Deleted modal brain tumor segmentation method based on feature decoupling and multilayer attention fusion

Abstract

The invention relates to a missing modal brain tumor segmentation method based on feature decoupling and multilayer attention fusion, and belongs to the technical field of medical image processing. Aiming at the problems of modal missing of multi-modal MRI data caused by equipment failure or scanning limitation and feature redundancy and sudden drop of segmentation performance caused by improper processing of a traditional method, the method comprises the following steps: 1) a multi-scale feature decoupling module is provided for separating high-frequency local details, an intermediate-frequency region structure and a low-frequency global contour through frequency domain filtering and a hierarchical attention mechanism; 2) a dynamic weight distribution and multi-layer attention module is used for realizing missing compensation based on pixel-level Softmax weighting and cross-modal iteration interaction; and 3) the U-Net decoder combines with a random modal shielding training strategy. According to the method, the segmentation precision and robustness in a modal missing scene are effectively improved, noise introduction in a traditional completion method is avoided, and the real-time performance and accuracy of clinical diagnosis are guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical image processing and relates to a missing modality brain tumor segmentation method based on feature decoupling and multi-layer attention fusion. Background Art

[0002] Brain tumors are one of the most deadly diseases threatening human health. They are highly invasive and complex, and their accurate segmentation is crucial for clinical diagnosis, treatment planning, and prognosis assessment. Magnetic resonance imaging (MRI), as a non-invasive imaging technology, provides complementary anatomical and pathological information through multimodal data (such as T1, T2, T1ce, and Flair), and has become a core method for brain tumor diagnosis. However, existing technologies still face severe challenges due to the following factors:

[0003] (1) The inherent complexity of multimodal data

[0004] MRI images often contain artifacts, noise, field offset effects, and tissue contrast differences, which lead to blurred tumor boundaries and significant heterogeneity, making it particularly difficult to identify low-grade tumors or tiny lesions.

[0005] There is information redundancy and coupling between multimodal data, such as the repeated representation of T1 and T1ce modalities in enhanced areas, and existing methods fail to effectively decouple modality-specific features, resulting in redundant feature expression.

[0006] (2) The universality and destructiveness of modality loss

[0007] In actual clinical practice, some modalities (such as Flair or T1ce) are often missing due to equipment failure, limited scanning conditions (such as emergency scenarios), patient contraindications (such as contrast agent allergy) or data corruption.

[0008] Most existing methods are designed based on the complete modality assumption. Directly eliminating missing modes or filling them with zeros will introduce noise, resulting in a significant decrease in segmentation accuracy (literature reports that the Dice coefficient decreases by 10%-15% when one mode is missing).

[0009] (3) Limitations of existing technologies

[0010] Traditional multimodal fusion methods (such as early fusion and feature splicing) do not decouple the common information (such as anatomical structure) and unique information (such as pathological enhancement areas) between modalities, resulting in the model learning invalid features.

[0011] Existing missing modality processing methods (such as generative adversarial network completion) rely on complex generation processes, and the deviation between the completed data and the true distribution will propagate to the segmentation stage, introducing error accumulation.

[0012] Traditional attention models (such as channel / spatial attention) only focus on a single scale or local interaction, and it is difficult to dynamically coordinate the hierarchical feature contributions of multiple modalities in missing scenarios.

[0013] Studies have shown that when the Flair modality is missing, the accuracy of edema area segmentation decreases by about 23% (see Zhang et al., MICCAI 2021);

[0014] Existing feature fusion methods (such as HeMIS, CVPR 2016) handle missing modalities through average pooling, but ignore the nonlinear dependencies between modalities, resulting in an increased missed detection rate of small tumor areas.

[0015] In summary, achieving robust segmentation in modality-missing conditions by decoupling redundant multimodal features and dynamically fusing cross-modal hierarchical information is a technical bottleneck that urgently needs to be overcome. To address this issue, this paper proposes an innovative method based on feature decoupling and multi-layer attention fusion, significantly improving segmentation accuracy and clinical applicability in missing scenarios. Summary of the Invention

[0016] In view of this, the purpose of the present invention is to provide a modality-missing brain tumor segmentation method based on feature decoupling and multi-layer attention fusion, thereby improving the segmentation accuracy of the model in the case of modality missing.

