Siemens Medical Solutions Usa, Inc.

Siemens Healthcare Gmbh

Z2sky Technologies Inc.

Haofu Liao

Shaohua Kevin Zhou

Yefeng Zheng

Gareth Funka-Lea

Yucheng Tang

Jin-hyeong Park

Sri Venkata Anirudh Nanduri

Wei-An Lin

For three-dimensional segmentation from two-dimensional intracardiac echocardiography imaging, the three-dimension segmentation is output by a machine-learnt multi-task generator. Rather than the brute force approach of training the generator from 2D ICE images to output a 2D segmentation, the generator is trained from 3D information, such as a sparse ICE volume assembled from the 2D ICE images. Where sufficient ground truth data is not available, computed tomography or magnetic resonance data may be used as the ground truth for the sample sparse ICE volumes. The generator is trained to output both the 3D segmentation and a complete volume (i.e., more voxels represented than in the sparse ICE volume). The 3D segmentation may be further used to project to 2D as an input with an ICE image to another network trained to output a 2D segmentation for the ICE image. Display of the 3D segmentation and/or 2D segmentation may guide ablation of tissue in the patient.

Three-dimensional segmentation from two-dimensional intracardiac echocardiography imaging

For three-dimensional segmentation from two-dimensional intracardiac echocardiography imaging, the three-dimension segmentation is output by a machine-learnt multi-task generator. The machine-learnt multi-task generator is trained from 3D information, such as a sparse ICE volume assembled from the 2D ICE images. The machine-learnt multi-task generator is trained to output both the 3D segmentation and a complete volume. The 3D segmentation may be used to project to 2D as an input with an ICE image to another network trained to output a 2D segmentation for the ICE image. Display of the 3D segmentation and/or 2D segmentation may guide ablation of tissue in the patient.

An apparatus and method for coupled medical image formation and medical image signal recovery using a dual domain network is disclosed. The dual-domain network includes a first deep neural network (DNN) to perform signal recovery in a sensor signal domain and a second DNN to perform signal recovery in an image domain. A sensor signal is acquired by a sensor of a medical imaging device. A refined sensor signal is generated from the received sensor signal using the first DNN. A first reconstructed medical image is generated from the received sensor signal. A second reconstructed medical image is generated from the refined sensor signal generated by the first DNN. An enhanced medical image is generated based on the both the first reconstructed medical image and the second reconstructed medical image using the second DNN. The enhanced medical image generated by the second DNN is displayed.

Unified dual-domain network for medical image formation, recovery, and analysis

A method and apparatus for automated reconstruction of a 3D computed tomography (CT) volume from a small number of X-ray images is disclosed. A sparse 3D volume is generated from a small number of x-ray images using a tomographic reconstruction algorithm. A final reconstructed 3D CT volume is generated from the sparse 3D volume using a trained deep neural network. A 3D segmentation mask can also be generated from the sparse 3D volume using the trained deep neural network.

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Rochester, NY, United States of America

Haofu Liao