International Journal of Biomedical Imaging最新文献

Breast MRI possesses high sensitivity for detecting breast cancer among the current clinical modalities and is an indispensable imaging practice. Breast MRI comprises diffusion-weighted imaging, ultrafast, and T2 weighted and T1 weighted CE (contrast-enhanced) imaging that may be utilized for improving the characterization of the lesions. This multimodal evaluation of breast lesions enables outstanding discrimination between the malignant and benign and malignant lesions. The expanding indications of breast MRI confirm the far superiority of MRI in preoperative staging, especially in the estimation of tumour size and identifying tumour foci in the contralateral and ipsilateral breast. Recent studies depicted that experts can meritoriously utilize this tool for improving breast cancer surgery despite their existence of no significant long term outcomes. For managing the, directly and indirectly, associated screening cost, abbreviated protocols are found to be more beneficial. Further, in some of the patients who were treated with neoadjuvant chemotherapy, breast MRI is utilized for documenting response. It is therefore essential to realise that oncological screening must be easily available, cost-effective, and time-consuming. Earlier detection of this short sequence protocol leads to prior and early breast cancer disease in high risky female populations like women with dense breasts, prehistoric evidence, etc. This proper utilization of AP reduces unnecessary mastectomies. Hence, this review focused on the explorative information for strongly suggesting the benefits of AP breast MRI compared to full diagnostic protocol MRI.

Background: Accurate placement of pedicle screws in the subaxial cervical spine requires precise understanding of vertebra anatomy. Little is known about the morphometric characteristics of the subaxial cervical pedicle in the Ugandan population. The objective of the study was to determine the morphometric dimensions of pedicles in the subaxial cervical spine among the adult Ugandan population.

Methods: We conducted a cross-sectional study from March to November 2019 among adult Ugandans with a normal cervical CT scan at Nsambya hospital in Kampala. Eligible participants were consecutively recruited into the study. Data on baseline characteristics and pedicle dimensions from the CT scan finding was collected using a structured questionnaire. Data was analysed using Stata 13.0. Pedicle dimensions for the different levels of subaxial cervical vertebrae were summarised as means and standard deviations, the Mann-Whitney test was used to compare pedicle dimensions for the different vertebra levels among females and males on both right and left sides, and the level of significance was set at 0.05.

Results: A total of 700 subaxial cervical pedicles (C3-C7) from 49 males and 21 female participants were studied. Pedicle width diameter showed cephalocaudal gradual increment from C3 1.65(0.63) mm to 3.46(0.75) mm at C7. Pedicle height also showed an increase caudally with smallest diameter at C3 (1.98(0.76) mm) and largest at C5 in females (3.67(6.42) mm) and at C7 in males (3.83(0.76) mm). The pedicle height was wider than the pedicle width at all levels. The pedicle chord length gradually increased caudally in both sexes ranging from 29.08(1.35) mm at C3 to 32.53(3.19) mm at C7. The axial angles were oriented medially and showed no consistent trend ranging between 50° and 53°. The sagittal angles decreased as one moved from C3 to C7. The dimensions of females were significantly smaller than in males.

Conclusion: Pedicle endosteal width was smaller than pedicle height dimensions at all levels. Pedicle cord length increased caudally. The pedicle dimensions, except angulations, were smaller in females than in males.

Among the different types of cancers, lung cancer is one of the widespread diseases which causes the highest number of deaths every year. The early detection of lung cancer is very essential for increasing the survival rate in patients. Although computed tomography (CT) is the preferred choice for lungs imaging, sometimes CT images may produce less tumor visibility regions and unconstructive rates in tumor portions. Hence, the development of an efficient segmentation technique is necessary. In this paper, water cycle bat algorithm- (WCBA-) based deformable model approach is proposed for lung tumor segmentation. In the preprocessing stage, a median filter is used to remove the noise from the input image and to segment the lung lobe regions, and Bayesian fuzzy clustering is applied. In the proposed method, deformable model is modified by the dictionary-based algorithm to segment the lung tumor accurately. In the dictionary-based algorithm, the update equation is modified by the proposed WCBA and is designed by integrating water cycle algorithm (WCA) and bat algorithm (BA).

Objective: While microCT evaluation of atherosclerotic lesions in mice has been formally validated, existing image processing methods remain undisclosed. We aimed to develop and validate a reproducible image processing workflow based on phosphotungstic acid-enhanced microCT scans for the volumetric quantification of atherosclerotic lesions in entire mouse aortas. Approach and Results. 42 WT and 42 apolipoprotein E knockout mouse aortas were scanned. The walls, lumen, and plaque objects were segmented using dual-threshold algorithms. Aortic and plaque volumes were computed by voxel counting and lesion surface by triangulation. The results were validated against manual and histological evaluations. Knockout mice had a significant increase in plaque volume compared to wild types with a plaque to aorta volume ratio of 0.3%, 2.8%, and 9.8% at weeks 13, 18, and 26, respectively. Automatic segmentation correlated with manual (r ² ≥ 0.89; p < .001) and histological evaluations (r ² > 0.96; p < .001).

Conclusions: The semiautomatic workflow enabled rapid quantification of atherosclerotic plaques in mice with minimal manual work.

Brain tumor is a deadly neurological disease caused by an abnormal and uncontrollable growth of cells inside the brain or skull. The mortality ratio of patients suffering from this disease is growing gradually. Analysing Magnetic Resonance Images (MRIs) manually is inadequate for efficient and accurate brain tumor diagnosis. An early diagnosis of the disease can activate a timely treatment consequently elevating the survival ratio of the patients. Modern brain imaging methodologies have augmented the detection ratio of brain tumor. In the past few years, a lot of research has been carried out for computer-aided diagnosis of human brain tumor to achieve 100% diagnosis accuracy. The focus of this research is on early diagnosis of brain tumor via Convolution Neural Network (CNN) to enhance state-of-the-art diagnosis accuracy. The proposed CNN is trained on a benchmark dataset, BR35H, containing brain tumor MRIs. The performance and sustainability of the model is evaluated on six different datasets, i.e., BMI-I, BTI, BMI-II, BTS, BMI-III, and BD-BT. To improve the performance of the model and to make it sustainable for totally unseen data, different geometric data augmentation techniques, along with statistical standardization, are employed. The proposed CNN-based CAD system for brain tumor diagnosis performs better than other systems by achieving an average accuracy of around 98.8% and a specificity of around 0.99. It also reveals 100% correct diagnosis for two brain MRI datasets, i.e., BTS and BD-BT. The performance of the proposed system is also compared with the other existing systems, and the analysis reveals that the proposed system outperforms all of them.

[This corrects the article DOI: 10.1155/2013/205494.].