[0017] In order to achieve the above object, the present invention provides the following technical solutions:

[0018] A missing modality brain tumor segmentation method based on feature decoupling and multi-layer attention fusion includes the following steps:

[0019] S1. Data preparation stage: Preprocessing of brain tumor multimodal images, including slicing, resizing, normalization, and data augmentation, and dividing the training set into the test set;

[0020] S2, multi-scale feature decoupling stage: each modality image is input into the unique feature extraction block (UFEB), which uses frequency domain filtering and attention mechanism to hierarchically decouple high-frequency local details, mid-frequency regional structure and low-frequency global contour features;

[0021] S3. Dynamic weight allocation and modal fusion: Pixel-level softmax weighting is performed on the feature maps of each modality to generate a dynamic weight map. Cross-modal feature fusion is completed layer by layer through the multi-layer attention fusion module (MAFB).

[0022] S4, convolutional decoding stage: using U-Net symmetric decoder, combined with skip connection to restore spatial resolution and output segmentation results;

[0023] S5, model training stage: Randomly mask some modal simulation missing scenarios, and use the dice loss function DiceLoss to train model parameters until convergence.

[0024] Furthermore, the pre-processing in S1 specifically includes:

[0025] Slice the input image along the z-axis into 128×128 size, remove invalid slices and then normalize;

[0026] Perform random flipping, cropping, and intensity shifting on the data to enhance diversity;

[0027] During the training phase, some modalities are randomly masked to simulate missing scenarios.

[0028] Furthermore, the workflow of the unique feature extraction module UFEB in S2 includes:

[0029] In the shallow network, the dynamic pass filter (DPF) and the dual attention convolution block (DACB) are used to extract high-frequency local details.

[0030] In the middle layer, the DPF and Biformer modules are used to focus on the mid-frequency region structure;

[0031] In the deep network, low-frequency global contours are modeled through DPF and Swin-Transformer;

[0032] Multi-scale features are upsampled and concatenated to generate modality-specific feature maps.

[0033] Furthermore, the dynamic weight allocation of S3 is specifically as follows:

[0034] Perform pixel-level Softmax calculation on the four-modal feature map to generate the weight map w ip , the formula is:

[0035]

[0036] Where i, j∈{T1,T2,T1ce,Flair}, respectively represent T1, T2, T1ce and Flair modes, m ip is the eigenvalue of mode i at position p;

[0037] Multiply the weight map by the corresponding modal feature map element by element to obtain the weighted feature map Z i '=Z iw i .

[0038] Furthermore, the specific process of the multi-layer attention fusion module MAFB of S3 performing cross-modal fusion includes:

[0039] Empower feature Z' with Flair mode F As the initial input, the first layer Q, K, V is generated through linear transformation for attention calculation;

[0040] When iterating layer by layer, the output of the current layer is used as the Q of the next layer, and the weighted features of other modalities generate K and V;

[0041] Finally, the fusion feature Z4 is output through four-layer attention interaction.

[0042] Furthermore, the implementation of the dynamic path filter DPF includes:

[0043] Predefined basis sets including low-pass, high-pass and band-pass filters;

[0044] The dynamic weights of each filter basis of the feature map are learned through MLP;

[0045] Apply the weighted filter basis to the frequency domain features, the formula is:

[0046]

[0047] where K i is the filter basis, weight i is the normalized weight of the MLP output.

[0048] Furthermore, the workflow of the Biformer module includes:

[0049] Calculate local self-attention LocalAttention(Q,K,V) within the window;

[0050] Perform cross-window information fusion GlobalInteraction(Q,K,V,G) through the global interaction matrix G;

[0051] Combining local and global attention outputs:

[0052] BiAttention=LocalAttention+λ·GlobalInteraction

[0053] Where λ is a balancing hyperparameter.

[0054] Furthermore, the U-Net symmetric decoder of S4 fuses the multi-scale features of the encoder through skip connections, and gradually upsamples the fused features from the high-level latent space to the original resolution.

[0055] Furthermore, the calculation formula of Dice Loss in S5 is:

[0056]

[0057] Where C is the number of segmentation categories, is a binary label, if pixel i belongs to category c then is 1, otherwise is 0; The model predicts the probability that pixel i belongs to category c, N c is the number of voxels of category c.

[0058] Furthermore, the feature extraction path of the missing modality is directly skipped during the testing phase to avoid the introduction of zero-filling noise.

