Electronic microscope data enhancement method, device and system, and storage medium

Abstract

The invention discloses an electron microscope data enhancement method, device and system, and a storage medium. The method comprises the following steps: acquiring a real electron microscope image data set; the method comprises the following steps: constructing a special text prompt library according to a real electron microscope image data set, introducing a prompt embedding mechanism, and coding through a pre-training model to form a vector for condition guidance; the method comprises the following steps: initializing a diffusion model framework according to a real electron microscope image data set, simulating a forward diffusion process by adopting a Stable Diffusion pipeline in combination with singular value decomposition, and generating a degraded image; performing a reverse denoising process on the degraded image, reconstructing a high-quality image step by step through iteration time steps, and generating a composite image through integrated text guide embedded vector control; and mixing the synthesized image with the real image to form an expanded data set. By adopting the technical scheme of the invention, the problems of long time consumption, high cost, limited data set scale and insufficient diversity of traditional EM image data acquisition are solved.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, specifically relating to an electron microscope data enhancement method, device, system, and storage medium. Background Technology

[0002] In recent years, electron microscopy (EM) and advanced volume electron microscopy (vEM) have been widely used in resolving cellular ultrastructure, particularly in the life sciences. However, traditional EM image data primarily relies on experimental acquisition, a method with significant limitations. Experimental acquisition is time-consuming and costly, and its limited sample size and diversity result in a finite amount of available datasets, making it difficult to meet the training requirements of deep learning models. In particular, acquiring high-quality, high-resolution EM images is extremely challenging. While existing public datasets contain a large number of images, most are low-resolution and lack sufficient ultrastructural detail, making it difficult to support complex image processing tasks.

[0003] To overcome these challenges, using diffusion models to generate synthetic electron microscopy images as a data augmentation technique has become a viable solution. Diffusion models, by simulating image degradation and reconstruction processes, can generate realistic synthetic images, thereby expanding the dataset size. In particular, text-guided diffusion models can precisely control the ultrastructural features of the generated images, such as the double membrane and cristae morphology of mitochondria, providing diverse and high-quality samples for model training. This method not only reduces data acquisition costs but also effectively improves the model's generalization ability and the accuracy of downstream tasks. However, existing technologies lack optimized diffusion models for the characteristics of EM images, and the generated synthetic images still differ from real images in terms of detail consistency. There is an urgent need to develop an efficient and accurate generation method to support the further development of EM image analysis. Furthermore, existing text-guided mechanisms are mostly trained on general image datasets and lack dedicated cue engineering for biological ultrastructures, resulting in insufficient morphological diversity of the generated membrane structures and difficulty in covering the variability of real biological samples. Although there are existing methods for enhancing traditional image data using diffusion models, they are quite different from the nanoscale biological ultrastructures of EM images. The EM images have problems such as high noise interference, fine structure resolution under low contrast, and 3D reconstruction distortion caused by Z-axis anisotropy in vEM, which makes it difficult to directly transfer traditional methods to the EM field. Summary of the Invention

[0004] To address the problems of existing technologies, this invention provides an electron microscope (EM) image data enhancement method, device, system, and storage medium, solving the issues of long acquisition time, high cost, limited dataset size, and insufficient diversity in traditional EM image data. This invention expands the dataset size and enhances data diversity by generating realistic synthetic electron microscope images, significantly improving model training efficiency and the accuracy of downstream biological analysis, further advancing EM image analysis technology. This invention innovatively designs degradation simulation and guidance mechanisms specifically for the biological ultrastructure of EM images, focusing on the unique spatial anisotropy and noise distribution of EM, achieving a dedicated innovation for the EM field and enhancing the practical value of the generated images in biological research.

[0005] To achieve the above objectives, the present invention provides the following solution: An electron microscopy data enhancement method, comprising: Step S1: Obtain a dataset of real electron microscope images; Step S2: Based on the real electron microscope image dataset, construct a dedicated text prompt word library, and introduce a prompt embedding mechanism to encode vectors through a pre-trained model for conditional guidance; Step S3: Based on the real electron microscope image dataset, initialize the diffusion model framework, use the StableDiffusion pipeline, and combine singular value decomposition to simulate the forward diffusion process to generate degraded images; Step S4: Perform an inverse denoising process on the degraded image, gradually reconstruct a high-quality image through iterative time steps, and integrate text-guided embedding vector control to generate a synthetic image; Step S5: Mix the synthetic image with the real image to form an augmented dataset.

[0006] Preferably, in step S1, images of membrane-bound organelles covering multiple cell types and species are collected from multiple EM datasets.

[0007] Preferably, the method further includes: step S6: using an unsupervised learning framework to train the generative model and verify the quality of the generated image.

