Using radiation dose information for automatic organ segmentation model training

Disclosed herein are systems and methods for training a machine learning model for automatic organ segmentation. A processor receives an image of one or more pre-contoured organs, the image comprising a plurality of voxels. The processor executes a machine learning model using the image to output pr...

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Bibliographische Detailangaben
Hauptverfasser:	Laaksonen, Hannu, Kuusela, Esa
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA IMAGE DATA PROCESSING OR GENERATION, IN GENERAL INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS PHYSICS
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Beschreibung
Zusammenfassung:	Disclosed herein are systems and methods for training a machine learning model for automatic organ segmentation. A processor receives an image of one or more pre-contoured organs, the image comprising a plurality of voxels. The processor executes a machine learning model using the image to output predicted organ labels for the plurality of voxels of the image. The processor determines differences between corresponding predicted organ labels and expected organ labels for the plurality of voxels. The processor determines radiation dose levels that correspond to the plurality of voxels of the image. The processor determines weights for the plurality of voxels based on the radiation dose levels of the respective voxels. The processor then trains the machine learning model based on the differences and the weights for the plurality of voxels.