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DICOMAnon helps imaging teams anonymize, batch process, and automate DICOM workflows without writing custom scripts.
Author profile
Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China
DICOMAnon helps imaging teams anonymize, batch process, and automate DICOM workflows without writing custom scripts.
To prospectively validate a prior and constraint-informed deep learning auto-segmentation framework for nasopharyngeal carcinoma (NPC) across three clinical centers, addressing accuracy, efficiency, and anatomical plausibility under real-world time constraint...
This study proposes an automated quality assurance (QA) method for radiation therapy structure delineation based on the RT contour QA software, addressing issues such as low efficiency in delineating clinical radiation therapy regions of interest (ROIs), sign...
To explore the feasibility of applying large language models (LLMs) for radiotherapy plan(RTP) evaluation to assist radiotherapy plan quality control and clinical decision making.
To investigate the inter- and intra-fractional morphological and dosimetric variations in precision radiotherapy for bladder cancer using Fan Beam CT (FBCT) and Electronic Portal Imaging Device (EPID), providing evidence for motion management.
To analyze the morphological and dosimetric variations in bladder patients during radiotherapy, and establish a dose-anatomy adaptive radiotherapy(ART) triggering model, providing a basis for individualized ART in clinical practice.
To evaluate a deep-learning based real-time automated radiotherapy planning pipeline for nasopharyngeal carcinoma (NPC) in both retrospective multi-center study and prospective clinical deployment.