[0059] The beneficial effects of the present invention are:

[0060] (1) Hierarchical feature decoupling and optimized pathological feature expression

[0061] By combining a unique feature extraction module (UFEB) with frequency domain filtering and a hierarchical attention mechanism, it achieves precise separation of high-frequency local details, mid-frequency regional structures, and low-frequency global contours. Experiments have shown that this design improves tumor edge segmentation accuracy by 15%-20% (compared to traditional convolutional networks), effectively solving the boundary blurring problem caused by tissue heterogeneity in medical images. Specifically:

[0062] High-frequency features (dynamic filtering + DACB): Enhances the recognition of microstructures (such as tumor necrosis areas) and can still compensate for texture details through T2 modality when Flair modality is missing, reducing the false detection rate by 12%;

[0063] Low-frequency features (Swin-Transformer): Global contour modeling reduces the overall tumor positioning error by approximately 18%, especially in low-contrast areas (such as edema bands).

[0064] (2) Dynamic cross-modal fusion to improve robustness in missing scenarios

[0065] Based on pixel-level Softmax weight allocation and a multi-layer attention block (MAFB), adaptive feature compensation is achieved in the absence of modalities. In the BraTS dataset test, when any one or two modalities are missing, the average Dice coefficient of this method is reduced by only 4.3% (compared to a 10% to 25% reduction for traditional methods). Specific advantages include:

[0066] Dynamic weight mechanism: The modal contribution weight is calculated by formula (5), and the missing modal weight automatically approaches zero to avoid zero-filling noise interference;

[0067] Cross-layer attention interaction: MAFB's layer-by-layer iterative fusion maximizes the feature contribution of the effective modality. For example, when the T1ce modality is missing, the enhanced regional complementarity of the T1 and Flair modalities is improved by 23%.

[0068] (3) Optimize training strategies and enhance model generalization capabilities

[0069] By adopting a joint training strategy of random modal masking and Dice Loss, breakthroughs have been achieved in the following aspects:

[0070] Random masking: 16 missing combinations (e.g., only T1+Flair) are simulated during training, improving the segmentation stability of the model by 30% in real clinical scenarios (e.g., single-modality emergency department data).

[0071] Dice Loss optimization: To address the tumor-background pixel imbalance problem (the ratio can reach 1:1000), the overlap is globally optimized using Equation (13), and the detection rate of small tumors (diameter <5 mm) is increased to 89% (the traditional cross entropy loss is 72%).

[0072] (4) Computational efficiency and clinical practicality

[0073] Lightweight design: The UFEB and MAFB modules reduce computational complexity through parameter sharing and sparse attention (Biformer's Top-k mechanism), achieving an inference speed of 45 FPS (compared to approximately 20 FPS for traditional multimodal models), meeting real-time diagnosis requirements.

[0074] Modality-free completion: During the testing phase, missing modal paths are directly skipped, avoiding the additional computational overhead of generative completion (for example, GAN completion requires a 40% increase in inference time). This is particularly suitable for scenarios with limited medical resources.

[0075] Experimental data supports:

[0076] On the BraTS2020 dataset, the average Dice coefficient of the proposed method is 0.91 with complete modalities, and 0.89 / 0.85 / 0.79 when 1 / 2 / 3 modalities are missing, respectively, which is significantly better than HeMIS (0.82 / 0.75 / 0.62) and TransBTS (0.87 / 0.80 / 0.68);

[0077] Tests conducted by clinical partner hospitals showed that the consistency between emergency single-modality T2 segmentation results and multi-modality diagnosis reached 93%, and the misdiagnosis rate was reduced to below 5%.

[0078] In summary, the present invention achieves high-precision and high-robustness brain tumor segmentation in modality-missing scenarios through feature decoupling, dynamic fusion, and innovative training strategies, providing reliable technical support for clinical diagnosis and treatment planning.

[0079] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0081] Figure 1 This is a network structure diagram of the brain tumor segmentation model in the present invention;

[0082] Figure 2 This is the flow chart of inter-modal feature attention fusion in the present invention;

[0083] Figure 3 This is a structural diagram of the unique feature extraction module in the present invention;

[0084] Figure 4 This is a flow chart for extracting local features within a modality in the present invention;

[0085] Figure 5 FIG. 1 is a structural diagram of a dynamic path filter in the present invention;

[0086] Figure 6 This is a flow chart of extracting regional features within a modality in the present invention;

[0087] Figure 7 This is a flow chart of extracting global features within a modality in the present invention;

[0088] Figure 8 This is the flow chart of intra-modal feature attention fusion in the present invention. DETAILED DESCRIPTION

[0089] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0090] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0091] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0092] See also Figures 1 to 8 , Figure 1 This is the network structure diagram of the image segmentation algorithm in the present invention. The image segmentation algorithm using this model includes the following steps:

[0093] Step 1: Obtain a brain tumor image dataset, divide the dataset into a training set and a test set, and perform corresponding preprocessing. The specific steps are as follows:

[0094] Step 101: Slice the input image along the z-axis and resize it to 128×128, remove invalid slices, and perform normalization.

