BMC Medical Imaging最新文献

Many image fusion methods have been proposed to leverage the advantages of functional and anatomical images while compensating for their shortcomings. These methods integrate functional and anatomical images while presenting physiological and metabolic organ information, making their diagnostic efficiency far greater than that of single-modal images. Currently, most existing multimodal medical imaging fusion methods are based on multiscale transformation, which involves obtaining pyramid features through multiscale transformation. Low-resolution images are used to analyse approximate image features, and high-resolution images are used to analyse detailed image features. Different fusion rules are applied to achieve feature fusion at different scales. Although these fusion methods based on multiscale transformation can effectively achieve multimodal medical image fusion, much detailed information is lost during multiscale and inverse transformation, resulting in blurred edges and a loss of detail in the fusion images. A multimodal medical image fusion method based on interval gradients and convolutional neural networks is proposed to overcome this problem. First, this method uses interval gradients for image decomposition to obtain structure and texture images. Second, deep neural networks are used to extract perception images. Three methods are used to fuse structure, texture, and perception images. Last, the images are combined to obtain the final fusion image after colour transformation. Compared with the reference algorithms, the proposed method performs better in multiple objective indicators of $Q_{\mathrm{EN}}$ , $Q_{\mathrm{NIQE}}$ , $Q_{\mathrm{SD}}$ , $Q_{\mathrm{SSEQ}}$ and $Q_{\mathrm{TMQI}}$ .

Breast cancer is a leading cause of mortality among women globally, necessitating precise classification of breast ultrasound images for early diagnosis and treatment. Traditional methods using CNN architectures such as VGG, ResNet, and DenseNet, though somewhat effective, often struggle with class imbalances and subtle texture variations, leading to reduced accuracy for minority classes such as malignant tumors. To address these issues, we propose a methodology that leverages EfficientNet-B7, a scalable CNN architecture, combined with advanced data augmentation techniques to enhance minority class representation and improve model robustness. Our approach involves fine-tuning EfficientNet-B7 on the BUSI dataset, implementing RandomHorizontalFlip, RandomRotation, and ColorJitter to balance the dataset and improve model robustness. The training process includes early stopping to prevent overfitting and optimize performance metrics. Additionally, we integrate Explainable AI (XAI) techniques, such as Grad-CAM, to enhance the interpretability and transparency of the model's predictions, providing visual and quantitative insights into the features and regions of ultrasound images influencing classification outcomes. Our model achieves a classification accuracy of 99.14%, significantly outperforming existing CNN-based approaches in breast ultrasound image classification. The incorporation of XAI techniques enhances our understanding of the model's decision-making process, thereby increasing its reliability and facilitating clinical adoption. This comprehensive framework offers a robust and interpretable tool for the early detection and diagnosis of breast cancer, advancing the capabilities of automated diagnostic systems and supporting clinical decision-making processes.

Recent improvements in artificial intelligence and computer vision make it possible to automatically detect abnormalities in medical images. Skin lesions are one broad class of them. There are types of lesions that cause skin cancer, again with several types. Melanoma is one of the deadliest types of skin cancer. Its early diagnosis is at utmost importance. The treatments are greatly aided with artificial intelligence by the quick and precise diagnosis of these conditions. The identification and delineation of boundaries inside skin lesions have shown promise when using the basic image processing approaches for edge detection. Further enhancements regarding edge detections are possible. In this paper, the use of fractional differentiation for improved edge detection is explored on the application of skin lesion detection. A framework based on fractional differential filters for edge detection in skin lesion images is proposed that can improve automatic detection rate of malignant melanoma. The derived images are used to enhance the input images. Obtained images then undergo a classification process based on deep learning. A well-studied dataset of HAM10000 is used in the experiments. The system achieves 81.04% accuracy with EfficientNet model using the proposed fractional derivative based enhancements whereas accuracies are around 77.94% when using original images. In almost all the experiments, the enhanced images improved the accuracy. The results show that the proposed method improves the recognition performance.

Objectives: To investigate the value of conventional ultrasonography (US) combined with quantitative shear wave elastography (SWE) in evaluating and identifying target axillary lymph node (TALN) for fine needle aspiration biopsy (FNAB) of patients with early breast cancer.

Materials and methods: A total of 222 patients with 223 ALNs were prospectively recruited from January 2018 to December 2021. All TALNs were evaluated by US, SWE and subsequently underwent FNAB. The diagnostic performances of US, SWE, UE_or (either US or SWE was positive) and UE_and (both US and SWE were positive), and FNAB guided by the above four methods for evaluating ALN status were assessed using receiver operator characteristic curve (ROC) analyses. Univariate and multivariate logistic regression analyses used to determine the independent predictors of axillary burden.

Results: The area under the ROC curve (AUC) for diagnosing ALNs using conventional US and SWE were 0.69 and 0.66, respectively, with sensitivities of 78.00% and 65.00% and specificities of 60.98% and 66.67%. The combined method, UE_or, demonstrated significantly improved sensitivity of 86.00% (p < 0.001 when compared with US and SWE alone). The AUC of the UE_or-guided FNAB [0.85 (95% CI, 0.80-0.90)] was significantly higher than that of US-guided FNAB [0.83 (95% CI, 0.78-0.88), p = 0.042], SWE-guided FNAB [0.79 (95% CI, 0.72-0.84), p = 0.001], and UE_and-guided FNAB [0.77 (95% CI, 0.71-0.82), p < 0.001]. Multivariate logistic regression showed that FNAB and number of suspicious ALNs were found independent predictors of axillary burden in patients with early breast cancer.

Conclusion: The UE_or had superior sensitivity compared to US or SWE alone in ALN diagnosis. The UE_or-guided FNAB achieved a lower false-negative rate compared to FNAB guided solely by US or SWE, which may be a promising tool for the preoperative diagnosis of ALNs in early breast cancer, and had the potential implication for the selection of axillary surgical modality.

