The invention discloses a
thyroid-related
eye disease auxiliary
verification method based on SPECT / CT and federal learning. The
thyroid-related
eye disease auxiliary
verification method comprises the steps that A, image data
standardization and
orbit area extraction are carried out; b, extracting a preliminary segmentation
mask of each rectus in the CT image; c, fusing the structure prior
mask, and obtaining an extraocular
muscle fine segmentation
mask through multi-mode collaborative segmentation; d, utilizing extraocular
muscle fine segmentation masks, SPECT and CT images, fusing segmentation prior and multi-
modal features to perform deep discrimination, performing automatic prediction of
thyroid-related
eye disease activity staging, and outputting a
prediction probability; and E, based on a dynamic model
combination strategy of style similarity and uncertainty quantization, obtaining a dynamic combination model of segmentation and classification adapted to new domain feature distribution, and predicting TAO activity. According to the method,
accurate segmentation of extraocular
muscle structures and accurate prediction of TAO activity in an invisible
data center can be realized, auxiliary
verification data support is provided for diagnosis of thyroid-related eye diseases, and the diagnosis accuracy is improved.