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US 2020/0265263 A1Published application
Deep learning volume quantifying methods and apparatus
AssigneeSiemens Healthcare Diagnostics Inc.
InventorsStefan Kluckner, Yao-Jen Chang, Kai Ma, Vivek Singh, Terrence Chen, Benjamin S. Pollack
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Abstract
A neural network-based method for quantifying a volume of a specimen. The method includes providing a specimen, capturing images of the specimen, and directly classifying to one of a plurality of volume classes or volumes using a trained neural network. Quality check modules and specimen testing apparatus adapted to carry out the volume quantification method are described, as are other aspects.
Claims20
A method of quantifying a specimen, comprising:
- providing a specimen container at an imaging location, the specimen container containing the specimen;
- capturing multiple high dynamic range images of the specimen and storing the multiple high dynamic range images as image data;
- operating on the image data with a neural network;
- and
- classifying the specimen with the neural network into one of:
- a plurality of volume classes, and
- a volume of the specimen.
4 more elements
A quality check module, comprising:
- one or more image capture devices configured to capture images of the specimen at multiple spectra having different nominal wavelengths, at multiple exposures, and from one or more viewpoints;
- and
- a computer operatively coupled to the one or more image capture devices and operable to:
- select optimally-exposed pixels from the images at the different exposures and at each of the multiple spectra and generate optimally-exposed image data for each of the multiple spectra,
- operate on the image data with a neural network;
- and
- classify the specimen with the neural network into one of:
- a plurality of volume classes, and
- a volume of the specimen.
6 more elements
A specimen testing apparatus, comprising:
- a track;
- and
- a quality check module on the track, the quality check module including:
- one or more image capture devices configured to capture images of the specimen at multiple spectra having different nominal wavelengths, at multiple different exposures, and from one or more viewpoints, and
- a computer configured and operable to:
- select optimally-exposed pixels from the images at the multiple different exposures at each of the multiple spectra to generate optimally-exposed image data for each of the multiple spectra and viewpoint,
- operate on the image data with a neural network;
- and
- classify the specimen with the neural network into one of:
- a plurality of volume classes, and
- a volume of the specimen.
8 more elements
Dependencies are inferred from the claim text.