Estimation of object location probability for object detection using brightness feature only

Most existing object detection methods use features such as color, shape, and contour. If there are no consistent features can be used, we need a new object detection method. Therefore, in this paper, we propose a new method for estimating the probability that an object can be located for object detection and generating an object location probability map using only brightness in a gray image. To evaluate the performance of the proposed method, we applied it to gallbladder detection. Experimental results showed 98.02% success rate for gallbladder detection in ultrasonogram. Therefore, the proposed method accurately estimates the object location probability and effectively detected gallbladder

Ultrasound image based human gallbladder 3D modelling along with volume and stress level assessment

Purpose: Three-dimensional (3D) gallbladder (GB) geometrical models are essential to GB motor function evaluation and GB wall biomechanical property identification by employing finite element analysis (FEA) in GB disease diagnosis with ultrasound systems. Methods for establishing such 3D geometrical models based on static two-dimensional (2D) ultrasound images scanned along the long-axis/sagittal and short-axis/transverse cross-sections in routine GB disease diagnosis at the beginning of emptying phase have not been documented in the literature so far. Methods: Based on two custom MATLAB codes composed, two images were segmented manually to secure two sets of the scattered points for the long- and short-axis GB cross-section edges; and the points were best fitted with a piecewise cubic spline function, and the short-axis cross-section edges were lofted along the long-axis to yield a 3D geometrical model, then GB volume of the model was figured out. The model was read into SolidWorks for real surface generation and involved in ABAQUS for FEA. Results: 3D geometrical models of seven typical GB samples were established. Their GB volumes are with 15.5% and − 4.4% mean errors in comparison with those estimated with the ellipsoid model and sum-of-cylinders method but can be correlated to the latter very well. The maximum first principal in-plane stress in the 3D models is higher than in the ellipsoid model by a factor of 1.76. Conclusions: A numerical method was put forward here to create 3D GB geometrical models and can be applied to GB disease diagnosis and GB shape analysis with principal component method potentially in the future

Can Radiological Visceral Adiposity Analysis in Acute Pancreatitis Aid in Identifying Underlying Etiology? Assessing the Clinical Potential of Quantitative Radiological Analyses of Visceral Adiposity

Purpose: This study aims to investigate the relationship between visceral adiposity and the possible etiologies of acute pancreatitis. Obesity creates low-grade inflammation and evidence supports an association between obesity and inflammatory conditions such as pancreatitis. CT imaging is utilized in assessing pancreatitis severity and complications but also offers the chance to quantitatively measure visceral fat area (VFA) and subcutaneous fat area (SFA). Given the metabolic role that fat plays, we hypothesized that different body fat distributions, as measured by these areas, may be associated with different etiologies. Further, this also allows us to explore a relationship between severity, etiology, and the fat distributions in patients with acute pancreatitis.  Method: Retrospective observational cohort study of all patients admitted to a single center. The VFA, SFA, their ratio (VFA/SFA) and total fat area (TFA) were calculated using a semi-automatic algorithm.  Results: 518 patients were admitted with acute pancreatitis over a three-year period. 177 patients underwent CT imaging. Gallstone pancreatitis patients had higher VFA and TFA measurements while alcoholic pancreatitis patients had lower measurements. Patients with pancreatitis with no clear cause had the lowest VFA/SFA ratio. Increasing VFA was associated with increasing severity in a univariate logistic regression model (p = 0.01) but this association diminished in a multivariate model accounting for etiology (p = 0.09). Conclusion: The pattern of fat distribution differs amongst the etiologies of acute pancreatitis, as this likely reflects multiple contributing pathogenic mechanisms. Patients with gallstone pancreatitis had disproportionately more visceral fat, alcohol had the least overall fat, and those without a clear cause had the lowest VFA/SFA ratio. Etiology is strongly associated with body fat distribution. Severity is associated with increased visceral fat, but much less so when etiology is controlled for. The radiological assessment of fat distribution thus can give clues to associated etiology

U-Net and its variants for medical image segmentation: theory and applications

U-net is an image segmentation technique developed primarily for medical image analysis that can precisely segment images using a scarce amount of training data. These traits provide U-net with a very high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in all major image modalities from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. As the potential of U-net is still increasing, in this review we look at the various developments that have been made in the U-net architecture and provide observations on recent trends. We examine the various innovations that have been made in deep learning and discuss how these tools facilitate U-net. Furthermore, we look at image modalities and application areas where U-net has been applied.Comment: 42 pages, in IEEE Acces

U-net and its variants for medical image segmentation: A review of theory and applications

U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net