Ge Precision Healthcare LLC

Hongxiang Yi

Tao Tan

Balázs Péter Cziria

Gopal Biligeri Avinash

Rakesh Mullick

Florian Wiesinger

Lehel Ferenczi

László Ruskó

Lehel Mihály Ferenczi

Richárd Zsámboki

Borbála Deák-Karancsi

Vanda Czipczer

Bernadett Kolozsvári

Fei Mian

Attila Márk Rádics

Methods and systems are provided for segmenting structures in medical images. In one embodiment, a method includes receiving an input dataset including a set of medical images, a structure list specifying a set of structures to be segmented, and a segmentation protocol, performing an input check on the input dataset, determining whether each medical image of the set of medical images has passed the input check and removing any medical images from the set of medical images that do not pass the input check to form a final set of medical images, segmenting each structure from the structure list using one or more segmentation models and the final set of medical images, receiving a set of segmentations output from the one or more segmentation models, processing the set of segmentations to generate a final set of segmentations, and displaying and/or saving in memory the final set of segmentations.

Systems and methods for segmenting objects in medical images

Techniques are described that facilitate dynamic multimodal segmentation selection and fusion in medical imaging. In one example embodiment, a computer processing system receives a segmentation dataset comprising a combination of different image segmentations of an anatomical object of interest respectively segmented via different segmentation models from different medical images captured of the (same) anatomical object, wherein the different medical images and the different image segmentations vary with respect to at least one of, capture modality, acquisition protocol, or acquisition parameters. The system employs a dynamic ranking protocol as opposed to a static ranking protocol to determine ranking scores for the different image segmentations that control relative contributions of the different image segmentations in association with combining the different image segmentations into a fused segmentation for the anatomical object. The system further combines the different image segmentations based on the ranking scores to generate the fused image segmentation.

Dynamic multimodal segmentation selection and fusion

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Excelsior, MN, United States of America

Hongxiang Yi