Truebusk1390

Metaplastic carcinoma demonstrated oval shape and parallel orientation more frequently. ICMF showed more irregular shape and angular margin on ultrasound, irregular or spiculated margin on breast MRI. ICMF showed more delayed washout pattern of enhancement than metaplastic carcinoma. Intratumoral T2, a very high signal was noted more in metaplastic carcinoma. CONCLUSION Our study presents variable imaging features observed between basal-like carcinomas. Although it is not sufficient to predict clinical progress, aggressiveness or prognosis of basal-like carcinomas, the results of this study will be helpful in understanding and diagnosing various basallike carcinomas. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.BACKGROUND Automatic approach to vertebrae segmentation from computed tomography (CT) images is very important in clinical applications. As the intricate appearance and variable architecture of vertebrae across the population, cognate constructions in close vicinity, pathology, and the interconnection between vertebrae and ribs, it is a challenge to propose a 3D automatic vertebrae CT image segmentation method. OBJECTIVE The purpose of this study was to propose an automatic multi-vertebrae segmentation method for spinal CT images. METHODS Firstly, CLAHE-Threshold-Expansion was preprocessed to improve image quality and reduce input voxel points. Then, 3D coarse segmentation fully convolutional network and cascaded finely segmentation convolutional neural network were used to complete multi-vertebrae segmentation and classification. RESULTS The results of this paper were compared with the other methods on the same datasets. Experimental results demonstrated that the Dice similarity coefficient (DSC) in this paper is 94.84%, higher than the V-net and 3D U-net. CONCLUSION Method of this paper has certain advantages in automatically and accurately segmenting vertebrae regions of CT images. Due to the easy acquisition of spine CT images. It was proven to be more conducive to clinical application of treatment that uses our segmentation model to obtain vertebrae regions, combining with the subsequent 3D reconstruction and printing work. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.BACKGROUND There are a few studies about the evaluation of maxillary first premolars internal structure with micro-computed tomography (micro-CT). The aim of this study was to assess morphological features of the pulp chamber in maxillary first premolar teeth using micro- CT. METHODS Extracted 15 maxillary first premolar teeth were selected from the patients who were in different age groups. The distance between the pulp orifices, the diameter of the pulp and the width of the pulp chamber floor were measured on the micro-CT images with the slice thickness of 13.6 µm. The number of root canal orifices and the presence of isthmus were evaluated. RESULTS The mean diameter of orifices was 0.73 mm on the buccal side while it was 0.61 mm on palatinal side. The mean distance between pulp orifices was 2.84 mm. The mean angle between pulp orifices was -21.53°. The mean height of pulp orifices on the buccal side was 4.32 mm while the mean height of pulp orifices on the palatinal side was 3.56 mm. The most observed shape of root canal orifices was flattened ribbon. No isthmus was found in specimens. CONCLUSION Minor anatomical structures can be evaluated in more detail with micro-CT. The observation of the pulp cavity was analyzed using micro-CT. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.BACKGROUND Left ventricular diastolic dysfunction (LVDD) is a common abnormality among patients in T2DM. AIMS We aimed to evaluate the feasibility of coronary computed tomography angiography (CCTA) for the assessment of LVDD in type 2 diabetes mellitus (T2DM) patients. METHODS 80 consecutive T2DM patients who were referred for a clinically dual-source CCTA examination to evaluate suspected coronary artery disease and also underwent 2D echocardiography within 7 days of CCTA inclusion and exclusion criteria, were performed. Correlation between CCTA and echocardiography was tested through linear regression and Bland-Altman analysis. RESULTS In total, 60 T2DM patients were included for the analysis. Pearson correlation showed good correlation for E (r = 0.28; P = 0.028), E/A (r = 0.69; P less then 0.01); E (r = -0.06; P = 0.776), E/A (r = 0.54; P = 0.003) and E (r = 0.64; P less then 0.01), E/A (r = 0.83; P less then 0.01) in three groups, respectively. Overall, diagnostic accuracy for assessment in CCTA of diastolic dysfunction was 79.76% (95% CI 68%-91%), 71.43% (95% CI 58%-85%) and 87.50 (95% CI 79%-96%) in three groups. CONCLUSION The presented study proved that CCTA showed good correlations in the estimation of LV filling pressures compared with echocardiography in T2DM patients. Accordingly, retrospectively ECG-gated CCTA may provide valuable information on the evaluation of LVDD in T2DM patients. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.BACKGROUND Image evaluation of scar tissue plays a significant role in the diagnosis of cardiovascular diseases. Segmentation of the scar tissue is the first step towards evaluating the morphology of the scar tissue. Then, with the use of CT images, the deep learning approach can be applied to identify possible scar tissue in the left ventricular endocardial wall. OBJECTIVES To develop an automated method for detecting the endocardial scar tissue in the left ventricular using Deep learning approach. METHODS Pixel values of the endocardial wall for each image in the sequence were extracted. Morphological operations, including defining regions of the endocardial wall of the LV where scar tissue could predominate, were performed. Convolutional Neural Networks (CNN) is a deep learning application, which allowed choosing appropriate features from delayed enhancement cardiac CT images to distinguish between endocardial scar and healthy tissues of the LV by applying pixel value-based concepts. RESULTS We achieved 89.23% accuracy, 91.11% sensitivity, and 87.75% specificity in the detection of endocardial scars using the CNN-based method. LDC203974 nmr CONCLUSION Our findings reveal that the CNN-based method yielded robust accuracies in LV endocardial scar detection, which is currently the most extensively used pixel-based method of deep learning. This study provides a new direction for the assessment of scar tissue in imaging modalities and provides a potential avenue for clinical adaptations of these algorithms. Additionally this methodology, in comparison with those in the literature, provides specific advantages in its translational ability to clinical use. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.BACKGROUND Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. OBJECTIVE The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. METHODS Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. RESULTS Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. CONCLUSION The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.PURPOSE The study aims to investigate the specificity, sensitivity, and accuracy of nuclear medicine, CT scan, and ultrasonography to diagnose the disorders related to the thyroid gland. METHODOLOGY The study is based on the retrospective approach of recruiting 52 patients suffering from thyroid disorders. The demographic details of each patient have been recorded. Moreover, the results of previously conducted nuclear medicine scan, CT scan, and ultrasound have also been assessed. The findings of all the tests have been compared to evaluate and compare their sensitivity, specificity, and accuracy. RESULTS A total of 52 patients were recruited for the study among which 41 were female and 11 were males. The findings of SPECT and MRI were compared, which revealed that MRI possessed 38.8% sensitivity and 22.22% specificity. As compared to the findings of CT scan, increased specificity (71.42%) and sensitivity (70.96%) have been identified in MRI. CONCLUSION There is an increase in the sensitivity and specificity of MRI outcomes as compared to the nuclear medicine and CT scan. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.BACKGROUND Measuring cornea thickness is an essential parameter for patients undergoing refractive Laser-Assisted in SItu Keratomileusis (LASIK) surgeries. DISCUSSION This paper describes about the various available imaging and non-imaging methods for identifying cornea thickness and explores the most optimal method for measuring it. Along with the thickness measurement, layer segmentation in the cornea is also an essential parameter for diagnosing and treating eye-related disease and problems. The evaluation supports surgical planning and estimation of the corneal health. After surgery, the thickness estimation and layer segmentation are also necessary for identifying the layer surface disorders. CONCLUSION Hence the paper reviews the available image processing techniques for processing the corneal image for thickness measurement and layer segmentation. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.BACKGROUND Despite the fact that lithium is not a biologically essential metallic element, its pharmacological properties are well known and human exposure to lithium is increasingly possible because of its used in aerospace industry and in batteries Objective Lithium-protein interactions are therefore interesting and the surveys of the structures of lithium-protein complexes is described in this paper. METHOD A high quality non-redundant set of lithium containing protein crystal structures was extracted from the Protein Data Bank and the stereochemistry of the lithium first coordination sphere was examined in detail. RESULT Four main observations were reported (i) lithium interacts preferably with oxygen atoms; (ii) preferably with side-chain atoms; (iii) preferably with Asp or Glu carboxylates; (iv) the coordination number tends to be four with stereochemical parameters similar to those observed in small molecules containing lithium Conclusion Although structural information on lithium-protein, available from the Protein Data Bank, is relatively scarce, these trends appears to be so clear that one may suppose that they will be confirmed by further data that will join the Protein Data Bank in the future.