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Treffer: MedicalDataHandler, a research-oriented graphical user interface for DICOM data management.

Title:
MedicalDataHandler, a research-oriented graphical user interface for DICOM data management.
Authors:
Maniscalco A; Medical Artificial Intelligence and Automation (MAIA) Laboratory, University of Texas Southwestern Medical Center, Dallas, Texas, USA.; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA., Park YK; Medical Artificial Intelligence and Automation (MAIA) Laboratory, University of Texas Southwestern Medical Center, Dallas, Texas, USA.; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA., Godley A; Medical Artificial Intelligence and Automation (MAIA) Laboratory, University of Texas Southwestern Medical Center, Dallas, Texas, USA.; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA., Lin MH; Medical Artificial Intelligence and Automation (MAIA) Laboratory, University of Texas Southwestern Medical Center, Dallas, Texas, USA.; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA., Jiang S; Medical Artificial Intelligence and Automation (MAIA) Laboratory, University of Texas Southwestern Medical Center, Dallas, Texas, USA.; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA., Nguyen D; Medical Artificial Intelligence and Automation (MAIA) Laboratory, University of Texas Southwestern Medical Center, Dallas, Texas, USA.; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Source:
Medical physics [Med Phys] 2026 Jan; Vol. 53 (1), pp. e70240.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: John Wiley and Sons, Inc Country of Publication: United States NLM ID: 0425746 Publication Model: Print Cited Medium: Internet ISSN: 2473-4209 (Electronic) Linking ISSN: 00942405 NLM ISO Abbreviation: Med Phys Subsets: MEDLINE
Imprint Name(s):
Publication: 2017- : Hoboken, NJ : John Wiley and Sons, Inc.
Original Publication: Lancaster, Pa., Published for the American Assn. of Physicists in Medicine by the American Institute of Physics.
MeSH Terms:
User-Computer Interface* , Data Management*/methods , Image Processing, Computer-Assisted*/methods , Computer Graphics*, Humans
References:
Larobina M, Thirty years of the DICOM standard. Tomography. 2023;9(5):1829‐1838. doi:10.3390/tomography9050145.
Mackenzie A, Lewis E, Loveland J, Successes and challenges in extracting information from DICOM image databases for audit and research. Brit J Radiol. 2023;96(1151):20230104. doi:10.1259/bjr.20230104.
Mason D, SU‐E‐T‐33: pydicom: an open source DICOM library. Med Phys. 2011;38(6Part10):3493‐3493. doi:10.1118/1.3611983.
Gao R, Diallo M, Liu H, et al. Automating High Quality RT Planning at Scale. arXiv(preprint). 2025; v5:arXiv:250111803. https://doi.org/10.48550/arXiv.2501.11803.
Everts MH, pynrrd: a pure‐Python module for reading and writing NRRD files into and from numpy arrays. Accessed September 28, 2025 https://github.com/mhe/pynrrd.
Panchal A, Keyes R, SU‐GG‐T‐260: dicompyler: an open source radiation therapy research platform with a plugin architecture. Med Phys. 2010;37(6Part19):3245‐3245. doi:10.1118/1.3468652.
Pieper S, Halle M, Kikinis R., 3D Slicer. In: 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821). Arlington, VA: IEEE; 2004. p. 632‐635. doi:10.1109/ISBI.2004.1398617.
Fedorov A, Beichel R, Kalpathy‐Cramer J, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imag. 2012;30(9):1323‐1341. doi:10.1016/j.mri.2012.05.001.
Pinter C, Lasso A, Wang A, Jaffray D, Fichtinger G, SlicerRT: radiation therapy research toolkit for 3D Slicer. Med Phys. 2012;39(10):6332‐6338. doi:10.1118/1.4754659.
Yan Y, DICOMan‐TX. Accessed March 4, 2025 https://sites.google.com/site/dicomantx/.
Hoffstadt J, Cothren P, Dear PyGui: A fast and powerful Graphical User Interface Toolkit for Python with minimal dependencies. Accessed March 4, 2025 https://github.com/hoffstadt/DearPyGui.
Chamon F, DearPyGui_Extend: Extensions and custom widgets for Dear Py GUI. Accessed March 4, 2025 https://github.com/fabriciochamon/DearPyGui_Extend.
Lowekamp BC, Chen DT, Ibáñez L, Blezek D, The design of SimpleITK. Front Neuroinform. 2013;7:45. doi:10.3389/fninf.2013.00045.
Virtanen P, Gommers R, Oliphant TE, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Met. 2020;17:261‐272. doi:10.1038/s41592‐019‐0686‐2.
Harris CR, Millman KJ, van der Walt SJ, et al. Array programming with NumPy. Nature. 2020;585(7825):357‐362. doi:10.1038/s41586‐020‐2649‐2.
Bradski G, The OpenCV library. Dr Dobb's Journal: Software Tools for the Professional Programmer. 2000;25(11):120‐123.
Owens M, Allen G, SQLite. Apress LP New York; 2010.
Bayer M. SQLAlchemy. In: Brown A, Wilson G, eds. The Architecture of Open Source Applications, Volume II: Structure, Scale, and a Few More Fearless Hacks. Mountain View: aosabook.org; 2012. Available from: http://www.aosabook.org/en/sqlalchemy.html.
Krekel H, et al. pytest [Computer software]. Version 8.4. 2025. Available from: https://github.com/pytest‐dev/pytest.
Cortesi D, Cabral G, et al., PyInstaller [Computer software]. Version 6.x. 2025. Available from: https://www.pyinstaller.org/.
Mayo C, Moran J, Bosch W, et al. Standardizing nomenclatures in radiation oncology: the report of AAPM Task Group 263. Radiat Oncol. 2018;100(4):1057‐1066.
Grant Information:
R01CA237269 NIH grants; R01CA254377 NIH grants; R01CA258987 NIH grants
Contributed Indexing:
Keywords: DICOM; Digital Imaging and Communications in Medicine; GUI; Python; data analysis; data management; data processing; graphical user interface; medical data; research tool
Entry Date(s):
Date Created: 20251231 Date Completed: 20251231 Latest Revision: 20260108
Update Code:
20260108
DOI:
10.1002/mp.70240
PMID:
41474038
Database:
MEDLINE

