Application of bone alkaline phosphatase and 25-oxhydryl-vitamin D in diagnosis and prediction of osteoporotic vertebral compression fractures

BACKGROUND

Osteoporosis is a bone metabolic disease that usually causes fracture. The improvement of the clinical diagnostic efficiency of osteoporosis is of great significance for the prevention of fracture. The predictive and diagnostic values of bone alkaline phosphatase (B-ALP) and 25-oxhydryl-vitamin D (25-OH-VD) for osteoporotic vertebral compression fractures (OVCFs) were evaluated.

METHODS

110 OVCFs patients undergoing percutaneous vertebroplasty were included as subjects and their spinal computed tomography (CT) images were collected. After that, deep convolutional neural network model was employed for intelligent fracture recognition. Next, the patients were randomly enrolled into Ctrl group (65 cases receiving postoperative routine treatment) and VD2 group (65 cases injected with vitamin D2 into muscle after the surgery). In addition, 100 healthy people who participated in physical examination were included in Normal group. The differences in Oswestry dysfunction indexes (ODI), imaging parameters, B-ALP and 25-OH-VD expressions, and quality of life (QOL) scores of patients among the three groups were compared. The values of B-ALP and 25-OH-VD in predicting and diagnosing OVCFs and their correlation with bone density were analyzed.

RESULTS

It was demonstrated that computer intelligent medical image technique was more efficient in fracture CT recognition than artificial recognition. In contrast to those among patients in Normal group, B-ALP rose while 25-OH-VD declined among patients in Ctrl and VD2 groups (P < 0.05). Versus those among patients in Ctrl group, ODI, Cobb angle, and B-ALP reduced, while bone density, the height ratio of the injured vertebrae, 25-OH-VD, and QOL score increased among patients in VD2 group after the treatment (P < 0.05). The critical values, accuracy, and areas under the curve (AUC) of the diagnosis of OVCFs by B-ALP and 25-OH-VD amounted to 87.8 μg/L versus 30.3 nmol/L, 86.7% versus 83.3%, and 0.86 versus 0.82, respectively. B-ALP was apparently negatively correlated with bone density (r =  - 0.602, P < 0.05), while 25-OH-VD was remarkably positively correlated with bone density (r = 0.576, P < 0.05).

CONCLUSION

To sum up, deep learning-based computer CT image intelligent detection technique could improve the diagnostic efficacy of fracture. B-ALP rose while 25-OH-VD declined among patients with OVCFs and OVCFs could be predicted and diagnosed based on B-ALP and 25-OH-VD. Postoperative intramuscular injection of VD2 could effectively improve the therapeutic effect on patients with OVCFs and QOL.

DC-cycleGAN: Bidirectional CT-to-MR synthesis from unpaired data

Magnetic resonance (MR) and computer tomography (CT) images are two typical types of medical images that provide mutually-complementary information for accurate clinical diagnosis and treatment. However, obtaining both images may be limited due to some considerations such as cost, radiation dose and modality missing. Recently, medical image synthesis has aroused gaining research interest to cope with this limitation. In this paper, we propose a bidirectional learning model, denoted as dual contrast cycleGAN (DC-cycleGAN), to synthesize medical images from unpaired data. Specifically, a dual contrast loss is introduced into the discriminators to indirectly build constraints between real source and synthetic images by taking advantage of samples from the source domain as negative samples and enforce the synthetic images to fall far away from the source domain. In addition, cross-entropy and structural similarity index (SSIM) are integrated into the DC-cycleGAN in order to consider both the luminance and structure of samples when synthesizing images. The experimental results indicate that DC-cycleGAN is able to produce promising results as compared with other cycleGAN-based medical image synthesis methods such as cycleGAN, RegGAN, DualGAN, and NiceGAN. Code is available at https://github.com/JiayuanWang-JW/DC-cycleGAN.

Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography

Background

Chest computed tomography (CT) has a high sensitivity for detecting COVID-19 lung involvement and is widely used for diagnosis and disease monitoring. We proposed a new image classification model, swin-textural, that combined swin-based patch division with textual feature extraction for automated diagnosis of COVID-19 on chest CT images. The main objective of this work is to evaluate the performance of the swin architecture in feature engineering.

