BMC Medical Imaging最新文献

Early screening methods for the thyroid gland include palpation and imaging. Although palpation is relatively simple, its effectiveness in detecting early clinical signs of the thyroid gland may be limited, especially in children, due to the shorter thyroid growth time. Therefore, this constitutes a crucial foundational work. However, accurately determining the location and size of the thyroid gland in children is a challenging task. Accuracy depends on the experience of the ultrasound operator in current clinical practice, leading to subjective results. Even among experts, there is poor agreement on thyroid identification. In addition, the effective use of ultrasound machines also relies on the experience of the ultrasound operator in current clinical practice. In order to extract sufficient texture information from pediatric thyroid ultrasound images while reducing the computational complexity and number of parameters, this paper designs a novel U-Net-based network called DC-Contrast U-Net, which aims to achieve better segmentation performance with lower complexity in medical image segmentation. The results show that compared with other U-Net-related segmentation models, the proposed DC-Contrast U-Net model achieves higher segmentation accuracy while improving the inference speed, making it a promising candidate for deployment in medical edge devices in clinical applications in the future.

This paper presents a non-contact and unrestrained respiration monitoring system based on the optical triangulation technique. The proposed system consists of a red-green-blue (RGB) camera and a line laser installed to face the frontal thorax of a human body. The underlying idea of the work is that the camera and line laser are mounted in opposite directions, unlike other research. By applying the proposed image processing algorithm to the camera image, laser coordinates are extracted and converted to world coordinates using the optical triangulation method. These converted world coordinates represent the height of the thorax of a person. The respiratory rate is measured by analyzing changes of the thorax surface depth. To verify system performance, the camera and the line laser are installed on the head and foot sides of a bed, respectively, facing toward the center of the bed. Twenty healthy volunteers were enrolled and underwent measurement for 100s. Evaluation results show that the optical triangulation-based image processing method demonstrates non-inferior performance to a commercial patient monitoring system with a root-mean-squared error of 0.30rpm and a maximum error of 1rpm ( $p>0.05$ ), which implies the proposed non-contact system can be a useful alternative to the conventional healthcare method.

Background: In recent years, radiomics has been shown to be an effective tool for the diagnosis and prediction of diseases. Existing evidence suggests that imaging features play a key role in predicting the recurrence of lumbar disk herniation (rLDH). Thus, this study aimed to evaluate the risk of rLDH in patients undergoing percutaneous endoscopic lumbar discectomy (PELD) using radiomics to facilitate the development of more rational surgical and perioperative management strategies.

Method: This was a retrospective case-control study involving 487 patients who underwent PELD at the L4/5 level. The rLDH and negative groups were matched using propensity score matching (PSM). A total of 1409 radiomic features were extracted from preoperative lumbar MRI images using intraclass correlation coefficient (ICC) analysis, t-test, and LASSO analysis. Afterward, 6 predictive models were constructed and evaluated using ROC curve analysis, AUC, specificity, sensitivity, confusion matrix, and 2 repeated 3-fold cross-validations. Lastly, the Shapley Additive Explanation (SHAP) analysis provided visual explanations for the models.

Results: Following screening and matching, 128 patients were included in both the recurrence and control groups. Moreover, 18 of the extracted radiomic features were selected for generating six models, which achieved an AUC of 0.551-0.859 for predicting rLDH. Among these models, SVM, RF, and XG Boost exhibited superior performances. Finally, cross-validation revealed that their accuracy was 0.674-0.791, 0.647-0.729, and 0.674-0.718.

Conclusion: Radiomics based on MRI can be used to predict the risk of rLDH, offering more comprehensive guidance for perioperative treatment by extracting imaging information that cannot be visualized with the naked eye. Meanwhile, the accuracy and generalizability of the model can be improved in the future by incorporating more data and conducting multicenter studies.

Background: The cost of labeling to collect training data sets using deep learning is especially high in medical applications compared to other fields. Furthermore, due to variances in images depending on the computed tomography (CT) devices, a deep learning based segmentation model trained with a certain device often does not work with images from a different device.