[0008] The present invention also provides an electron microscope data enhancement device, comprising: The first processing module is used to acquire a dataset of real electron microscope images; The second processing module is used to build a dedicated text prompt library based on real electron microscope image datasets, and introduce a prompt embedding mechanism to encode vectors through a pre-trained model for conditional guidance. The third processing module is used to initialize the diffusion model framework based on the real electron microscope image dataset, and uses the Stable Diffusion pipeline combined with singular value decomposition to simulate the forward diffusion process and generate degraded images. The fourth processing module is used to perform an inverse denoising process on the degraded image, gradually reconstructing a high-quality image through iterative time steps, and integrating text-guided embedding vector control to generate a synthetic image; The fifth processing module is used to mix the synthetic images with real images to form an expanded dataset.

[0009] Preferably, the first processing module acquires images of membrane-bound organelles covering multiple cell types and species from various EM datasets.

[0010] Preferably, it also includes: a sixth processing module for training a generative model and verifying the quality of the generated images using an unsupervised learning framework.

[0011] The present invention also provides an electron microscope data enhancement system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program performs an electron microscope data enhancement method when executed by the processor.

[0012] The present invention also provides a storage medium storing a computer program that executes an electron microscope data enhancement method when running.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. By integrating singular value decomposition and posterior sampling, this system achieves efficient generation of realistic synthetic images for data augmentation. Leveraging the advancements in deep learning for image generation, it provides an efficient solution for EM image data augmentation. Compared to traditional methods, this invention can significantly expand the dataset size while maintaining high quality. Its innovation for EM images lies in simulating the degradation of biological ultrastructures.

[0014] 2. Employing a text-guided mechanism, the system can precisely control the ultrastructure of the generated images and simulate the degradation process through singular value decomposition, providing accurate image information and thus enabling precise control over data augmentation. Once the generated images meet the required quality standards, they can be used for training to improve the model's generalization ability.

[0015] 3. The generation mechanism, which focuses on the smallest computational unit, combines singular value decomposition to provide feedback on the generation results, facilitating rapid problem optimization. This technical solution tracks the quality of each generation stage in greater detail, effectively improving the efficiency and accuracy of the entire data augmentation process.

[0016] 4. This invention has the advantages of high cost-effectiveness and low implementation difficulty. It is applicable to the field of EM image analysis, can effectively reduce experimental costs, and can also reduce the uncertainty caused by insufficient data, thereby improving the overall training efficiency and quality assurance capability. Attached Figure Description

[0017] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the electron microscope data enhancement method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the forward diffusion process; Figure 3 This is a schematic diagram of the reverse diffusion process; Figure 4 This is a schematic diagram of the synthesized image; Figure 5 This is a schematic diagram of downstream applications. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] Example 1 like Figure 1As shown, this invention provides an electron microscopy data augmentation method for generating synthetic electron microscopy images for data augmentation, supporting downstream biological analysis tasks such as organelle segmentation and repair. It utilizes a Stable Diffusion pipeline to generate synthetic electron microscopy images and significantly expands the dataset through data augmentation techniques to support subsequent model training and image analysis optimization. Text labels are used to precisely control the ultrastructural features of the generated images, such as mitochondrial double membranes and cristae morphology, and diverse augmentation operations are combined to improve data scale and robustness. This invention is based on a diffusion model of forward degradation and reverse reconstruction processes. The forward process simulates noise addition and structural degradation in EM images, while the reverse process restores high-quality images through iterative denoising. Specifically, it includes: Step S1: Construct a real electron microscope image dataset. Images of membrane-bound organelles covering multiple cell types and species are acquired from public and private EM collections. Feature annotation is performed during dataset construction. The annotation process involves manually verifying ultrastructural details, such as cristae bending angles and double membrane thicknesses. To enhance the representativeness of the dataset, data preprocessing steps are introduced, such as normalization: image pixel values ​​are standardized using the formula... Where x is the original pixel value, This is the mean of the dataset (calculated as the average of all pixels). The standard deviation is calculated as the square root of the pixel variance. Additionally, contrast enhancement is performed using Adaptive Histogram Equalization (CLAHE) to highlight low-contrast areas in the EM (Earth Element), with parameters clipLimit=2.0 and tileGridSize= The dataset covers multiple modalities, including TEM (transmission electron microscopy) and SEM (scanning electron microscopy) images, and incorporates slice images to enhance context. This step provides foundational data for subsequent text-guided and diffusion models, ensuring the biological realism of the generated images. Through this EM-specific preprocessing, this invention innovatively enhances the dataset's sensitivity to nanoscale structures.

