A cell image segmentation method using scribble labels includes iteratively pre-training via an image segmentation network (U-Net) using a cell image and scribble labels indicating a cell region and a background region as training data, calculating an exponential moving average (EMA) of image segmentation prediction probabilities at a predetermined interval during the pre-training, self-training by assigning the cell region and the background region for which the EMA of image segmentation prediction probabilities is over a preset threshold to be a pseudo-label, and iteratively refining the image segmentation prediction probability based on a scribbled loss (L.sub.sp) obtained through a result of the training and an unscribbled loss (L.sub.up). Accordingly, it is possible to achieve cell image segmentation with high reliability using only scribble labels.

A method and a system automatically generate a digital representation of an annulus structure of a valve from a segmented digital representation of a human internal heart. The basis for the segmented digital representation is multi-slice computed tomography image data. The method includes automatically determining, for at least a first effective time point, based on a segmentation, i.e. labels, of a provided input segmented digital representation, a candidate plane, and/or a candidate orientation vector together with a candidate center point, arranged with respect to the input segmented digital representation for the first effective time point, and candidate points for the annulus structure are determined automatically. From the candidate points acting as support points, a candidate spline interpolation is generated which is then adapted based on the input segmented digital representation. The digital representation of the annulus structure is then generated based on the adapted candidate spline interpolation.

The present application discloses a method and apparatus for determining a route, a device and a computer storage medium, and relates to the field of big data. An implementation includes: acquiring route description information input by a user, and acquiring a route contour with the route description information; matching the route contour in road network data to obtain the route matched with the route contour, so as to generate recommended routes, wherein this operation specifically includes: extracting intersection points in the route contour, each of which is formed by intersecting at least two lines; selecting one of the intersection points, and querying the road network data using a corresponding angle sequence of the selected intersection point in the route contour, so as to obtain intersections matched with the selected intersection point as candidate intersections; traversing the candidate intersections, fixedly mapping the selected intersection point to the positions of the candidate intersections, equally scaling the route contour in the road network data, and recording mapped road information if all lines of the route contour are mapped onto connected roads; and generating the recommended routes with the recorded road information.

The present invention relates to a method for obtaining a picture for measuring a body size and a body size measurement method using same, wherein, by providing a guiding line through a capturing screen of a terminal, an optical image for measuring a body size is obtained, and the edge of a user body is extracted from the obtained image and used to obtain a 3D image of the user body, and thus an accurate body size of a user may be calculated.

Systems and methods for detecting features in ultrasound images and delineating boundaries that representative of the detected features. A method can include determining a boundary of a feature in a displayed ultrasound image, determining a closed polygon that represents the feature based on the boundary, and determining information of the feature using the dimensions of the closed polygon. The ultrasound image and graphical representations of the feature (e.g., the boundary, the closed polygon) can be displayed in a user interface on a touch screen display. The method can include receiving user input indicative of a location of the feature, and adjusting the feature boundary and closed polygon based on user input. The method can also include determining an area and/or a perimeter of the feature based on the closed polygon.

There is provided a method and apparatus for refining a model of an anatomical structure in an image. A model for the anatomical structure in the image is acquired. The model comprises a plurality of control points, each control point corresponding to a feature in the anatomical structure. The model is placed in the image with respect to the anatomical structure. Based on a user input received to adjust the model in the image, a position of at least one of the plurality of control points is adjusted to alter a shape of the model to the anatomical structure in the image, wherein adjustment of the position of one or more of the at least one control points is restricted based on information relating to the at least one control point.

A method and system for automated visual inspection, include receiving, from a camera imaging an inspection line, an image of the inspection line. The image includes an item on the inspection line personal or confidential image data. A processor produces from the image of the inspection line a reduced image, which does not include the personal or confidential image data, and inputs the reduced image to an inspection process.

A method for training a deep learning model includes: acquiring (n+1)th first label information output by a first model, the first model having been subject to n rounds of training, and acquiring (n+1)th second label information output by a second model, the second model having been subject to n rounds of training, the n being an integer greater than 1; generating an (n+1)th training set of the second model based on training data and the (n+1)th first label information, and generating an (n+1)th training set of the first model based on the training data and the (n+1)th second label information; performing an (n+1)th round of training on the second model by inputting the (n+1)th training set of the second model to the second model, and performing the (n+1)th round of training on the first model by inputting the (n+1)th training set of the first model to the first model.

Techniques are disclosed for deep neural network (DNN) based interactive image matting. A methodology implementing the techniques according to an embodiment includes generating, by the DNN, an alpha matte associated with an image, based on user-specified foreground region locations in the image. The method further includes applying a first DNN subnetwork to the image, the first subnetwork trained to generate a binary mask based on the user input, the binary mask designating pixels of the image as background or foreground. The method further includes applying a second DNN subnetwork to the generated binary mask, the second subnetwork trained to generate a trimap based on the user input, the trimap designating pixels of the image as background, foreground, or uncertain status. The method further includes applying a third DNN subnetwork to the generated trimap, the third subnetwork trained to generate the alpha matte based on the user input.

Systems and methods for anatomical structure segmentation in medical images using multiple anatomical structures, instructions and segmentation models.