Abstract: The present disclosure relates to methods, apparatuses, and computer programs for processing computed tomography images. Precise segmentation of the left atrium (LA) in computed tomography (CT) images constitutes a crucial preparatory step for catheter ablation in atrial fibrillation (AF). We aim to apply deep convolutional neural networks (DCNNs) to automate the LA detection/segmentation procedure and create a three-dimensional (3D) geometries. The deep learning provides an efficient and accurate way for automatic contouring and LA volume calculation based on the construction of the 3D LA geometry. Non-pulmonary vein (NPV) trigger has been reported as an important predictor of recurrence post atrial fibrillation (AF) ablation. Elimination of NPV triggers can reduce the post-ablation AF recurrence. The deep learning was applied in pre-ablation pulmonary vein computed tomography (PVCT) geometric slices to create a prediction model for NPV triggers in patients with paroxysmal atrial fibrillation (PAF).

Abstract: The present disclosure relates to methods, apparatuses, and computer programs for processing computed tomography images. Precise segmentation of the left atrium (LA) in computed tomography (CT) images constitutes a crucial preparatory step for catheter ablation in atrial fibrillation (AF). We aim to apply deep convolutional neural networks (DCNNs) to automate the LA detection/segmentation procedure and create a three-dimensional (3D) geometries. The deep learning provides an efficient and accurate way for automatic contouring and LA volume calculation based on the construction of the 3D LA geometry. Non-pulmonary vein (NPV) trigger has been reported as an important predictor of recurrence post atrial fibrillation (AF) ablation. Elimination of NPV triggers can reduce the post-ablation AF recurrence. The deep learning was applied in pre-ablation pulmonary vein computed tomography (PVCT) geometric slices to create a prediction model for NPV triggers in patients with paroxysmal atrial fibrillation (PAF).

Abstract: A method for estimating stress of an electronic component. An electronic component including first and second elements and conductive bumps is provided. Each conductive bump has two surfaces connected to the first and second elements respectively. Two adjacent conductive bumps have a pitch therebetween. The conductive bumps includes a first conductive bump and second conductive bumps. A stress value of the first conductive bump related to a testing parameter is calculated. A stress value of each second conductive bump related to the testing parameter is calculated according to a first calculating formula. The first calculating formula is ? 2 = L D - 2 ? r ? ? 1 , ?2 is the stress of each second conductive bump, L is a beeline distance between each second conductive bump and the first conductive bump, D is an average value of the pitches of the conductive bumps, r is a radius of each surface, and ?1 is the stress value of the first conductive bump.

Abstract: A method for estimating stress of an electronic component. An electronic component including first and second elements and conductive bumps is provided. Each conductive bump has two surfaces connected to the first and second elements respectively. Two adjacent conductive bumps have a pitch therebetween. The conductive bumps includes a first conductive bump and second conductive bumps. A stress value of the first conductive bump related to a testing parameter is calculated. A stress value of each second conductive bump related to the testing parameter is calculated according to a first calculating formula. The first calculating formula is ? 2 = L D - 2 ? r ? ? 1 , ?2 is the stress of each second conductive bump, L is a beeline distance between each second conductive bump and the first conductive bump, D is an average value of the pitches of the conductive bumps, r is a radius of each surface, and ?1 is the stress value of the first conductive bump.

Abstract: An apparatus, system and method which use a portable sensor to monitor the physical and mental reactions of a person in motion. The physical and mental reactions in motion will be transformed into the related physical and mental characteristic vectors, which are used to build up a personalized physical and mental database. The database executes a self-learning algorithm to output a set of physical and mental weighting factors. After executing the set of physical and mental weighting factors and physical and mental characteristic vectors, an embedded calculator incorporated in the portable sensor gives a warning signal if an abnormal situation is detected.