Ge Precision Healthcare LLC

Máté Fejes

Gopal Biligeri Avinash

Rakesh Mullick

Krishna Seetharam Shriram

Vikram Reddy Melapudi

Ravi Soni

Tao Tan

Bipul Das

Pál Tegzes

Lehel Ferenczi

Sohan Rashmi Ranjan

Utkarsh Agrawal

Zita Herczeg

Barbara Darázs

Dániel Attila Szabó

László Ruskó

Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.

Multimodality image processing techniques for training image data generation and usage thereof for developing mono-modality image inferencing models

Methods and systems are provided for inferring thickness and volume of one or more object classes of interest in two-dimensional (2D) medical images, using deep neural networks. In an exemplary embodiment, a thickness of an object class of interest may be inferred by acquiring a 2D medical image, extracting features from the 2D medical image, mapping the features to a segmentation mask for an object class of interest using a first convolutional neural network (CNN), mapping the features to a thickness mask for the object class of interest using a second CNN, wherein the thickness mask indicates a thickness of the object class of interest at each pixel of a plurality of pixels of the 2D medical image; and determining a volume of the object class of interest based on the thickness mask and the segmentation mask.

System and methods for inferring thickness of object classes of interest in two-dimensional medical images using deep neural networks

System and methods for inferring thickness of anatomical classes of interest in two-dimensional medical images using deep neural networks

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Szeged, Hungary

Máté Fejes