Determination of illumination parameters in medical image rendering
AssigneeSiemens Healthineers AG
InventorSandra Sudarsky
Life of this patent
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Abstract
A convolution neural network, CNN, is trained to determine illumination parameters for image rendering of medical data. For training, a set of images are rendered using a set of rendering configurations, selected from the group of: a set of different camera parameters, a set of different transfer functions for assigning optical properties, like for example color and opacity, to original values of the raw data to be rendered, and a set of different illumination parameters. An evaluation score is computed for representing an amount of image information for each of the rendered images. The computed evaluation score for each rendered image and the rendering configurations applied for rendering the image are used to train the CNN.
Claims18
A method for training a convolution neural network (CNN) for determining illumination parameters for image rendering of medical data, the method comprising:
- receiving raw data for rendering;
- providing a set of three-dimensional-to-two-dimensional rendering configurations, selected from the group of:
- a set of different camera parameters, a set of different transfer functions for assigning optical properties to original values of the raw data to be rendered, and a set of different illumination parameters;
- rendering by a renderer a set of images using the set of provided rendering configurations;
- computing an evaluation score for representing an amount of image information for each of the rendered images independent of other rendered images;
- training the CNN using the computed evaluation score for each rendered image and the rendering configurations which have been applied for rendering each image, the training using losses as differences between the evaluations scores, wherein the training is executed by using an L2 loss as the loss on an entropy function as the evaluation score where ∥H(I(ωi,li))−H(I(ωo,lo)∥, where H(I(ωi,li) is an entropy of a respective rendered image rendered with lighting direction ωi and intensity li and where H(I(ωo,lo)) is an entropy of a ground truth image rendered with lighting direction ωo and intensity lo;
- and
- storing the CNN as trained from the training.
A method for determining illumination parameters for image rendering of medical data, the method comprising:
- receiving raw data for rendering, the raw data comprising the medical data;
- determining the illumination parameters with a trained convolutional neural network (CNN) configured by training to maximize an amount of image information present in a rendered image, the configuration by training having used a loss that was a difference between the amounts of imaging information in output and ground truth images, the loss having comprised an L2 loss as the loss on an entropv function as an evaluation score where ∥H(I(ωi,li))−H(I(ωo,lo)∥, where H(I(ωi,li)) is an entropy of a respective rendered image rendered with lighting direction ωi and intensity li and where H(I(ωo,lo)) was an entropy of a ground truth image rendered with lighting direction ωo and intensity lo;
- and
- rendering the rendered image using the determined illumination parameters.
A medical imaging system for determining illumination parameters for image rendering of medical data, the medical imaging system comprising:
- an input interface for receiving raw data to be rendered;
- a processor configured for applying a trained convolutional neural network (CNN) for determining the illumination parameters, the CNN configured by training, with a loss as a difference in amounts of information between output and ground truth images, to maximize an amount of image information present in the rendered image, the loss having comprised an L2 loss as the loss on an entropv function as an evaluation score where ∥H(I(ωi,li))−H(I(ωo,lo)∥, where H(I(ωi,li)) is an entropy of a respective rendered image rendered with lighting direction ωi and intensity li and where H(I(ωo,lo)) was an entropy of a ground truth image rendered with lighting direction ωo and intensity lo;
- and
- a renderer configured to render a medical image using the determined illumination parameters from the raw data.
Dependencies are inferred from the claim text.