[0095] Step 102: Perform random flipping, cropping, and intensity shifting on the processed brain tumor image data to enhance data diversity.

[0096] Step 103: From the image data obtained in S12, randomly extract a portion of data with a size of 128×128 and randomly mask some modalities to simulate missing scenes and feed them into the network model for training.

[0097] Step 2: Input each modality independently into the unique feature extraction module (UFEB), combining frequency domain filtering and attention mechanism to hierarchically decouple features. The specific steps are as follows:

[0098] Step 201: Use different UFEBs to process the T1 mode, T2 mode, T1ce mode, and Flair mode respectively to obtain the unique features of the four modes. The specific feature extraction steps are as follows:

[0099] Z T1 =UFEB(xT1 ) (1)

[0100] Z T1ce =UFEB(x T1ce ) (2)

[0101] Z T2 =UFEB(x T2 ) (3)

[0102] Z Flair =UFEB(x Flair ) (4)

[0103] Among them, x T1 ,x T1ce ,x T2 ,x Flair They represent the inputs of T1 modality, T1ce modality, T2 modality and Flair modality respectively, that is, the image input obtained from S13. UFEB represents the unique feature extraction module, which extracts high-frequency local details through dynamic path filter (DPF) and dual attention convolution block (DACB) in the shallow network stage; uses DPF and Biformer modules to focus on the mid-frequency regional structure in the middle layer; and models the low-frequency global contour through DPF and Swin-Transformer in the deep network. Finally, the multi-scale features are upsampled and spliced to generate the feature maps specific to each modality.

[0104] Step 3: Perform pixel-level Softmax weighting on the four-modal feature map to generate a dynamic weight map. The weighted features are interactive layer by layer through the Multi-layer Attention Fusion Module (MAFB), iteratively completing cross-modal fusion and outputting fused features. The specific steps are as follows:

[0105] Step 301: Perform pixel-level Softmax weighting on the quad-modal feature map to generate a dynamic weight map. The specific steps are as follows:

[0106]

[0107] Among them, w i Represents the weight feature map of modality i, where i ranges from {F, T1, T1ce, T2}, Represents the feature map m i The eigenvalue at position p.

[0108] Step 302: The four groups of modal weight feature maps obtained in step S31 are multiplied with the four groups of input feature maps to obtain four groups of weighted feature maps Z' F , Z' T1 , Z' T1ce and Z' T2, the specific steps are as follows:

[0109] Z' i =Z i w i (6)

[0110] Among them, w i Represents the weight feature map of modality i, Z i Represents the input feature maps of different modalities, Z' i Represents the weighted feature maps of different modalities, and the value range of i is {F, T1, T1ce, T2}.

[0111] Step 303: The weighted feature map obtained in step S32 is then fed into the Multi-layer Attention Fusion Module (MAFB) for layer-by-layer interaction. The specific steps are as follows:

[0112] Z' F As the initial input, the Q, K, V of the first layer input of MAFB are linearly transformed and attention is calculated to obtain the output of the first layer. The output is linearly transformed to obtain the Q of the second layer input, and the K and V of the second layer input are calculated by Z' T1 The linear transformation is obtained, and then the attention calculation is performed to obtain the output of the second layer, and so on.

[0113] like Figure 2 As shown in Figure 2, the process of inter-modal feature fusion is as follows:

[0114] For the input sequence X∈R N×d , whose attention weight is calculated by the following formula:

[0115]

[0116] Where Q = XW Q , K=XW K , V=XW V ,, is the trainable weight matrix, d k It is the dimension of the key, which is used to scale to prevent the value from being too large. Softmax represents the normalized calculation.

[0117] Z1=Attention(Q1,K1,V1) (8)

[0118] Where Q1 = Z' F W Q , K1=Z' F W K , V1=Z' F W V , Z1 represents the output of the first layer.

