3D SEGMENTATION OF LESIONS IN CT IMAGES USING SELF-SUPERVISED PRETRAINING WITH AUGMENTATION

3D SEGMENTATION OF LESIONS IN CT IMAGES USING SELF-SUPERVISED PRETRAINING WITH AUGMENTATION

A method or system for training a convolutional neural network (CNN) for medical imaging analysis. The system pre-trains the CNN's encoder using a dataset of unlabeled 3D medical images. Each 3D image includes an annotated slice delineating a boundary of a lesion and multiple non-annotated 2D s...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: Liu, Yiqiao, Goldmacher, Gregory, Chen, Antong
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
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:A method or system for training a convolutional neural network (CNN) for medical imaging analysis. The system pre-trains the CNN's encoder using a dataset of unlabeled 3D medical images. Each 3D image includes an annotated slice delineating a boundary of a lesion and multiple non-annotated 2D slices above and below the annotated slice. The system then fine-tunes the pre-trained encoder using an annotated 2D image dataset. The annotated 2D image dataset includes multiple 2D slices of lesions, each including an annotation that delineates a boundary of a corresponding lesion.