Material and method

We used a public dataset comprising 2167, 1247, and 757 (total 4171) transverse chest CT images belonging to 80, 80, and 50 (total 210) subjects with COVID-19, other non-COVID lung conditions, and normal lung findings. In our model, resized 420 × 420 input images were divided using uniform square patches of incremental dimensions, which yielded ten feature extraction layers. At each layer, local binary pattern and local phase quantization operations extracted textural features from individual patches as well as the undivided input image. Iterative neighborhood component analysis was used to select the most informative set of features to form ten selected feature vectors and also used to select the 11th vector from among the top selected feature vectors with accuracy >97.5%. The downstream kNN classifier calculated 11 prediction vectors. From these, iterative hard majority voting generated another nine voted prediction vectors. Finally, the best result among the twenty was determined using a greedy algorithm.

Results

Swin-textural attained 98.71% three-class classification accuracy, outperforming published deep learning models trained on the same dataset. The model has linear time complexity.

Conclusions

Our handcrafted computationally lightweight swin-textural model can detect COVID-19 accurately on chest CT images with low misclassification rates. The model can be implemented in hospitals for efficient automated screening of COVID-19 on chest CT images. Moreover, findings demonstrate that our presented swin-textural is a self-organized, highly accurate, and lightweight image classification model and is better than the compared deep learning models for this dataset.

Biodynamic responses of adolescent idiopathic scoliosis exposed to vibration

Patients with adolescent idiopathic scoliosis suffer severe health issues. The unclear dynamic biomechanical characteristics of scoliosis were needed to be explored to improve the prevention and treatment in clinics. Validated 3D finite element (FE) models of thoracolumbosacral spine (T1-S1) both with and without scoliosis were developed from computed tomography (CT) images. Modal and harmonic analyses were performed to investigate the biomechanical responses of the spinal models to vibration. Resonant frequencies of the scoliotic model were lower than those of the model without scoliosis. Peak amplitudes occurred at vibrational frequencies close to the modal resonant frequencies, which caused the deformed thoracic segment in scoliosis suffered the maximum amplitude. The stresses on vertebrae and intervertebral discs in the scoliotic model derived from vibrations were significantly larger than those in the non-scoliosis model, and heterogeneously concentrated on the scoliotic thoracic segment. In conclusion, the scoliotic spine in the patients with Lenke 1BN scoliosis is more prone to injuries than the non-scoliotic spine while vibrating. Scoliotic thoracic segments in patients with Lenke 1BN scoliosis were the more vulnerable and sensitive component of the T1-S1 spine to vibration than lumbar spines. This study suggested that vibration would impair the scoliotic spines, and patients with Lenke 1BN scoliosis should avoid exposure to vibration, especially the low-frequency vibration.

An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19

The SARS-CoV-2 virus causes a respiratory disease in humans, known as COVID-19. The confirmatory diagnostic of this disease occurs through the real-time reverse transcription and polymerase chain reaction test (RT-qPCR). However, the period of obtaining the results limits the application of the mass test. Thus, chest X-ray computed tomography (CT) images are analyzed to help diagnose the disease. However, during an outbreak of a disease that causes respiratory problems, radiologists may be overwhelmed with analyzing medical images. In the literature, some studies used feature extraction techniques based on CNNs, with classification models to identify COVID-19 and non-COVID-19. This work compare the performance of applying pre-trained CNNs in conjunction with classification methods based on machine learning algorithms. The main objective is to analyze the impact of the features extracted by CNNs, in the construction of models to classify COVID-19 and non-COVID-19. A SARS-CoV-2 CT data-set is used in experimental tests. The CNNs implemented are visual geometry group (VGG-16 and VGG-19), inception V3 (IV3), and EfficientNet-B0 (EB0). The classification methods were k-nearest neighbor (KNN), support vector machine (SVM), and explainable deep neural networks (xDNN). In the experiments, the best results were obtained by the EfficientNet model used to extract data and the SVM with an RBF kernel. This approach achieved an average performance of 0.9856 in the precision macro, 0.9853 in the sensitivity macro, 0.9853 in the specificity macro, and 0.9853 in the F1 score macro.

Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation

This paper presents an automatic classification segmentation tool for helping screening COVID-19 pneumonia using chest CT imaging. The segmented lesions can help to assess the severity of pneumonia and follow-up the patients. In this work, we propose a new multitask deep learning model to jointly identify COVID-19 patient and segment COVID-19 lesion from chest CT images. Three learning tasks: segmentation, classification and reconstruction are jointly performed with different datasets. Our motivation is on the one hand to leverage useful information contained in multiple related tasks to improve both segmentation and classification performances, and on the other hand to deal with the problems of small data because each task can have a relatively small dataset. Our architecture is composed of a common encoder for disentangled feature representation with three tasks, and two decoders and a multi-layer perceptron for reconstruction, segmentation and classification respectively. The proposed model is evaluated and compared with other image segmentation techniques using a dataset of 1369 patients including 449 patients with COVID-19, 425 normal ones, 98 with lung cancer and 397 of different kinds of pathology. The obtained results show very encouraging performance of our method with a dice coefficient higher than 0.88 for the segmentation and an area under the ROC curve higher than 97% for the classification.

Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation

This paper presents an automatic classification segmentation tool for helping screening COVID-19 pneumonia using chest CT imaging. The segmented lesions can help to assess the severity of pneumonia and follow-up the patients. In this work, we propose a new multitask deep learning model to jointly identify COVID-19 patient and segment COVID-19 lesion from chest CT images. Three learning tasks: segmentation, classification and reconstruction are jointly performed with different datasets. Our motivation is on the one hand to leverage useful information contained in multiple related tasks to improve both segmentation and classification performances, and on the other hand to deal with the problems of small data because each task can have a relatively small dataset. Our architecture is composed of a common encoder for disentangled feature representation with three tasks, and two decoders and a multi-layer perceptron for reconstruction, segmentation and classification respectively. The proposed model is evaluated and compared with other image segmentation techniques using a dataset of 1369 patients including 449 patients with COVID-19, 425 normal ones, 98 with lung cancer and 397 of different kinds of pathology. The obtained results show very encouraging performance of our method with a dice coefficient higher than 0.88 for the segmentation and an area under the ROC curve higher than 97% for the classification.

Influence of storage on the quality of conventional CT and µCT-imaging for the middle and inner cat ear

The aim of this study was to analyze whether different fixation methods such as freezing or formaldehyde storage for different periods of time have an influence on the recognition of anatomical relevant structures in the middle and inner ear of the cat with conventional computed tomography (cCT) and micro-computed tomography (µCT). Besides, effects of freeze-thaw cycles on determined structures of the ear were investigated by means of histological slices. Three veterinarians with different radiologic expertise evaluated the scans of 30 dissected cat ears anonymously and scored predefined structures in a five-point scale with reference to visually sharp reproducibility and perfect image quality. The total scores of the different fixation groups as well as the ears within a group were compared with each other. Furthermore, an intra-reader examination including an evaluation of the identifiability of specified structures was performed for both imaging methods. cCT as well as µCT-scans have a very low variation coefficient of 1.6% and 2.3%, respectively. The results for the alterations between the different fixation methods show that the changes for cCT-scans are negligible, as the percentage alteration compared to fresh samples ranges in a very small interval with values from 1.0% better to 1.2% worse. µCT-scans are more influenced by the fixation method with a range from 1.3% better to 6.9% worse values. The scans mostly deteriorated after two freeze-thaw cycles (1.8% worse) and after storing the samples for 1 (2.4% worse), respectively, and 3 weeks in formaldehyde (6.9% worse).

3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning

In the paper, we propose a new deep learning-based method for segmenting nasopharyngeal carcinoma (NPC) in the nasopharynx from three orthogonal CT images. The proposed method introduces a cascade strategy composed of two-phase manners. In CT images, there are organs, called non-target organs, which NPC never invades. Therefore, the first phase is to detect and eliminate non-target organ regions from the CT images. In the second phase, NPC is extracted from the remained regions in the CT images. Convolutional neural networks (CNNs) are applied to detect non-target organs and NPCs. The proposed system determines the final NPC segmentation by integrating three results obtained from coronal, axial and sagittal images. Moreover, we construct two CNN-based NPC detection systems using one kind of overlapping patches with a fixed size and various overlapping patches with different sizes. From the experiments using CT images of 70 NPC patients, our proposed systems, especially the system using various patches, achieves the best performance for detecting NPC compared with conventional NPC detection methods.