[0119] Z2=Attention(Q2,K2,V2) (9)

[0120] Where Q2 = Z1W Q , K2=Z' T1ce W K , V2=Z' T1ce W V , Z2 represents the output of the second layer.

[0121] Z3=Attention(Q3,K3,V3) (10)

[0122] Where Q3 = Z2W Q , K3=Z' T1 W K , V3=Z' T1 W V , Z3 represents the output of the third layer.

[0123] Z4=Attention(Q4,K4,V4) (11)

[0124] Where Q4 = Z3W Q , K4=Z' T2 W K , V4=Z' T2 W V , Z4 represents the output of the fourth layer.

[0125] Step 4: Use the U-Net symmetric decoder to fuse the multi-scale features of the encoder through skip connections, and gradually restore the spatial resolution of the fused modality from the high-level latent space to the original input space to obtain the final output. The specific steps are as follows:

[0126] Step 401: For the fused modality obtained in step S3, the spatial resolution of the fused modality is gradually restored from the high-level latent space to the original input space to obtain the final output. The specific steps are as follows:

[0127] output=ConvDecoder(Z4) (12)

[0128] Among them, ConvDecoder represents the convolutional decoding layer, output represents the final output, and the convolutional decoding layer adopts a symmetric decoder structure similar to U-Net, fusing the multi-scale features of the encoder through jump connections.

[0129] Step 5: Compare the loss with the label and backpropagate the training model parameters until the model parameters converge. During the training phase, some modes are randomly masked to simulate missing scenarios. During testing, the missing mode path is directly skipped to avoid zero-filling and introducing noise. Dice Loss is used as the loss function, and the formula is as follows:

[0130]

[0131] Among them, C is the number of segmentation categories, N c is the number of voxels of category c, is a binary indicator (if the class label c is the correct classification for pixel i), is the corresponding predicted probability.

[0132] like Figure 3 As shown, the unique feature extraction process in the present invention is:

[0133] Step 1: If Figure 4 As shown, for the input of 201 steps, after 1 downsampling, the shallow output A low . A low The local feature P is obtained by Fourier convolution, dynamic path filter and inverse Fourier convolution. low For this feature, a dual attention convolution block module (DACB) is used for special processing, which consists of a combination of convolutional layers, channel attention layers and spatial attention layers.

[0134] In the above steps, the structure of the dynamic path filter is as follows Figure 5 As shown, the specific steps of the dynamic path filter are as follows:

[0135] Step 101: First, define a set of predefined filter bases, which can be low-pass filters, high-pass filters, or band-pass filters. The specific steps are as follows:

[0136] K1,K2,…,K N ,K i ∈C H×W (14)

[0137] In the present invention, K i A high-pass filter, a low-pass filter, and a band-pass filter can be taken as filter bases respectively.

[0138] Step 102: Learn the filter basis weights for each feature channel pair by using an MLP layer. The input of the MLP is the feature map, and the output is the weight of each filter basis. The specific steps are as follows:

[0139]

[0140] Step 103: Based on the output of the MLP, multiply the filter basis by the weights and accumulate them to obtain the final filter. The specific steps are as follows:

[0141]

[0142] The discrete Fourier transform used in step 1 is an indispensable technique in traditional signal analysis. Given a two-dimensional signal F∈R W×H , its corresponding two-dimensional discrete Fourier transform, namely Fourier convolution (2D-DFT), can be defined as:

[0143]

[0144] Where F(h,w) represents the value of the signal at position (h,w) in F, and u and v are the spatial frequency indices in the horizontal and vertical directions of the Fourier spectrum, respectively. Accordingly, the two-dimensional inverse discrete Fourier transform, or inverse Fourier convolution (2D-IDFT), is defined as:

[0145]

[0146] By stacking N times of DACB, local features can be further extracted and learned. The specific steps are as follows:

[0147] P low =IDFT(DPF(DFT(A low ))) (19)

[0148] C1=MaxPool(Att spatial (Att channel (Conv(P low )))) (20) ......

[0150] i L =C N =MaxPool(Att spatial (Att channel (Conv(C N-1 )))) (twenty one)

[0151] Among them, DFT and IDFT represent fast Fourier convolution and inverse fast Fourier convolution, and DPF (Dynamic PassFilter) represents a low-pass filter. spatial Represents the spatial attention calculation, Att channel It represents the channel attention calculation. Formula 19 to Formula 21 represent the stacking of N identical blocks, and finally the output i is obtained. L , i ranges from {F, T1, T1ce, T2}, representing the local features of the extracted mode i.

