2results about How to "Helpful for analysis" patented technology

The application discloses a kind of three-dimensional full-automatic human body composition analysis method, system and medium based on CT image, method includes: acquisition CT image data and mark;Spine segmentation model is constructed and is trained, the spine segmentation model is in the jump connection part of pre-set U-Net network joins attention module and learns the overall information of spine, to make that spine segmentation model obtains better segmentation effect;Construct body composition segmentation model, and the same loss function and depth supervision method as spine segmentation model is used to train the body composition segmentation model;Based on the spine positioning result and body composition segmentation result obtained in well-trained spine segmentation model and body composition segmentation model, calculate human body composition related index and mutual relationship based on spine positioning result and body composition segmentation result.The application realizes full-automatic process by intelligent positioning to spine, simultaneously provides selectable interface, and it is convenient for user to carry out the analysis of specific organ or tissue.

The application discloses a time anomaly detection method, device, medium and product, comprising: acquiring a reference time of a current sampling moment as a time observation value of the current sampling moment; determining a local time of the current sampling moment based on a local time counter; determining a time prediction value of the current sampling moment based on a linear time relationship model and the local time of the current sampling moment, wherein the linear time relationship model is a relationship model between the local time and the reference time; determining a difference value between the time prediction value of the current sampling moment and the time observation value, obtaining a prediction residual of the current sampling moment; adding the prediction residual of the current sampling moment to a prediction residual sequence, and performing time anomaly detection based on the prediction residual sequence. In this way, the sensitivity of time anomaly detection can be improved, and the type of anomaly can be identified.