Methods: In this study, we propose an efficient learning strategy for deep learning models in medical image segmentation. We aim to overcome the difficulties of segmentation in CT images by training a VNet segmentation model which enables rapid labeling of organs in CT images with the model obtained by transfer learning using a small number of manually labeled images, called SEED images. We established a process for generating SEED images and conducting transfer learning a model. We evaluate the performance of various segmentation models such as vanilla UNet, UNETR, Swin-UNETR and VNet. Furthermore, assuming a scenario that a model is repeatedly trained with CT images collected from multiple devices, in which is catastrophic forgetting often occurs, we examine if the performance of our model degrades.

Results: We show that transfer learning can train a model that does a good job of segmenting muscles with a small number of images. In addition, it was confirmed that VNet shows better performance when comparing the performance of existing semi-automated segmentation tools and other deep learning networks to muscle and liver segmentation tasks. Additionally, we confirmed that VNet is the most robust model to deal with catastrophic forgetting problems.

Conclusion: In the 2D CT image segmentation task, we confirmed that the CNN-based network shows better performance than the existing semi-automatic segmentation tool or latest transformer-based networks.

Background: Most patients with osteoporosis experience vertebral compression fracture (VCF), which significantly reduces their quality of life. These patients are at a high risk of secondary VCF regardless of treatment. Thus, accurate diagnosis of VCF is important for treating and preventing new fractures. We aimed to investigate the diagnostic and predictive value of quantitative bone imaging techniques for fresh VCF.

Methods: From November 2021 to March 2023, 34 patients with VCF were enrolled in this study, all of whom underwent routine ^99mTc-MDP whole-body bone planar scan and local SPECT/CT imaging. The maximum standard uptake value (SUVmax) of 57 fresh VCF, 57 normal adjacent vertebrae, and 19 old VCF were measured. Based on the site of the fracture, fresh VCFs were regrouped into the intervertebral-type group and the margin-type group. Meanwhile, 52 patients who had no bone metastasis or VCFs in their bone scan were assigned to the control group. The SUVmax of 110 normal vertebral bodies and 10 old VCFs in the control group were measured.

Results: The median SUVmax of fresh VCF was 19.80, which was significantly higher than the SUVmax of other groups. The receiver operator characteristic (ROC) curve showed that the cut-off value of SUVmax was 9.925 for diagnosing fresh VCF. The SUVmax in the intervertebral-type group was significantly higher than that in the margin-type group (P = 0.04). The SUVmax of normal vertebrae was higher among patients than among the control group (P<0.01), but the CT HU value showed no significant difference.

Conclusion: The quantitative technique of bone SPECT/CT has a significant value in diagnosing fresh VCF. It can also determine the severity of fractures. In addition, whether the SUVs of the vertebrae adjacent to the fractured vertebra can predict re-fracture deserves further studies.

Background: 'Kimura's disease (KD) is a rare chronic inflammatory disorder of unknown etiology and is difficult to diagnose due to poor clinical presentation and imaging features. Few studies on characteristics of ¹⁸F-FDG PET/CT of KD have been reported. This study aimed to observe the reliable characteristics and usefulness of ¹⁸F-FDG PET/CT for the evaluation of consecutive patients with KD.

Methods: The clinical data and ¹⁸F-FDG PET/CT imaging findings of 8 patients with pathologically confirmed KD were reviewed retrospectively.¹⁸F-FDG PET/CT images were evaluated visually and semiquantitatively by measuring the maximum standardized uptake value (SUV_max). The correlations between clinical data and ¹⁸F-FDG PET/CT features were analyzed by simple linear regression.

Results: This study included 7 males and one female ranging in age from 17 to 79 years. The longest diameter of lesions ranged from 0.8 cm to 4.8 cm, and regional or generalized lymphadenopathy was found in all 8 patients with eosinophilia, while subcutaneous masses and salivary gland involvement concurrently were found in 4 patients. ¹⁸F-FDG PET/CT revealed that these involved lesions had high ¹⁸F-FDG uptake with SUV_max > 2.5 (2.6 to 6.3). Moreover, the margin of the lesions was well defined in 6 cases and ill defined in 2 cases, and homogeneous density and ¹⁸F-FDG uptake were both found in all these lesions. There was negative correlation between eosinophils and SUV_max (R² = 0.538).

Conclusions: Kimura's disease should be considered when ¹⁸F-FDG PET/CT is characterized by homogeneous lesions of regional or generalized lymphadenopathy, accompanied with subcutaneous masses and salivary gland involvement concurrently, especially in patients with eosinophilia.

