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Sepsis is a severe immune system reaction to infection and a major cause of ICU-related fatalities. Because of the high mortality, high cost of treatment, and complex aetiology of sepsis, sepsis has a huge impact on healthcare. Some of the health complications in sepsis are abnormal cardiac functions, hypoperfusion, hypotension, tissue damage, multiple organ failure, and ultimately death. Individuals with weak immune systems and chronic medical conditions are highly vulnerable to sepsis. In sepsis, a patient shows the extreme immune response in the initial stage while prolonged immunosuppression in the later stages. Sepsis-driven immunosuppression ushers in death because sepsis cases develop secondary infections postrecovery. The later immunocompromised state in sepsis is attributed myeloid-derived suppressor cell upregulation and reduced immune activity displayed by lymphocytes (lymphocyte anergy). As a result, it is currently suggested that regulating the immune response is a better therapeutic approach than focusing on inflammation to improve the immune system's capacity to fight infections. Moreover, finding novel and accurate prognostic biomarkers that can help in rapid sepsis diagnoses and deciding better therapeutic strategies will significantly lower clinical case mortality rates. [ABSTRACT FROM AUTHOR]

Due to the complex topological structure of the coronary artery and the uneven distribution of the contrast agent, the angiography images are inevitably blurred and has low contrast, which causes great difficulty in process of segmentation. For this problem, a two-steps segmentation algorithm based on Hessian matrix and level set is proposed in this paper. Firstly, potential blood vessels of coronary images are preliminary extracted via Hessian matrix eigenvalues feature vectors of the geometric features and the response function. Then a novel regularization and area constraint is introduced into the local data energy fitting functional. Finally, the precision of Coronary Artery image is obtained in the evolution of the level set function. Experiments show that our proposed algorithm has better performance to these comparison segmentation algorithms. [ABSTRACT FROM AUTHOR]

Background: Pulmonary nodules in computerized tomography (CT) images are potential manifestations of lung cancer. Segmentation of potential nodule objects is the first necessary and crucial step in computer-aided detection system of pulmonary nodules. The segmentation of various types of nodules, especially for ground-glass opacity (GGO) nodules and juxta-vascular nodules, present various challenges. The nodule with GGO characteristic possesses typical intensity inhomogeneity and weak edges, which is difficult to define the boundary; the juxta-vascular nodule is connected to a vessel, and they have very similar intensities. Traditional segmentation methods may result in the problems of boundary leakage and a small volume over-segmentation. This paper deals with the above mentioned problems. Methods: A novel segmentation method for pulmonary nodules is proposed, which uses an adaptive local region energy model with probability density function (PDF)-based similarity distance and multi-features dynamic clustering refinement method. Our approach has several novel aspects: (1) in the proposed adaptive local region energy model, the local domain for local energy model is selected adaptively based on k-nearest-neighbour (KNN) estimate method, and measurable distances between probability density functions of multi-dimension features with high class separability are used to build the cost function. (2) A multi-features dynamic clustering method is used for the segmentation refinement of juxta-vascular nodules, which is based on the nodule segmentation using active contour model (ACM) with adaptive local region energy and vessel segmentation using flow direction feature (FDF)-based region growing method. (3) it handles various types of nodules under a united framework. Results: The proposed method has been validated on a clinical dataset of 113 chest CT scans that contain 157 nodules determined by a ground truth reading process, and evaluating the algorithm on the provided data leads to an average Tanimoto/Jaccard error of 0.17, 0.20 and 0.24 for GGO, juxta-vascular and GGO juxta-vascular nodules, respectively. Conclusions: Experimental results show desirable performances of the proposed method. The proposed segmentation method outperforms the traditional methods. [ABSTRACT FROM AUTHOR]