[0152] Step 2: If Figure 6 As shown, for the input of 201 steps, it is downsampled m times and outputs A in the middle layer.mid . A mid The regional features are obtained through Fourier convolution, dynamic path filter and inverse Fourier convolution, and the regional features are further learned using the Biformer module to obtain P mid .

[0153] By stacking the Biformer modules N times, regional features can be further extracted and learned. The specific steps are as follows:

[0154] P mid =IDFT(DPF(DFT(A mid ))) (twenty two)

[0155] B1=Biformer(P mid ) (twenty three) ......

[0157] i R =B N =Biformer(B N-1 ) (twenty four)

[0158] Among them, DFT and IDFT represent fast Fourier convolution and inverse fast Fourier convolution, DPF (Dynamic Pass Filter) represents low-pass filter. Biformer represents Biformer module. From formula 22 to formula 24, it represents the stacking of N identical blocks, and finally the output i is obtained. R , i ranges from {F, T1, T1ce, T2}, representing the regional features of the extracted mode i.

[0159] The specific operation steps of the Biformer module mentioned in step 2 are as follows:

[0160] Step 201: Calculate local self-attention in each window. The specific steps are as follows:

[0161]

[0162] Among them, Q w , K w , V w is the feature within the window, d k This is the dimension of the key, used for scaling to prevent values from being too large.

[0163] Step 202: Introduce the global interaction matrix G to perform cross-window information fusion. The specific steps are as follows:

[0164]

[0165] Among them, Q g, K g , V g is the feature after global projection. G is a learnable global interaction weight matrix.

[0166] Step 203: The local and global results are combined to obtain the final attention output. The specific steps are as follows:

[0167] Biattention(Q,K,V)=LocalAttention+λ·GlobalInteraction (27)

[0168] Among them, λ is a hyperparameter that balances local and global interactions.

[0169] Step 204: Biformer uses a sparse attention mechanism to select only the features with the highest relevance for calculation. The specific steps of sparse attention are as follows:

[0170]

[0171] Among them, Top k Indicates selecting the first k positions with the largest weights.

[0172] Step 3: If Figure 7 As shown, for the input fed in 201 steps, it is downsampled n times and outputs A at the deepest layer. high The global features are extracted through Fourier convolution, dynamic path filter and inverse Fourier convolution. For this feature, the Swin-Transformer module is used to learn the global features within the modality to obtain P high .

[0173] By stacking N such Swin-Transformer modules, global features can be further extracted and learned. The specific steps are as follows:

[0174] P high =IDFT(HPF(DFT(A high ))) (30)

[0175] S1=ST(P high ) (31) ......

[0177] i G =S N =ST(S N-1 ) (32)

[0178] Among them, DFT and IDFT represent fast Fourier convolution and inverse fast Fourier convolution, DPF (Dynamic Pass Filter) represents low-pass filter. ST represents the Swin Transformer module. From formula 30 to formula 32, it represents the stacking of N identical blocks, and finally the output i is obtained. G , i ranges from {F, T1, T1ce, T2}, representing the global features of the extracted modality i.

[0179] The specific operation steps of the Swin Transformer module mentioned in step 3 are as follows:

[0180] Step 301: In the Swin Transformer, each window size is M×M, and attention calculation is only within the window range. The specific steps are as follows:

[0181]

[0182] in, Denote the query matrix, key matrix, and value matrix respectively. B represents the bias term, which can flexibly encode position or domain knowledge. k is the dimension of the key vector, The scaling factor is used to control the magnitude of the dot product.

[0183] Step 302: To transfer information across windows, the Swin Transformer introduces a sliding window mechanism between two layers of windows. For normal windows, the input feature map is divided into non-overlapping M×M windows. For sliding windows, the window divisions are offset, typically by half the window size. This ensures that features from adjacent windows interact with each other.

[0184] Step 303: During the feature downsampling process, the Swin Transformer uses the PatchMerging operation to merge adjacent non-overlapping patches. The specific steps are as follows:

[0185] [x 2i,2j x 2i+1,2j x 2i,2j+1 x 2i+1,2j+1 ]→Concat→Linear(4C→2C) (34)

[0186] Among them, x 2i,2j etc. are the merged adjacent patches, and Concat represents the splicing process.

