Author profile
Johns Hopkins University
To reduce reliance on labor-intensive voxel-wise tumor masks by training CT segmentation models directly from routine radiology and pathology reports, enabling scalable detection and localization of tumors relevant to radiotherapy planning and incidental find...
To establish a large-scale, independent benchmark for evaluating auto-contouring AI with an emphasis on radiotherapy-relevant requirements: robustness to domain shift, calibration of confidence, and clinically meaningful failure modes beyond average Dice.
To provide a large, multicenter, longitudinal CT dataset with voxel-wise tumor annotations across multiple cancer sites to support development, benchmarking, and validation of AI models for radiotherapy target and organ-at-risk (OAR) segmentation under real-w...
To provide a large, diverse, and quality-controlled abdominal CT dataset with pancreas- and tumor-centric voxel-wise annotations to support benchmarking and development of AI models for pancreatic target segmentation and anatomy-aware evaluation relevant to r...
To determine whether tumor descriptions in routine radiology reports can be converted into controllable priors for synthetic tumor generation in CT, and whether these report-conditioned synthetic tumors improve robustness of tumor detection and segmentation r...
While PET-CT imaging holds promise for simulation-free radiotherapy workflows, its inherent image resolution limits its use for accurate tumor and organ-at-risk (OAR) contouring. This study aims to enhance the spatial resolution of PET-CT by leveraging a reso...
To test whether commonly used pixel-wise CT reconstruction metrics reflect preservation of clinically relevant anatomy for radiotherapy imaging, and to develop an anatomy-centered, task-based evaluation and enhancement approach for sparse-view reconstruction.
Diagnostic and Interventional Radiology Physics
To test whether a physics-informed “time-machine” tumor synthesis pipeline can generate realistic small pancreatic ductal adenocarcinoma (PDAC) targets on contrast-enhanced CT for training and stress-testing AI models intended to support CT-based target delin...