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Working with DICOM at scale?
DICOMAnon helps imaging teams anonymize, batch process, and automate DICOM workflows without writing custom scripts.
Author profile
Justin Brady Hink
Department of Radiation Oncology, University of California, Los Angeles
Automated Dose-Based Anatomic Region Classification of Radiotherapy Treatment for Big Data Applications
Aggregating large radiotherapy datasets for predictive modeling is impeded by inconsistent nomenclature. To address this, we developed a fully automated, metadata-independent framework to classify treatment plans into six Anatomic Regions and identify specifi...
Poster Program · Therapy Physics
Head and Neck Gross Tumor Volume Deep-Learning Autocontouring Model Using Bigroc Collaborative Data
Accurate delineation of Head and Neck (H&N) Gross Tumor Volumes (GTV) is a prerequisite for effective radiotherapy, represents a significant cognitive challenge, and may contribute to an observed outcomes deficit between high-volume and low-volume H&N radioth...
Poster Program · Therapy Physics
Rosamllib: An Open-Source Software Package for Large-Scale Radiotherapy Dicom Ingestion, Indexing, Visualization and Preprocessing
Large-scale radiotherapy research increasingly relies on heterogeneous DICOM datasets containing complex cross-references across imaging and radiotherapy objects. Beyond ingestion, downstream preprocessing for analysis and machine learning requires consistent...
Poster Program · Therapy Physics