Cancer-induced spiculation on computed tomography: a significant preoperative prognostic factor for colorectal cancer

PURPOSE

Cancer-induced spiculation (CIS) on computed tomography, which is reticular or linear opacification of the pericolorectal fat tissues around the cancer site, is generally regarded as cancer infiltration into T3 or T4, but its clinicopathological significance is unknown. This study examines the correlation between CIS and clinicopathological findings to establish its prognostic value.

METHODS

The subjects of this retrospective study were 335 patients with colorectal cancer (CRC), who underwent curative surgery between January, 2010 and December, 2011, at the National Defense Medical College Hospital in Saitama Prefecture, Japan.

RESULTS

The level of interobserver agreement in the evaluation of CIS was substantial (83%; kappa value, 0.65). The presence of CIS was specific for T3/T4 disease (positive predictive value, 88.3%), and was significantly associated with tumor size and venous invasion. The 5-year relapse-free survival rate was significantly lower in patients with CIS than in those without CIS (68.6% and 84.0%, respectively, p = 0.001). Subgroup analysis revealed remarkable prognostic differences in patients with stage III and T3 disease. Multivariate analysis revealed that CIS was a significant independent prognostic factor.

CONCLUSIONS

CIS was a significant preoperative prognostic factor and could be useful in the selection of preoperative therapy for patients with CRC.

A hybrid energy model for region based curve evolution - Application to CTA coronary segmentation

BACKGROUND AND OBJECTIVE

State-of-the-art medical imaging techniques have enabled non-invasive imaging of the internal organs. However, high volumes of imaging data make manual interpretation and delineation of abnormalities cumbersome for clinicians. These challenges have driven intensive research into efficient medical image segmentation. In this work, we propose a hybrid region-based energy formulation for effective segmentation in computed tomography angiography (CTA) imagery.

METHODS

The proposed hybrid energy couples an intensity-based local term with an efficient discontinuity-based global model of the image for optimal segmentation. The segmentation is achieved using a level set formulation due to the computational robustness. After validating the statistical significance of the hybrid energy, we applied the proposed model to solve an important clinical problem of 3D coronary segmentation. An improved seed detection method is used to initialize the level set evolution. Moreover, we employed an auto-correction feature that captures the emerging peripheries during the curve evolution for completeness of the coronary tree.

RESULTS

We evaluated the segmentation accuracy of the proposed energy model against the existing techniques in two stages. Qualitative and quantitative results demonstrate the effectiveness of the proposed framework with a consistent mean sensitivity and specificity measures of 80% across the CTA data. Moreover, a high degree of agreement with respect to the inter-observer differences justifies the generalization of the proposed method.

CONCLUSIONS

The proposed method is effective to segment the coronary tree from the CTA volume based on hybrid image based energy, which can improve the clinicians ability to detect arterial abnormalities.

A CAD of fully automated colonic polyp detection for contrasted and non-contrasted CT scans

PURPOSE

Computer-aided detection (CAD) systems are developed to help radiologists detect colonic polyps over CT scans. It is possible to reduce the detection time and increase the detection accuracy rates by using CAD systems. In this paper, we aimed to develop a fully integrated CAD system for automated detection of polyps that yields a high polyp detection rate with a reasonable number of false positives.

METHODS

The proposed CAD system is a multistage implementation whose main components are: automatic colon segmentation, candidate detection, feature extraction and classification. The first element of the algorithm includes a discrete segmentation for both air and fluid regions. Colon-air regions were determined based on adaptive thresholding, and the volume/length measure was used to detect air regions. To extract the colon-fluid regions, a rule-based connectivity test was used to detect the regions belong to the colon. Potential polyp candidates were detected based on the 3D Laplacian of Gaussian filter. The geometrical features were used to reduce false-positive detections. A 2D projection image was generated to extract discriminative features as the inputs of an artificial neural network classifier.

RESULTS

Our CAD system performs at 100% sensitivity for polyps larger than 9 mm, 95.83% sensitivity for polyps 6-10 mm and 85.71% sensitivity for polyps smaller than 6 mm with 5.3 false positives per dataset. Also, clinically relevant polyps ([Formula: see text]6 mm) were identified with 96.67% sensitivity at 1.12 FP/dataset.

CONCLUSIONS

To the best of our knowledge, the novel polyp candidate detection system which determines polyp candidates with LoG filters is one of the main contributions. We also propose a new 2D projection image calculation scheme to determine the distinctive features. We believe that our CAD system is highly effective for assisting radiologist interpreting CT.