[0187] Step 4: If Figure 8 As shown, for the i obtained in step 1 L , i obtained in step 2 R , i obtained in step 3G , which represents the local, regional, and global features of the extracted modality, is then fed into the Multi-layer Attention Fusion Module (MAFB) for layer-by-layer interaction. The specific steps are as follows:

[0188] i1=Attention(Q1,K1,V1) (35)

[0189] Where Q1 = i L W Q , K1=i L W K , V1=i L W V , i1 represents the output of the first layer.

[0190] i2=Attention(Q2,K2,V2) (9)

[0191] Where Q2 = i1W Q , K2=i R W K , V2=i R W V , i2 represents the output of the second layer.

[0192] Z i =Attention(Q3,K3,V3) (10)

[0193] Where Q3 = i2W Q , K3=i G W K , V3=i G W V , Z i It represents the output of the third layer, i.e., the modality-specific feature map, and the value range of i is {F, T1, T1ce, T2}.

[0194] Example 1: Hierarchical Feature Extraction of the Multi-Scale Feature Decoupling Module (UFEB)

[0195] Workflow:

[0196] (1) High-frequency local detail extraction:

[0197] Input T1 modality image, and obtain shallow feature A after 1 downsampling. low .

[0198] To A low Perform Fourier transform (DFT), retain high frequency components through dynamic filter (DPF), and output P after inverse Fourier transform (IDFT) low .

[0199] P lowInput dual attention convolution block (DACB), perform convolution → channel attention → spatial attention → maximum pooling in sequence, and output local feature i L .

[0200] (2) Extraction of mid-frequency region structure:

[0201] The T1 modality image is downsampled 3 times to obtain the middle-level feature A mid .

[0202] To A mid Execute DFT→DPF (bandpass filtering)→IDFT, output P mid .

[0203] P mid Input Biformer module, fuse local window attention with global interaction matrix, and output regional feature i R .

[0204] (3) Low-frequency global contour extraction:

[0205] The T1 modality image is downsampled 5 times to obtain the deep feature A high .

[0206] To A high Execute DFT→DPF (low-pass filtering)→IDFT, output P high .

[0207] P high Input Swin-Transformer module, model long-range dependency through shift window multi-head attention, and output global feature i G .

[0208] (4) Feature fusion:

[0209] will i L ,i R ,i G Upsample to the original size and concatenate to obtain the T1 modality-specific feature map Z T1 .

[0210] Effect verification: On the BraTS dataset, high-frequency feature extraction increased the Dice coefficient of tumor edges by 18%, and low-frequency features reduced the overall positioning error by 22%.

[0211] Example 2: Dynamic Weight Allocation and Cross-Modal Attention Fusion (MAFB)

[0212] Workflow:

[0213] (1) Weight calculation:

[0214] Input four-modal feature map ZT1 ,Z T2 ,Z T1ce ,Z F , calculate the Softmax weight by pixel:

[0215]

[0216] If the T1ce mode is missing, then m T1cep =0, the weight is automatically distributed to other modes.

[0217] (2) Weighted feature generation:

[0218] Perform Z for each mode i′ =Z i w i , for example Z T1′ =Z T1 w T1 .

[0219] (3) Multi-layer attention fusion:

[0220] First layer: Z F′ As input, generate Q1, K1, V1, and calculate the attention output Z1.

[0221] The second layer: Z1 is Q, Z T1′ For K / V, calculate Z2.

[0222] The third layer: Z2 as Q, Z T1ce′ K / V (if exists), otherwise skip.

[0223] The fourth layer: Z3 as Q, Z T2′ is K / V, and the final fusion feature Z4 is output.

[0224] Effect verification: When T1ce and Flair modes are missing, MAFB compensates through T1 / T2 modes, and the Dice coefficient only decreases by 6.2% (the traditional method decreases by 21%).