Background: The presence of lateral lymph node metastases (LNM) in paediatric patients with papillary thyroid cancer (PTC) is an independent risk factor for recurrence. We aimed to identify risk factors and establish a prediction model for lateral LNM before surgery in children and adolescents with PTC.

Methods: We developed a prediction model based on data obtained from 63 minors with PTC between January 2014 and June 2023. We collected and analysed clinical factors, ultrasound (US) features of the primary tumour, and pathology records of the patients. Multivariate logistic regression analysis was used to determine independent predictors and build a prediction model. We evaluated the predictive performance of risk factors and the prediction model using the area under the receiver operating characteristic (ROC) curve. We assessed the clinical usefulness of the predicting model using decision curve analysis.

Results: Among the minors with PTC, 21 had lateral LNM (33.3%). Logistic regression revealed that independent risk factors for lateral LNM were multifocality, tumour size, sex, and age. The area under the ROC curve for multifocality, tumour size, sex, and age was 0.62 (p = 0.049), 0.61 (p = 0.023), 0.66 (p = 0.003), and 0.58 (p = 0.013), respectively. Compared to a single risk factor, the combined predictors had a significantly higher area under the ROC curve (0.842), with a sensitivity and specificity of 71.4% and 81.0%, respectively (cutoff value = 0.524). Decision curve analysis showed that the prediction model was clinically useful, with threshold probabilities between 2% and 99%.

Conclusions: The independent risk factors for lateral LNM in paediatric PTC patients were multifocality and tumour size on US imaging, as well as sex and age. Our model outperformed US imaging and clinical features alone in predicting the status of lateral LNM.

Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) are vision related complications prominently found in diabetic patients. The early identification of DR/DME grades facilitates the devising of an appropriate treatment plan, which ultimately prevents the probability of visual impairment in more than 90% of diabetic patients. Thereby, an automatic DR/DME grade detection approach is proposed in this work by utilizing image processing. In this work, the retinal fundus image provided as input is pre-processed using Discrete Wavelet Transform (DWT) with the aim of enhancing its visual quality. The precise detection of DR/DME is supported further with the application of suitable Artificial Neural Network (ANN) based segmentation technique. The segmented images are subsequently subjected to feature extraction using Adaptive Gabor Filter (AGF) and the feature selection using Random Forest (RF) technique. The former has excellent retinal vein recognition capability, while the latter has exceptional generalization capability. The RF approach also assists with the improvement of classification accuracy of Deep Convolutional Neural Network (CNN) classifier. Moreover, Chicken Swarm Algorithm (CSA) is used for further enhancing the classifier performance by optimizing the weights of both convolution and fully connected layer. The entire approach is validated for its accuracy in determination of grades of DR/DME using MATLAB software. The proposed DR/DME grade detection approach displays an excellent accuracy of 97.91%.

Background: Although endometrial cancer (EC) is staged surgically, magnetic resonance imaging (MRI) plays a critical role in assessing and selecting the most appropriate treatment planning. We aimed to assess the diagnostic performance of quantitative analysis of diffusion-weighted imaging (DWI) in preoperative assessment of EC.

Methods: Prospective analysis was done for sixty-eight patients with pathology-proven endometrial cancer who underwent MRI and DWI. Apparent diffusion coefficient (ADC) values were measured by two independent radiologists and compared with the postoperative pathological results.

Results: There was excellent inter-observer reliability in measuring ADCmean values. There were statistically significant lower ADCmean values in patients with deep myometrial invasion (MI), cervical stromal invasion (CSI), type II EC, and lympho-vascular space involvement (LVSI) (AUC = 0.717, 0.816, 0.999, and 0.735 respectively) with optimal cut-off values of ≤ 0.84, ≤ 0.84, ≤ 0.78 and ≤ 0.82 mm²/s respectively. Also, there was a statistically significant negative correlation between ADC values and the updated 2023 FIGO stage and tumor grade (strong association), and the 2009 FIGO stage (medium association).

Conclusions: The preoperative ADCmean values of EC were significantly correlated with main prognostic factors including depth of MI, CSI, EC type, grade, nodal involvement, and LVSI.

Objective: To investigate the diagnostic value of combined ⁹⁹Tc^m-DX lymphoscintigraphy and CT lymphangiography (CTL) in primary chylopericardium.

Methods: Fifty-five patients diagnosed with primary chylopericardium clinically were retrospectively analyzed. ⁹⁹Tc^m-DX lymphoscintigraphy and CTL were performed in all patients. Primary chylopericardium was classified into three types, according to the ⁹⁹Tc^m-DX lymphoscintigraphy results. The evaluation indexes of CTL include: (1) abnormal contrast distribution in the neck, (2) abnormal contrast distribution in the chest, (3) dilated thoracic duct was defined as when the widest diameter of thoracic duct was > 3 mm, (4) abnormal contrast distribution in abdominal. CTL characteristics were analyzed between different groups, and P < 0.05 was considered a statistically significant difference.

Results: Primary chylopericardium showed 12 patients with type I, 14 patients with type II, and 22 patients with type III. The incidence of abnormal contrast distribution in the posterior mediastinum was greater in type I than type III (P = 0.003). The incidence of abnormal contrast distribution in the pericardial and aortopulmonary windows, type I was greater than type III (P = 0.008). And the incidence of abnormal distribution of contrast agent in the bilateral cervical or subclavian region was greater in type II than type III (P = 0.002).

Conclusion: The combined application of the ⁹⁹Tc^m-DX lymphoscintigraphy and CTL is of great value for the localized and qualitative diagnosis of primary chylopericardium and explore the pathogenesis of lesions.