Patents by Inventor Wei-Shiang Chen

Wei-Shiang Chen has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Methods, apparatuses, and computer programs for processing pulmonary vein computed tomography images
    Patent number: 11869208
    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).
    Type: Grant
    Filed: March 15, 2021
    Date of Patent: January 9, 2024
    Assignees: TAIPEI VETERANS GENERAL HOSPITAL, National Yang Ming Chiao Tung University
    Inventors: Horng-Shing Lu, Chih-Min Liu, Shih-Lin Chang, Shih-Ann Chen, Yenn-Jiang Lin, Hung-Hsun Chen, Wei-Shiang Chen
  • METHODS, APPARATUSES, AND COMPUTER PROGRAMS FOR PROCESSING PULMONARY VEIN COMPUTED TOMOGRAPHY IMAGES
    Publication number: 20210287365
    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).
    Type: Application
    Filed: March 15, 2021
    Publication date: September 16, 2021
    Applicants: TAIPEI VETERANS GENERAL HOSPITAL, National Yang Ming Chiao Tung University
    Inventors: Horng-Shing Lu, Chih-Min Liu, Shih-Lin Chang, Shih-Ann Chen, Yenn-Jiang Lin, Hung-Hsun Chen, Wei-Shiang Chen