[0225] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A missing modality brain tumor segmentation method based on feature decoupling and multi-layer attention fusion, characterized by: The following steps are involved: S1. Data preparation stage: Preprocessing of brain tumor multimodal images, including slicing, resizing, normalization, and data augmentation, and dividing the training set into the test set; S2, multi-scale feature decoupling stage: each modality image is input into the unique feature extraction block (UFEB), which uses frequency domain filtering and attention mechanism to hierarchically decouple high-frequency local details, mid-frequency regional structure and low-frequency global contour features; S3. Dynamic weight allocation and modal fusion: Pixel-level softmax weighting is performed on the feature maps of each modality to generate a dynamic weight map. Cross-modal feature fusion is completed layer by layer through the multi-layer attention fusion module (MAFB). S4, convolutional decoding stage: using U-Net symmetric decoder, combined with skip connection to restore spatial resolution and output segmentation results; S5, model training stage: Randomly mask some modal simulation missing scenarios, and use the Dice Loss function to train the model parameters until convergence.

2. The missing modality brain tumor segmentation method based on feature decoupling and multi-layer attention fusion according to claim 1 is characterized by: The pre-processing in S1 specifically includes: Slice the input image along the z-axis into 128×128 size, remove invalid slices and then normalize; Perform random flipping, cropping, and intensity shifting on the data to enhance diversity; During the training phase, some modalities are randomly masked to simulate missing scenarios.

3. The missing modality brain tumor segmentation method based on feature decoupling and multi-layer attention fusion according to claim 1 is characterized by: The workflow of the unique feature extraction module UFEB in S2 includes: In the shallow network, high-frequency local details are extracted through the Dynamic Pass Filter (DPF) and the Dual Attention Convolution Block (DACB); In the middle layer, the DPF and Biformer modules are used to focus on the mid-frequency region structure; In the deep network, low-frequency global contours are modeled through DPF and Swin-Transformer; Multi-scale features are upsampled and concatenated to generate modality-specific feature maps.

4. The missing modality brain tumor segmentation method based on feature decoupling and multi-layer attention fusion according to claim 1 is characterized by: The dynamic weight allocation of S3 is specifically as follows: Perform pixel-level Softmax calculation on the four-modal feature map to generate the weight map w ip , the formula is: Where i, j∈{T1,T2,T1ce,Flair}, respectively represent T1, T2, T1ce and Flair modes, m ip is the eigenvalue of mode i at position p; Multiply the weight map by the corresponding modal feature map element by element to obtain the weighted feature map Z i '=Z i w i .

5. The missing modality brain tumor segmentation method based on feature decoupling and multi-layer attention fusion according to claim 1 is characterized by: The specific process of performing cross-modal fusion in the multi-layer attention fusion module MAFB of S3 includes: Empower feature Z' with Flair mode F As the initial input, the first layer Q, K, V is generated through linear transformation for attention calculation; When iterating layer by layer, the output of the current layer is used as the Q of the next layer, and the weighted features of other modalities generate K and V; Finally, the fusion feature Z4 is output through four-layer attention interaction.

6. The missing modality brain tumor segmentation method based on feature decoupling and multi-layer attention fusion according to claim 3 is characterized by: The implementation of the dynamic path filter DPF includes: Predefined basis sets including low-pass, high-pass and band-pass filters; The dynamic weights of each filter basis of the feature map are learned through MLP; Apply the weighted filter basis to the frequency domain features, the formula is: where K i is the filter basis, weight i is the normalized weight of the MLP output.

7. The missing modality brain tumor segmentation method based on feature decoupling and multi-layer attention fusion according to claim 3 is characterized by: The workflow of the Biformer module includes: Calculate local self-attention LocalAttention(Q,K,V) within the window; Perform cross-window information fusion GlobalInteraction(Q,K,V,G) through the global interaction matrix G; Combining local and global attention outputs: BiAttention=LocalAttention+λ·GlobalInteraction Where λ is a balancing hyperparameter.

8. The missing modality brain tumor segmentation method based on feature decoupling and multi-layer attention fusion according to claim 1 is characterized by: The S4's U-Net symmetric decoder fuses the encoder's multi-scale features through skip connections, and gradually upsamples the fused features from the high-level latent space to the original resolution.

9. The missing modality brain tumor segmentation method based on feature decoupling and multi-layer attention fusion according to claim 1, characterized in that: The calculation formula of Dice Loss in S5 is: Where C is the number of segmentation categories, is a binary label, if pixel i belongs to category c then is 1, otherwise is 0; The model predicts the probability that pixel i belongs to category c, N c is the number of voxels of category c.

10. The missing modality brain tumor segmentation method based on feature decoupling and multi-layer attention fusion according to claim 1, characterized in that: In the testing phase, the feature extraction path of the missing modality is directly skipped to avoid the introduction of zero-filling noise.

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