Recognizing Focal Liver Lesions in Contrast-Enhanced Ultrasound with Discriminatively Trained Spatio-Temporal Model
This work addresses the challenge of assisting clinicians in diagnosing liver lesions, representing an incremental improvement in medical imaging analysis.
The study tackled the problem of automatically diagnosing Focal Liver Lesions in Contrast-Enhanced Ultrasound videos by developing a discriminative spatio-temporal model that iteratively infers optimal region-of-interest locations, achieving promising results on the largest publicly available dataset in the literature.
The aim of this study is to provide an automatic computational framework to assist clinicians in diagnosing Focal Liver Lesions (FLLs) in Contrast-Enhancement Ultrasound (CEUS). We represent FLLs in a CEUS video clip as an ensemble of Region-of-Interests (ROIs), whose locations are modeled as latent variables in a discriminative model. Different types of FLLs are characterized by both spatial and temporal enhancement patterns of the ROIs. The model is learned by iteratively inferring the optimal ROI locations and optimizing the model parameters. To efficiently search the optimal spatial and temporal locations of the ROIs, we propose a data-driven inference algorithm by combining effective spatial and temporal pruning. The experiments show that our method achieves promising results on the largest dataset in the literature (to the best of our knowledge), which we have made publicly available.