This invention discloses an
automatic segmentation and scoring method and
system for FDG PET-CT lesions in
lymphoma, belonging to the field of medical
image analysis technology. It aims to improve the segmentation accuracy of
lymphoma lesions and the objectivity of the Deauville
score. First, standardized uptake values ​​are calculated for FDG PET images, and
lymph node morphological features are extracted from CT images based on multi-scale Hessian enhancement filtering to construct a dual-modality PET-CT
image pair. Then, a dual-channel depth network is used to extract anatomical structural features and metabolic distribution features respectively. Cross-
modal gating fusion is used to suppress physiological uptake interference, outputting preliminary
lesion segmentation results. Next, three-dimensional
connected component analysis is performed on the segmentation
mask, and metabolic heterogeneity index is extracted by combining kurtosis and
Haar wavelet multi-scale energy. A graph
attention network is used to identify key lesions. Finally, the ratio of key lesions to standardized liver uptake values ​​is combined with the metabolic heterogeneity index to correct the Deauville
score, achieving
automation from
lesion detection to
treatment efficacy evaluation.