When applying deep learning algorithms to magnetic resonance (MR) image segmentation, a large number of annotated images are required as data support. However, the specificity of MR images makes it difficult and costly to acquire large amounts of annotated image data. To reduce the dependence of MR image segmentation on a large amount of annotated data, this paper proposes a meta-learning U-shaped network (Meta-UNet) for few-shot MR image segmentation. Meta-UNet can use a small amount of annotated image data to complete the task of MR image segmentation and obtain good segmentation results. Meta-UNet improves U-Net by introducing dilated convolution, which can increase the receptive field of the model to improve the sensitivity to targets of different scales. We introduce the attention mechanism to improve the adaptability of the model to different scales. We introduce the meta-learning mechanism, and employ a composite loss function for well-supervised and effective bootstrapping of model training. We use the proposed Meta-UNet model to train on different segmentation tasks, and then use the trained model to evaluate on a new segmentation task, where the Meta-UNet model achieves high-precision segmentation of target images. Meta-UNet has a certain improvement in mean Dice similarity coefficient (DSC) compared with voxel morph network (VoxelMorph), data augmentation using learned transformations (DataAug) and label transfer network (LT-Net). Experiments show that the proposed method can effectively perform MR image segmentation using a small number of samples. It provides a reliable aid for clinical diagnosis and treatment.
Algorithms ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging

Aiming at the problems of missing important features, inconspicuous details and unclear textures in the fusion of multimodal medical images, this paper proposes a method of computed tomography (CT) image and magnetic resonance imaging (MRI) image fusion using generative adversarial network (GAN) and convolutional neural network (CNN) under image enhancement. The generator aimed at high-frequency feature images and used double discriminators to target the fusion images after inverse transform; Then high-frequency feature images were fused by trained GAN model, and low-frequency feature images were fused by CNN pre-training model based on transfer learning. Experimental results showed that, compared with the current advanced fusion algorithm, the proposed method had more abundant texture details and clearer contour edge information in subjective representation. In the evaluation of objective indicators, Q ^AB/F, information entropy (IE), spatial frequency (SF), structural similarity (SSIM), mutual information (MI) and visual information fidelity for fusion (VIFF) were 2.0%, 6.3%, 7.0%, 5.5%, 9.0% and 3.3% higher than the best test results, respectively. The fused image can be effectively applied to medical diagnosis to further improve the diagnostic efficiency.
Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Tomography, X-Ray Computed ; Magnetic Resonance Imaging/methods* ; Algorithms

Magnetic resonance (MR) imaging is an important tool for prostate cancer diagnosis, and accurate segmentation of MR prostate regions by computer-aided diagnostic techniques is important for the diagnosis of prostate cancer. In this paper, we propose an improved end-to-end three-dimensional image segmentation network using a deep learning approach to the traditional V-Net network (V-Net) network in order to provide more accurate image segmentation results. Firstly, we fused the soft attention mechanism into the traditional V-Net's jump connection, and combined short jump connection and small convolutional kernel to further improve the network segmentation accuracy. Then the prostate region was segmented using the Prostate MR Image Segmentation 2012 (PROMISE 12) challenge dataset, and the model was evaluated using the dice similarity coefficient (DSC) and Hausdorff distance (HD). The DSC and HD values of the segmented model could reach 0.903 and 3.912 mm, respectively. The experimental results show that the algorithm in this paper can provide more accurate three-dimensional segmentation results, which can accurately and efficiently segment prostate MR images and provide a reliable basis for clinical diagnosis and treatment.
Male ; Humans ; Prostate/diagnostic imaging* ; Image Processing, Computer-Assisted/methods* ; Magnetic Resonance Imaging/methods* ; Imaging, Three-Dimensional/methods* ; Prostatic Neoplasms/diagnostic imaging*

In order to address the issues of spatial induction bias and lack of effective representation of global contextual information in colon polyp image segmentation, which lead to the loss of edge details and mis-segmentation of lesion areas, a colon polyp segmentation method that combines Transformer and cross-level phase-awareness is proposed. The method started from the perspective of global feature transformation, and used a hierarchical Transformer encoder to extract semantic information and spatial details of lesion areas layer by layer. Secondly, a phase-aware fusion module (PAFM) was designed to capture cross-level interaction information and effectively aggregate multi-scale contextual information. Thirdly, a position oriented functional module (POF) was designed to effectively integrate global and local feature information, fill in semantic gaps, and suppress background noise. Fourthly, a residual axis reverse attention module (RA-IA) was used to improve the network's ability to recognize edge pixels. The proposed method was experimentally tested on public datasets CVC-ClinicDB, Kvasir, CVC-ColonDB, and EITS, with Dice similarity coefficients of 94.04%, 92.04%, 80.78%, and 76.80%, respectively, and mean intersection over union of 89.31%, 86.81%, 73.55%, and 69.10%, respectively. The simulation experimental results show that the proposed method can effectively segment colon polyp images, providing a new window for the diagnosis of colon polyps.
Humans ; Colonic Polyps/diagnostic imaging* ; Computer Simulation ; Electric Power Supplies ; Semantics ; Image Processing, Computer-Assisted

