the tooth image, the deep-layer spatial enhancement feature map of the tooth image, and the tooth edge information feature map to obtain a multi-scale significant fusion feature map of the tooth image; Inputting the multi-scale significant fusion feature map of the tooth image into the classification layer to obtain a tooth voxel probability map; Converting the tooth voxel probability map into a tooth voxel discrete segmentation label map as a tooth image semantic segmentation result; The method of fusing the middle-layer spatial enhancement feature map of the tooth image, the deep-layer spatial enhancement feature map of the tooth image, and the tooth edge information feature map to obtain a multi-scale significant fusion feature map of the tooth image comprises: Inputting the middle-layer spatial enhancement feature map of the tooth image, the deep-layer spatial enhancement feature map of the tooth image, and the tooth edge information feature map into a skip connection layer to obtain an initial tooth image multi-scale significant fusion down-sampled feature map; The complementary fusion optimization of visual semantic features of the initial multi-scale significant fusion down-sampling feature map of the dental image is performed to obtain a multi-scale significant fusion down-sampling feature map of the dental image, including: performing void convolution operations with different void rates on the dental image mid-layer space enhancement feature map, the dental image deep space enhancement feature map and the dental edge information feature map to obtain a dental image mid-layer space enhancement void convolution feature map, a dental image deep space enhancement void convolution feature map and a dental edge information void convolution feature map; taking the dental image mid-layer space enhancement void convolution feature map as a benchmark, calculating the dental image deep space enhancement void convolution feature map. The cross-layer geometric feature response of the cavity convolution feature map and the tooth edge information cavity convolution feature map is used to obtain a first tooth image cross-layer geometric feature response feature map and a second tooth image cross-layer geometric feature response feature map; based on the shift window mechanism and the relative position encoding mechanism, the first tooth image cross-layer geometric feature response feature map and the second tooth image cross-layer geometric feature response feature map are fused and corrected to obtain a tooth image multi-scale corrected feature map; the tooth image multi-scale corrected feature map is point-multiplied with the initial tooth image multi-scale significant fusion down-sampling feature map to obtain the tooth image multi-scale significant fusion down-sampling feature map; The multi-scale significant fusion down-sampled feature map of the dental image is up-sampled to obtain the multi-scale significant fusion feature map of the dental image.

2. The dental image intelligent analysis method for oral surgery according to claim 1, characterized in that: Inputting the shallow feature map of the tooth image into the edge detection branch to obtain a tooth edge information feature map, including: Inputting the shallow feature map of the tooth image into the convolution layer of the edge detection branch to obtain a focused feature map of the edge features of the tooth image; Inputting the tooth image edge feature focus feature map into the activation layer of the edge detection branch to obtain a tooth edge feature mask feature map, wherein the activation layer uses a Sigmoid activation function; The tooth edge information feature map is obtained by multiplying the tooth edge feature mask feature map and the tooth image shallow feature map by position points.

3. The dental image intelligent analysis method for oral surgery according to claim 1, characterized in that: The backbone network is a Swin Transformer network.

4. The dental image intelligent analysis method for oral surgery according to claim 1, characterized in that: Inputting the middle-layer feature map of the tooth image and the deep-layer feature map of the tooth image into a spatial adaptive module to obtain a middle-layer spatial enhancement feature map of the tooth image and a deep-layer spatial enhancement feature map of the tooth image, including: Inputting the middle-layer feature map of the tooth image into an offset learning module based on a convolutional layer to obtain a middle-layer feature offset map of the tooth image; Inputting the middle-layer feature map of the tooth image into a modulation scalar learning module based on a convolutional layer to obtain a middle-layer feature modulation map of the tooth image; The tooth image mid-layer feature offset map and the tooth image mid-layer feature modulation map are combined, and the tooth image mid-layer feature map is input into a spatial adaptive enhancement component based on a deformable convolutional layer to obtain the tooth image mid-layer spatial enhancement feature map.

5. The method for intelligent analysis of dental images for oral surgery according to claim 4, characterized in that: Combining the tooth image mid-layer feature offset map and the tooth image mid-layer feature modulation map, inputting the tooth image mid-layer feature map into a spatial adaptive enhancement component based on a deformable convolutional layer to obtain the tooth image mid-layer spatial enhancement feature map, including: Performing bilinear interpolation sampling on the middle-layer feature map of the tooth image based on the middle-layer feature offset map of the tooth image to obtain a middle-layer sampling feature map of the tooth image; The spatial enhancement feature map of the middle layer of the tooth image is obtained by calculating the multiplication of the sampling feature map of the middle layer of the tooth image and the feature modulation map of the middle layer of the tooth image according to the position point.

6. The dental image intelligent analysis method for oral surgery according to claim 1, characterized in that: The dilation rates of the dilated convolution operation are 3, 5, and 1 respectively.

7. The dental image intelligent analysis method for oral surgery according to claim 1, characterized in that: The classification layer includes a point convolution layer and a Softmax classification unit.

8. A dental image intelligent analysis system for oral surgery, used to execute the dental image intelligent analysis method for oral surgery according to any one of claims 1 to 7, characterized in that: include: A dental image data acquisition module, used to acquire dental image data for oral surgery; A dental image multi-scale feature extraction module, configured to input the dental image data into a backbone network to obtain a dental image shallow feature map, a dental image middle feature map, and a dental image deep feature map; A tooth image spatial enhancement module, configured to input the tooth image middle layer feature map and the tooth image deep layer feature map into a spatial adaptive module to obtain a tooth image middle layer spatial enhancement feature map and a tooth image deep layer spatial enhancement feature map; A tooth image edge detection module is used to input the tooth image shallow feature map into the edge detection branch to obtain a tooth edge information feature map; A dental image multi-scale feature fusion module, configured to fuse the dental image mid-layer spatial enhancement feature map, the dental image deep-layer spatial enhancement feature map, and the dental edge information feature map to obtain a dental image multi-scale significant fusion feature map; A tooth image classification processing module, configured to input the multi-scale significant fusion feature map of the tooth image into a classification layer to obtain a tooth voxel probability map; The tooth image semantic segmentation module is used to convert the tooth voxel probability map into a tooth voxel discrete segmentation label map as the tooth image semantic segmentation result.

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