Siemens Healthineers Ag

Florin-Cristian Ghesu

Dorin Comaniciu

Bogdan Georgescu

Vivek Kumar Singh

Rui Liao

Eli Gibson

Thomas Re

Yue Zhang

Sureerat Reaungamornrat

Caius Constantin Suliman

Marc Demoustier

Systems and methods for training a machine learning based segmentation network are provided. A set of medical images, each depicting an anatomical object, in a first modality is received. For each respective medical image of the set of medical images, a synthetic image, depicting the anatomical object, in a second modality is generated based on the respective medical image. One or more augmented images are generated based on the synthetic image. One or more segmentations of the anatomical object are performed from the one or more augmented images using a machine learning based reference network. An uncertainty associated with segmenting the anatomical object from the respective medical image is computed based on results of the one or more segmentations. It is determined whether the respective medical image is suitable for training a machine learning based segmentation network based on the uncertainty. The machine learning based segmentation network is trained based on 1) the suitable medical images of the set of medical images and 2) annotations of the anatomical object determined using a machine learning based teacher network.

Cross domain segmentation with uncertainty-guided curriculum learning

Systems and methods for registering a plurality of medical images are provided. A plurality of partial point clouds of anatomical objects is received. Each of the plurality of partial point clouds is extracted from a respective one of a plurality of medical images. A set of missing points is generated for each of the plurality of partial point clouds. A measure of certainty is generated for each point in 1) the plurality of partial point clouds and 2) the sets of missing points. One or more transformations aligning the plurality of partial point clouds is determined based on the measures of certainty using a machine learning based point registration network for registering the plurality of medical images. The one or more transformations are output.

Unsupervised multitask ensemble for spatial transfer of multi-structures via partial point cloud matching

Systems and methods for generating a probabilistic tree of vessels are provided. An input medical image of vessels of a patient is received. Anatomical landmarks are identified in the input medical image. A centerline of the vessels in the input medical image is determined based on the anatomical landmarks. A probabilistic tree of the vessels is generated based on a probability of fit of the anatomical landmarks and the centerline of the vessels. The probabilistic tree of the vessels is output.

Probabilistic tree tracing and large vessel occlusion detection in medical imaging

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Baiersdorf, Germany

Florin-Cristian Ghesu