Medical image segmentation based on deep learning has become a powerful tool in the field of medical image processing. Due to the special nature of medical images, image segmentation algorithms based on deep learning face problems such as sample imbalance, edge blur, false positive, false negative, etc. In view of these problems, researchers mostly improve the network structure, but rarely improve from the unstructured aspect. The loss function is an important part of the segmentation method based on deep learning. The improvement of the loss function can improve the segmentation effect of the network from the root, and the loss function is independent of the network structure, which can be used in various network models and segmentation tasks in plug and play. Starting from the difficulties in medical image segmentation, this paper first introduces the loss function and improvement strategies to solve the problems of sample imbalance, edge blur, false positive and false negative. Then the difficulties encountered in the improvement of the current loss function are analyzed. Finally, the future research directions are prospected. This paper provides a reference for the reasonable selection, improvement or innovation of loss function, and guides the direction for the follow-up research of loss function.
Algorithms ; Image Processing, Computer-Assisted

Objective: To analyze the clinical and imaging characteristics of stage Ⅰ occupational cement pneumoconiosis patients. Methods: In October 2021, the data of patients with occupational cement pneumoconiosis diagnosed by the Third Hospital of Peking University from 2014 to 2020 were collected, and the data of the patients' initial exposure age, dust exposure duration, diagnosis age, incubation period, chest X-ray findings, lung function and other data were analyzed retrospectively. Spearman grade correlation was used for correlation analysis of grade count data. The influencing factors of lung function were analyzed by binary logistic regression. Results: A total of 107 patients were enrolled in the study. There were 80 male patients and 27 female patients. The inital exposure age was (26.2±7.7) years, the diagnosis age was (59.4±7.9) years, the dust exposure duration was (17.9±8.0) years, and the incubation period was (33.1±10.3) years. The initial dust exposure age and the dust exposure duration in female patients were less than those in men, and the incubation period was longer than that in men (P<0.05). The imaging analysis showed the small opacities as"pp"accounted for 54.2%. 82 patients (76.6%) had small opacities distributed in two lung areas. The lung areas distribution of small opacities in female patients was less than that in male patients (2.04±0.19 vs 2.41±0.69, P<0.001). There were 57 cases of normal pulmonary function, 41 cases of mild abnormality and 9 cases of moderate abnormality. The number of lung regions with small opacities on X-ray was the risk factor for abnormal lung function in cement pneumoconiosis patients (OR=2.491, 95%CI=1.197-5.183, P=0.015) . Conclusion: The patients with occupational cement pneumoconiosis had long dust exposure duration and incubation period, light imaging changes and pulmonary function damage. The abnormal lung function was related to the range of pulmonary involvement.
Humans ; Female ; Male ; Adolescent ; Young Adult ; Adult ; Middle Aged ; Aged ; Retrospective Studies ; Pneumoconiosis ; Dust ; Hospitals ; Image Processing, Computer-Assisted

Accurate segmentation of retinal blood vessels is of great significance for diagnosing, preventing and detecting eye diseases. In recent years, the U-Net network and its various variants have reached advanced level in the field of medical image segmentation. Most of these networks choose to use simple max pooling to down-sample the intermediate feature layer of the image, which is easy to lose part of the information, so this study proposes a simple and effective new down-sampling method Pixel Fusion-pooling (PF-pooling), which can well fuse the adjacent pixel information of the image. The down-sampling method proposed in this study is a lightweight general module that can be effectively integrated into various network architectures based on convolutional operations. The experimental results on the DRIVE and STARE datasets show that the F₁-score index of the U-Net model using PF-pooling on the STARE dataset improved by 1.98%. The accuracy rate is increased by 0.2%, and the sensitivity is increased by 3.88%. And the generalization of the proposed module is verified by replacing different algorithm models. The results show that PF-pooling has achieved performance improvement in both Dense-UNet and Res-UNet models, and has good universality.
Algorithms ; Retinal Vessels ; Image Processing, Computer-Assisted

OBJECTIVE: Current mainstream PET scattering correction methods are introduced and evaluated horizontally, and finally, the existing problems and development direction of scattering correction are discussed. METHODS: Based on NeuWise Pro PET/CT products of Neusoft Medical System Co. Ltd. , the simulation experiment is carried out to evaluate the influence of radionuclide distribution out of FOV (field of view) on the scattering estimation accuracy of each method. RESULTS: The scattering events produced by radionuclide out of FOV have an obvious impact on the spatial distribution of scattering, which should be considered in the model. The scattering estimation accuracy of Monte Carlo method is higher than single scatter simulation (SSS). CONCLUSIONS Clinically, if the activity of the adjacent parts out of the FOV is high, such as brain, liver, kidney and bladder, it is likely to lead to the deviation of scattering estimation. Considering the Monte Carlo scattering estimation of the distribution of radionuclide out of FOV, it's helpful to improve the accuracy of scattering distribution estimation.
Positron Emission Tomography Computed Tomography ; Scattering, Radiation ; Computer Simulation ; Brain ; Monte Carlo Method ; Phantoms, Imaging ; Image Processing, Computer-Assisted

This study briefly introduces the tongue diagnostic equipment of traditional Chinese medicine. It analyzes and discusses the key points of technical evaluation of tongue diagnostic equipment from the aspects of product name, performance parameters, image processing functions, product use methods, clinical evaluation, etc. It analyzes the safety risks and effectiveness indicators of tongue diagnostic equipment, hoping to bring some help to the gradual standardization of tongue diagnostic equipment and the registration of enterprises.
Medicine, Chinese Traditional/methods* ; Tongue ; Image Processing, Computer-Assisted ; Diagnostic Equipment ; Reference Standards

Machine Learning ; Image Processing, Computer-Assisted/methods*