Individual tooth segmentation from CT images scanned with contacts of maxillary and mandible teeth

BACKGROUND AND OBJECTIVE

Tooth segmentation from computed tomography (CT) images is a fundamental step in generating the three-dimensional models of tooth for computer-aided orthodontic treatment. Individual tooth segmentation from CT images scanned with contacts of maxillary and mandible teeth is especially challenging, and no method has been reported previously. This study aimed to develop a method for individual tooth segmentation from these images.

METHODS

Tooth contours of maxilla and mandible are first segmented from the volumetric CT images slice-by-slice. For each slice, a line is extracted using the Radon transform to separate neighboring teeth, and each tooth contour is then segmented by a level set model from the corresponding side of the line. Then, each maxillary tooth whose contours overlap with that of mandible ones is detected, and a mesh model is reconstructed from all the contours of these maxillary and mandible teeth with contour overlap. The reconstructed mesh model is segmented using threshold and fast marching watershed method to separate the touched maxillary and mandible teeth. Finally, the separated tooth models are restored to fill the holes to obtain complete tooth models. The proposed method was tested on CT images of ten subjects scanned with natural contacts of maxillary and mandible teeth.

RESULTS

For all the tested images, individual tooth regions are extracted successfully, and the segmentation accuracy and efficiency of the proposed method is promising.

CONCLUSIONS

The proposed method is effective to segment individual tooth from CT images scanned with contacts of maxillary and mandible teeth.

Quantitative diagnosis of connective tissue disease-associated interstitial pneumonia using thoracic computed tomography images

Patients with polymyositis (PM) or dermatomyositis (DM) frequently show interstitial pneumonia (IP), which is sometimes rapidly progressive or resistant to treatment, thereby significantly affecting the prognosis. The diagnosis and response evaluation of IP are commonly performed qualitatively based on imaging findings, which may cause disagreement among rheumatologists in the evaluation of early lesions and atypical interstitial changes. To determine whether IP could be diagnosed in a quantitative manner during the early stage of PM/DM using a workstation that allows quantitative image processing. Thoracic computed tomography (CT) images of 20 PM/DM patients were reconstructed into a three-dimensional (3D) image using an image processing workstation. The CT values of the constituent voxels were arranged in a histogram of -1000 to +1000 Hounsfield units (HU). The most frequent lung field density was -900 to -801 HU, and relative size was as follows: IP (+) group 0.45 and IP (-) group 0.53. Between -1000 and -701 HU, relative size was not significantly different between the IP (+) group and IP (-) group. Between -700 and -1 HU, the relative size of the lung field was significantly larger in the IP (+) than in the IP (-) group, demonstrating its IP-diagnosing ability. Particularly, within the range from -700 to -301 HU, the macroscopically-assessed ground glass opacity was consistent with the CT value, which, in turn, was closely correlated with KL-6, the pre-existing marker for IP diagnosis. The results of this study may lead to the establishment of quantitative methods of evaluating IP and possible elucidation of the pathogenesis of IP.

Differential diagnostic value of small bowel wall thickness and density changes measured by computed tomography in small bowel obstruction

AIM: To investigate the diagnostic value of small bowel wall thickness and density measured by computed tomography (CT) in differentiating simple small bowel obstruction (SI) from strangulated small bowel obstruction (ST).

METHODS: Forty-two patients with SI and 38 patients with ST were included. All patients underwent plain and contrast-enhanced CT scans. The density of bowel wall thickening was recorded and compared.

RESULTS: The percentage of patients with normal inner density measured by plain CT was significantly higher in the SI group than in the ST group, and the percentage of patients with decreased whole density was significantly lower in the SI group than in the ST group (P < 0.05). The percentage of patients with decreased whole density measured by contrast-enhanced CT was significantly lower in the SI group than in the ST group (P < 0.05). The sensitivity and specificity of normal inner density measured by plain CT for diagnosis of ST were 34.2% and 33.3%, respectively. The sensitivity and specificity of decreased whole density measured by plain CT were 73.7% and 71.4%, respectively. The sensitivity and specificity of decreased whole density measured by contrast-enhanced CT were 84.2% and 73.8%, respectively.

CONCLUSION: Small bowel wall thickness and density changes measured by computed tomography can help differentiate between SI and ST.