Rapid development of image analysis research tools: Bridging the gap between researcher and clinician with pyOsiriX.
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| Title: | Rapid development of image analysis research tools: Bridging the gap between researcher and clinician with pyOsiriX. |
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| Authors: | Blackledge MD; CR-UK Cancer Imaging Centre, Radiotherapy and Imaging Division, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom. Electronic address: matthew.blackledge@icr.ac.uk., Collins DJ; CR-UK Cancer Imaging Centre, Radiotherapy and Imaging Division, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom., Koh DM; CR-UK Cancer Imaging Centre, Radiotherapy and Imaging Division, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom., Leach MO; CR-UK Cancer Imaging Centre, Radiotherapy and Imaging Division, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom. |
| Source: | Computers in biology and medicine [Comput Biol Med] 2016 Feb 01; Vol. 69, pp. 203-12. Date of Electronic Publication: 2015 Dec 18. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE |
| Imprint Name(s): | Publication: New York : Elsevier Original Publication: New York, Pergamon Press. |
| MeSH Terms: | Image Processing, Computer-Assisted* , Programming Languages*, Absorptiometry, Photon/*methods , Tomography, X-Ray Computed/*methods, Humans |
| Abstract: | We present pyOsiriX, a plugin built for the already popular dicom viewer OsiriX that provides users the ability to extend the functionality of OsiriX through simple Python scripts. This approach allows users to integrate the many cutting-edge scientific/image-processing libraries created for Python into a powerful DICOM visualisation package that is intuitive to use and already familiar to many clinical researchers. Using pyOsiriX we hope to bridge the apparent gap between basic imaging scientists and clinical practice in a research setting and thus accelerate the development of advanced clinical image processing. We provide arguments for the use of Python as a robust scripting language for incorporation into larger software solutions, outline the structure of pyOsiriX and how it may be used to extend the functionality of OsiriX, and we provide three case studies that exemplify its utility. For our first case study we use pyOsiriX to provide a tool for smooth histogram display of voxel values within a user-defined region of interest (ROI) in OsiriX. We used a kernel density estimation (KDE) method available in Python using the scikit-learn library, where the total number of lines of Python code required to generate this tool was 22. Our second example presents a scheme for segmentation of the skeleton from CT datasets. We have demonstrated that good segmentation can be achieved for two example CT studies by using a combination of Python libraries including scikit-learn, scikit-image, SimpleITK and matplotlib. Furthermore, this segmentation method was incorporated into an automatic analysis of quantitative PET-CT in a patient with bone metastases from primary prostate cancer. This enabled repeatable statistical evaluation of PET uptake values for each lesion, before and after treatment, providing estaimes maximum and median standardised uptake values (SUVmax and SUVmed respectively). Following treatment we observed a reduction in lesion volume, SUVmax and SUVmed for all lesions, in agreement with a reduction in concurrent measures of serum prostate-specific antigen (PSA). (Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.) |
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| Grant Information: | 16464 United Kingdom CRUK_ Cancer Research UK; PDF-2012-05-441 United Kingdom DH_ Department of Health |
| Contributed Indexing: | Keywords: Computed tomography; Dicom management; Dicom visualisation; Medical imaging; OsiriX; Python; Radiology |
| Entry Date(s): | Date Created: 20160117 Date Completed: 20161027 Latest Revision: 20250529 |
| Update Code: | 20250530 |
| PubMed Central ID: | PMC4761020 |
| DOI: | 10.1016/j.compbiomed.2015.12.002 |
| PMID: | 26773941 |
| Database: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | We present pyOsiriX, a plugin built for the already popular dicom viewer OsiriX that provides users the ability to extend the functionality of OsiriX through simple Python scripts. This approach allows users to integrate the many cutting-edge scientific/image-processing libraries created for Python into a powerful DICOM visualisation package that is intuitive to use and already familiar to many clinical researchers. Using pyOsiriX we hope to bridge the apparent gap between basic imaging scientists and clinical practice in a research setting and thus accelerate the development of advanced clinical image processing. We provide arguments for the use of Python as a robust scripting language for incorporation into larger software solutions, outline the structure of pyOsiriX and how it may be used to extend the functionality of OsiriX, and we provide three case studies that exemplify its utility. For our first case study we use pyOsiriX to provide a tool for smooth histogram display of voxel values within a user-defined region of interest (ROI) in OsiriX. We used a kernel density estimation (KDE) method available in Python using the scikit-learn library, where the total number of lines of Python code required to generate this tool was 22. Our second example presents a scheme for segmentation of the skeleton from CT datasets. We have demonstrated that good segmentation can be achieved for two example CT studies by using a combination of Python libraries including scikit-learn, scikit-image, SimpleITK and matplotlib. Furthermore, this segmentation method was incorporated into an automatic analysis of quantitative PET-CT in a patient with bone metastases from primary prostate cancer. This enabled repeatable statistical evaluation of PET uptake values for each lesion, before and after treatment, providing estaimes maximum and median standardised uptake values (SUVmax and SUVmed respectively). Following treatment we observed a reduction in lesion volume, SUVmax and SUVmed for all lesions, in agreement with a reduction in concurrent measures of serum prostate-specific antigen (PSA).<br /> (Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.) |
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| ISSN: | 1879-0534 |
| DOI: | 10.1016/j.compbiomed.2015.12.002 |