Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A Real-World Evaluation of Failure Detection for Liver CT Segmentation.

medRxiv : the preprint server for health sciences·2026

Same author

Automated identification of bolus types in modified barium swallow studies using deep learning: a preliminary study.

medRxiv : the preprint server for health sciences·2026

Same author

Stereotactic Percutaneous Thermal Ablation of Primary and Metastatic Liver Tumors: A Prospective Registry-Based Study.

Journal of vascular and interventional radiology : JVIR·2026

Same author

Standardized reporting as a foundation for trustworthy and reproducible artificial intelligence in radiotherapy.

Physics and imaging in radiation oncology·2026

Same author

Large language models for scalable standardization in radiation oncology.

Physics and imaging in radiation oncology·2026

Same author

Clinicogenomic and Histopathologic Analyses of Supermassive Intrahepatic Cholangiocarcinoma and the Role of Ablative Radiotherapy.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026

Search research articles

Related Experiment Video

Updated: Nov 16, 2025

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates

Published on: July 17, 2020

Simple Python Module for Conversions Between DICOM Images and Radiation Therapy Structures, Masks, and Prediction

Brian M Anderson¹, Kareem A Wahid², Kristy K Brock³

¹Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Practical Radiation Oncology

|February 19, 2021

Summary

A new Python module simplifies medical image analysis for deep learning. It converts medical imaging data (DICOM) and radiation therapy (RT) structures into formats usable by machine learning models, saving researchers time.

More Related Videos

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Related Experiment Videos

Last Updated: Nov 16, 2025

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates

Published on: July 17, 2020

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

Medical Imaging
Machine Learning
Radiotherapy

Background:

Deep learning is increasingly used in medicine, requiring specific data formats.
Medical image segmentation and analysis often utilize DICOM images and RT structures.
Existing tools lack a dedicated Python module for converting NumPy arrays to RT structures.

Purpose of the Study:

To develop a Python module for seamless conversion between medical imaging formats and deep learning inputs.
To streamline data curation and manipulation for medical researchers using deep learning.
To facilitate the creation of RT structures from deep learning model outputs.

Main Methods:

Developed a Python module, DicomRTTool, for manipulating DICOM images and RT structures.
Implemented intuitive methods for ROI identification and data curation.
Enabled conversion between DICOM/RT structures and NumPy arrays/SimpleITK Images.

Main Results:

The module efficiently converts DICOM and RT structures into NumPy arrays and SimpleITK Images.
It allows for the creation of DICOM RT structures from predicted NumPy arrays.
The tool simplifies data preprocessing for deep learning in medical imaging.

Conclusions:

The DicomRTTool provides a valuable resource for medical researchers leveraging deep learning.
It enhances efficiency in data handling for tasks like image segmentation and outcome analysis.
Open collaboration via GitHub and PyPi ensures accessibility and future development.