Eating disorders (EDs), including anorexia nervosa (AN), bulimia nervosa (BN), binge-eating disorder (BED), and pica, are psychobehavioral conditions characterized by abnormal eating behaviors and an excessive preoccupation with weight and body shape. This review examines changes in brain regions and functional connectivity in ED patients over the past decade (2013-2023) using resting-state functional magnetic resonance imaging (rs-fMRI). Key findings highlight alterations in brain networks such as the default mode network (DMN), central executive network (CEN), and emotion regulation network (ERN). In individuals with AN, there is reduced functional connectivity in areas associated with facial information processing and social cognition, alongside increased connectivity in regions linked to sensory stimulation, aesthetic judgment, and social anxiety. Conversely, BED patients show diminished connectivity in the dorsal anterior cingulate cortex within the salience network and increased connectivity in the posterior cingulate cortex and medial prefrontal cortex within the DMN. These findings suggest that rs-fMRI could serve as a valuable biomarker for assessing brain function and predicting treatment outcomes in EDs, paving the way for personalized therapeutic strategies.

Objective: To evaluate value of Nomogram prediction model based on CTA imaging features for selecting treatment methods for isolated superior mesenteric artery dissection (ISMAD).

Methods: Symptomatic ISMAD patients were randomly divided into a training set and a validation set in a 7:3 ratio. In the training set, relevant risk factors for conservative treatment failure in ISMAD patients were analyzed, and a Nomogram prediction model for treatment outcome of ISMAD was constructed with risk factors. The predictive value of the model was evaluated.

Results: Low true lumen residual ratio (TLRR), long dissection length, and large arterial angle (superior mesenteric artery [SMA]/abdominal aorta [AA]) were identified as independent high-risk factors for conservative treatment failure (P < 0.05). The receiver operating characteristic curve (ROC) results showed that the area under curve (AUC) of Nomogram prediction model was 0.826 (95% CI: 0.740-0.912), indicating good discrimination. The Hosmer-Lemeshow goodness-of-fit test showed good consistency between the predicted curve and the ideal curve of the Nomogram prediction model. The decision curve analysis (DCA) analysis results showed that when probability threshold for the occurrence of conservative treatment failure predicted was 0.05-0.98, patients could obtain more net benefits. Similar results were obtained for the predictive value in the validation set.

Conclusion: Low TLRR, long dissection length, and large arterial angle (SMA/AA) are independent high-risk factors for conservative treatment failure in ISMAD. The Nomogram model constructed with independent high-risk factors has good clinical effectiveness in predicting the failure.

Background: Invasive pulmonary aspergillosis (IPA) is a serious fungal infection. However, current diagnostic methods have limitations. The purpose of this study was to use artificial intelligence to achieve a more accurate diagnosis of IPA.

Methods: Totally 263 patients (148 cases of IPA, 115 cases of non-IPA) were retrospectively enrolled from a single institution and randomly divided into training and test sets at a ratio of 7:3. Clinic-radiological independent risk factors for IPA were screened using univariate analysis and multivariate logistic regression analysis, after which a clinic-radiological model was constructed. The optimal radiomics features were extracted and screened based on CT images to construct the radiomics label score (Rad-score) and radiomics model. The optimal DL features were extracted and screened using four pre-trained convolutional neural networks, respectively, followed by the construction of the DL label score (DL-score) and DL model. Then, the radiomics-DL model was constructed. Finally, the combined model was constructed based on clinic-radiological independent risk factors, the Rad-score, and the DL-score. LR was adopted as the classifier. Receiver operating characteristic (ROC) curves were drawn, and the areas under the curve (AUC) were calculated to evaluate the efficacy of each model in predicting IPA. Additionally, based on the best-performing model on the LR classifier, four other machine learning (ML) classifiers were constructed to evaluate the predictive value for IPA.

Results: The AUC of the clinic-radiological model for predicting IPA in the training and test sets was 0.845 and 0.765, respectively. The AUC of the radiomics-DL and combined models in the training set was 0.871 and 0.932, while in the test set was 0.851 and 0.881, respectively. The combined model showed better predictive performance than all other models. DCA showed that taking 0.00-1.00 as the threshold, the clinical benefit of the combined model was higher than that of all other models. Then, the combined model was trained on four other machine learning classifiers, all of which achieved AUC values above 0.80 in the test set, showing good performance in predicting IPA.

Conclusion: Clinic, CT radiomics, and DL combined model could be used to predict IPA effectively.