MACHINE-LEARNING BASED SEGMENTATION OF BIOLOGICAL OBJECTS IN MEDICAL IMAGES

In one embodiment, a method includes accessing a first scan image from a set of computed tomography (CT) scan images with each CT scan image being at a first resolution, generating a first downscaled image of the first scan image by resampling the first scan image to a second resolution that is lowe...

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Bibliographische Detailangaben
Hauptverfasser: JEMAA, Mohamed Skander, BENGTSSON, Nils Gustav Thomas, WANG, Xiaoyong, CARANO, Richard Alan Duray, PETROV, Yury Anatolievich
Format: Patent
Sprache:eng
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Zusammenfassung:In one embodiment, a method includes accessing a first scan image from a set of computed tomography (CT) scan images with each CT scan image being at a first resolution, generating a first downscaled image of the first scan image by resampling the first scan image to a second resolution that is lower than the first resolution, determining coarse segmentations corresponding to organs portrayed in the first scan image by first machine-learning models based on the first downscaled image, extracting segments of the first scan image based on the coarse segmentations with each extracted segment being at the first resolution, determining fine segmentations corresponding to the respective organs portrayed in the extracted segments by second machine-learning models based on the extracted segments, and generating a segmented image of the first scan image based on the fine segmentations, wherein the segmented image comprises confirmed segmentations corresponding to the organs.