[0022] Step S2: Design a text-guided prompting system. Based on feature annotations from a real dataset, construct a dedicated text prompt library to control the generated images. The text prompt library is built based on real image features and includes both positive and negative prompts to ensure the diversity and realism of the generated images. Text prompts include keywords describing membrane structure morphology, such as "high-resolution mitochondrial double membrane structure, cristae clearly visible, low-noise EM image, complete nanometer-level details." To optimize the prompting effect, a prompt embedding mechanism is introduced, converting the text into an embedding vector e, which is then encoded using the CLIP model. Here, t represents the text prompts, and the CLIP model is pre-trained on a biological image dataset with 512 dimensions. The prompt library covers various variations, such as "swollen mitochondrial cristae, strong double membrane continuity" and "distorted cristae structure, low-contrast background," to increase the diversity of generated images, totaling approximately 10,000 prompts. The prompt library is constructed based on real image statistics, using natural language processing tools to extract high-frequency biological terms, and combining them with negative prompts such as "blurred, low contrast, distorted structure" to avoid poor generation. The negative prompt weight is set to 0.5 to ensure that the generation avoids common EM artifacts. This step ensures the accuracy of the text guidance, making the generated images optimized for EM ultrastructures. Combining the above text guidance lexicon, a pre-trained model for generating images using the diffusion model is obtained through low-rank fine-tuning, based on the Stable Diffusion V1.5 model.

[0023] Step S3: Initialize the diffusion model framework, using the Stable Diffusion pipeline and singular value decomposition to simulate the forward diffusion process, generating a degraded image, such as... Figure 2 As shown. Specifically, the input real image dataset is transformed into a matrix X and singular value decomposition is performed: Where U and V are orthogonal matrices, This is a diagonal matrix with singular values. The singular values ​​are adjusted... To control structural degradation and noise levels, for example, k = min(m,n) / 2 singular values ​​are truncated to simulate low resolution, and a singular value attenuation factor of 0.2 is added in the Z-axis direction for both EM and vEM. Gradually adding Gaussian noise yields the following degraded image: ,in, The original images are from a real image dataset. , , The noise scheduling parameter is linearly adjusted from 1e-4 to 0.02, with the upper limit adjusted to 0.03 for high noise in EM images. This process simulates the real degradation of EM images, such as high noise, blurring, and anisotropy (Z-axis noise variance increases by 20%). Singular value decomposition is parameterized, and the singular value threshold is solved by least squares optimization to ensure that the degradation matches the EM physical imaging model. This step innovatively simulates the anisotropic degradation specific to EMs, adjusting the singular value truncation along the Z-axis and introducing EM physical imaging constraints: the singular value attenuation factor is based on the vEM interlayer spacing, and the formula is... Where λ is the attenuation rate (0.1 / μm) and z is the Z-axis depth, this ensures that the degradation matches the biological 3D structure, improving the realism of the simulation. Model initialization was performed on an NVIDIA GeForce RTX 3060 GPU with a batch size of 128. Memory optimization used mixed-precision FP16 to ensure efficient simulation of the degradation process. This step lays the foundation for the inverse process, ensuring that the degraded image conforms to the characteristics of EM imaging, and innovatively introduces an EM-specific singular value decomposition truncation strategy to simulate the loss of continuity in biological structures.

[0024] Step S4: Perform an inverse denoising process on the degraded noisy image, gradually reconstructing a high-quality image through iterative time steps, integrating text-guided embedding vectors for control generation, such as... Figure 3 As shown. The principle of the reverse process is based on the posterior sampling distribution, and the prediction noise is... ,in Here, represents the model parameters, e represents the text embedding, the denoising process uses the U-Net framework, and the model uses the pre-trained model obtained in step S2. The iterative update is as follows: ,in , = The generated images support various ultrastructural variations. This step enables high-quality reconstruction under textual control, improving the recovery of EM nanometer-level details.

[0025] Step S5: Apply data augmentation strategies to mix the synthetic images with real images to form an augmented dataset. Augmentation operations include random cropping (size 0.8-1.0 of the original image), flipping (horizontal / vertical probability 0.5), and brightness adjustment. ,in The enhancement strategy includes anisotropy simulation; for vEM images, additional noise is added along the Z-axis to match true anisotropy. Further, an affine transformation is added to simulate EM sample deformation, with the transformation matrix being... , , ~U(-10,10). To verify the enhancement effect, the quality of the synthesized image is evaluated by calculating the Structural Similarity Index (SSIM): , , L=255, target SSIM>0.95. For example... Figure 4 The comparison between real and synthetic images ensures consistency in ultrastructure. This step ensures data diversity, supports model training, and innovatively introduces Z-axis enhancement for EM by stacking the generated image and real slices along the Z-axis to simulate a vEM sequence. The formula is as follows: ,in Z-axis noise (σ=0.05) is used to ensure enhanced consistency of 3D data.

