Siemens Healthcare Gmbh

Siemens Healthineers Ag

Jiangping Wang

Vivek Kumar Singh

Terrence Chen

Yao-Jen Chang

Kai Ma

Ankur Kapoor

Andreas Wimmer

Andreas Krauss

Bogdan Georgescu

Shaohua Kevin Zhou

Shanhui Sun

Stefan Kluckner

Birgi Tamersoy

Mingqing Chen

Ruhan Sa

Oliver Lehmann

Yifan Wu

Elena Balashova

A 3D shape is reconstructed from a topogram. A generative network is machine trained. The generative network includes a topogram encoder for inputting the topogram and a decoder to output the 3D shape from the output of the encoder. For training, one or more other encoders are included, such as for input of a mask and/or input of a 3D shape as a regularlizer. The topogram encoder and decoder are trained with the other encoder or encoders outputting to the decoder. For application, the topogram encoder and decoder as trained, with or without the encoder for the mask and without the encoder for the 3D shape, are used to estimate the 3D shape for a patient from input of the topogram for that patient.

Three-dimensional shape reconstruction from a topogram in medical imaging

Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Dense body marker estimation from camera data for patient positioning in medical imaging

A system and method includes operation of a generation network to generate first generated computed tomography data based on a first instance of surface data, determination of a generation loss based on the first generated computed tomography data and on a first instance of computed tomography data which corresponds to the first instance of surface data, operation of a discriminator network to discriminate between the first generated computed tomography data and the first instance of computed tomography data, determination of a discriminator loss based on the discrimination between the first generated computed tomography data and the first instance of computed tomography data, determination of discriminator gradients of the discriminator network based on the discriminator loss, and updating of weights of the generation network based on the generation loss and the discriminator gradients.

System for predicting computed tomography data from surface data

In scan data retrieval, a mesh is fit () to surface data of a current patient, such as data from an optical or depth sensor (). Meshes are also fit () to medical scan data, such as fitting () to skin surface segments of computed tomography data. The meshes or parameters derived from the meshes may be more efficiently compared () to identify () a previous patient with similar body shape and/or size. The scan configuration () for that patient, or that patient as altered to account for differences from the current patient, is used. In some embodiments, the parameter vector used for searching () includes principle component analysis coefficients. In further embodiments, the principle component analysis coefficients may be projected to a more discriminative space using metric learning.

Scan data retrieval with depth sensor data

A system and method includes creation of a combined network comprising an image segmentation network and an image representation network, the combined network to generate an image descriptor based on an input query image, training of the combined network based on a plurality of first images and a segmentation mask associated with each of the plurality of first images, reception of a first input query image, use of the combined network to generate an image descriptor based on the first input query image, determination of a matching image descriptor from a plurality of stored image descriptors, determination of a camera pose associated with the matching image descriptor, registration of the first input query image with image data based on the determined camera pose, generation of a composite image based on the registered first input query image and image data, and presentation of the composite image.

Segmentation and representation network for pose discrimination

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Plainsboro, NJ, United States of America

Jiangping Wang