Weitere Informationen

Background: Processing DICOM datasets for research and education is challenging due to the format's complexity and frequent patient-specific workflow exceptions. Proper handling demands substantial technical expertise and meticulous care to ensure data fidelity in downstream applications.
Purpose: We developed MedicalDataHandler to streamline the reading, visualization, and processing of DICOM data. By consolidating essential tasks into a user-friendly environment, it minimizes reliance on advanced coding skills and promotes reproducible data handling without custom scripting.
Methods: Implemented in Python with the third-party Dear PyGui toolkit, MedicalDataHandler organizes DICOM files by patient identifiers and groups each patient's radiation therapy (RT) images, structure sets, plans, and doses based on mutual unique identifiers (UIDs). A comprehensive table of patient data enables metadata inspection, data visualization, and data processing. The GUI supports interactive visualization in axial, coronal, and sagittal views, with intuitive scrolling, zooming, panning, and window width/level adjustments. Segmentation labels, colors, and data orientation can be modified on the fly, and hovering over a voxel reveals its image/dose values and relevant segmented structures. Multithreading and multiprocessing enable rapid data reading and conversion to the deep-learning-friendly Nearly Raw Raster Data (NRRD) format. Additional features include metadata inspection, voxel grid resampling, Hounsfield-Unit-to-Relative-Electron-Density mapping, plan-sum dose creation, and partial or bulk data saving options.
Results: We validated MedicalDataHandler with an end-to-end testing approach. DICOM data from 61 radiotherapy patients were processed, and the resulting dataset was used to train a deep-learning-based dose prediction model. MedicalDataHandler streamlined the workflow by eliminating the need for complex, patient-specific code and accelerating the preparation of a research-ready dataset.
Conclusion: MedicalDataHandler streamlines DICOM data management and accelerates preprocessing, serving as a valuable tool for researchers and trainees. Its intuitive interface, flexible editing, and rapid data conversion empower a broader audience to manage DICOM data efficiently and consistently in research and education settings.
(© 2025 American Association of Physicists in Medicine.)