[0026] Step S6: Adjust the loss function to improve the quality of the synthesized image. The original loss function is simple noise prediction loss. Training was performed on a real membrane-bound organelle dataset with a batch size of 128, a learning rate of 1e-4 (decreasing with cosine annealing), and 100,000 iterations. Training was monitored using a validation set to evaluate generation quality, with an FID score <10 calculated every 5000 steps, and a stopping condition of loss convergence <0.01. A variant loss was further introduced. ,in KL divergence ensures the generated distribution matches the true EM distribution, q is the model posterior, and p is the prior N(0,I). Training uses the AdamW optimizer with weight decay of 1e-2 to avoid overfitting. After training, the generated images are used for downstream tasks such as 2D mitochondrial segmentation and 3D reconstruction. Figure 5 As shown. This innovation ensures that the model is optimized for downstream EM tasks, such as 3D reconstruction volume calculation. Added multi-task joint loss: ,in To mitigate variant loss, the model's applicability in EM biological analysis is improved. Using the enhanced data from this invention, segmentation accuracy is significantly improved, and the error in calculating the surface area of ​​3D reconstruction is effectively reduced.

[0027] Example 2 The present invention also provides an electron microscope data enhancement device, comprising: The first processing module is used to acquire a dataset of real electron microscope images; The second processing module is used to build a dedicated text prompt library based on real electron microscope image datasets, and introduce a prompt embedding mechanism to encode vectors through a pre-trained model for conditional guidance. The third processing module is used to initialize the diffusion model framework based on the real electron microscope image dataset, and uses the Stable Diffusion pipeline combined with singular value decomposition to simulate the forward diffusion process and generate degraded images. The fourth processing module is used to perform an inverse denoising process on the degraded image, gradually reconstructing a high-quality image through iterative time steps, and integrating text-guided embedding vector control to generate a synthetic image; The fifth processing module is used to mix the synthetic images with real images to form an expanded dataset.

[0028] As one embodiment of the present invention, the first processing module acquires images of membrane-bound organelles covering multiple cell types and species from multiple EM datasets.

[0029] As one embodiment of the present invention, it further includes: a sixth processing module, used to train a generative model and verify the quality of the generated image using an unsupervised learning framework.

[0030] Example 3 The present invention also provides an electron microscope data enhancement system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program performs an electron microscope data enhancement method when executed by the processor.

[0031] Example 4 The present invention also provides a storage medium storing a computer program that executes an electron microscope data enhancement method when running.

[0032] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for enhancing electron microscopy data, characterized in that, include: Step S1: Obtain a dataset of real electron microscope images; Step S2: Based on the real electron microscope image dataset, construct a dedicated text prompt library, and introduce a prompt embedding mechanism to encode vectors through a pre-trained model for conditional guidance; Step S3: Based on the real electron microscope image dataset, initialize the diffusion model framework, use the StableDiffusion pipeline, and combine singular value decomposition to simulate the forward diffusion process to generate degraded images; Step S4: Perform an inverse denoising process on the degraded image, gradually reconstruct a high-quality image through iterative time steps, and integrate text-guided embedding vector control to generate a synthetic image; Step S5: Mix the synthetic image with the real image to form an augmented dataset.

2. The electron microscope data enhancement method as described in claim 1, characterized in that, In step S1, images of membrane-bound organelles covering multiple cell types and species are collected from various EM datasets.

3. The electron microscope data enhancement method as described in claim 1, characterized in that, It also includes: Step S6: Using an unsupervised learning framework, train the generative model and verify the quality of the generated images.

4. An electron microscope data enhancement device, characterized in that, include: The first processing module is used to acquire a dataset of real electron microscope images; The second processing module is used to build a dedicated text prompt library based on real electron microscope image datasets, and introduce a prompt embedding mechanism to encode vectors through a pre-trained model for conditional guidance. The third processing module is used to initialize the diffusion model framework based on the real electron microscope image dataset, and uses the Stable Diffusion pipeline combined with singular value decomposition to simulate the forward diffusion process and generate degraded images. The fourth processing module is used to perform an inverse denoising process on the degraded image, gradually reconstructing a high-quality image through iterative time steps, and integrating text-guided embedding vector control to generate a synthetic image; The fifth processing module is used to mix the synthetic images with real images to form an expanded dataset.

5. The electron microscope data enhancement device as described in claim 4, characterized in that, The first processing module acquires images of membrane-bound organelles covering multiple cell types and species from various EM datasets.

6. The electron microscope data enhancement device as described in claim 5, characterized in that, Also includes: The sixth processing module is used to train the generative model and verify the quality of the generated images using an unsupervised learning framework.

7. An electron microscope data enhancement system, characterized in that, include: A memory and a processor, wherein the memory stores a computer program executed by the processor, the computer program performing the electron microscopy data enhancement method as described in any one of claims 1-3 when executed by the processor.

8. A storage medium, characterized in that, The storage medium stores a computer program that, when executed, performs the electron microscope data enhancement method as described in any one of claims 1-3.

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