Interactive Segmentation and Report Generation for CT Images
This addresses the need for interpretable and adjustable automated reporting in medical imaging for clinicians, though it appears incremental by combining existing interactive segmentation with structured reports.
The paper tackles the problem of automated CT report generation lacking interpretability and dynamic adjustment capabilities by proposing an interactive framework that generates segmentation masks with attribute descriptions for 3D lesion morphology. Experimental results across 15 lesion types demonstrate its effectiveness in providing a more comprehensive and reliable reporting system.
Automated CT report generation plays a crucial role in improving diagnostic accuracy and clinical workflow efficiency. However, existing methods lack interpretability and impede patient-clinician understanding, while their static nature restricts radiologists from dynamically adjusting assessments during image review. Inspired by interactive segmentation techniques, we propose a novel interactive framework for 3D lesion morphology reporting that seamlessly generates segmentation masks with comprehensive attribute descriptions, enabling clinicians to generate detailed lesion profiles for enhanced diagnostic assessment. To our best knowledge, we are the first to integrate the interactive segmentation and structured reports in 3D CT medical images. Experimental results across 15 lesion types demonstrate the effectiveness of our approach in providing a more comprehensive and reliable reporting system for lesion segmentation and capturing. The source code will be made publicly available following paper acceptance.