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DeepInfer: Open-Source Deep Learning Deployment Toolkit for Image-Guided Therapy

Institution:
1Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
2Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA.
3Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada.
Publication Date:
Feb-2017
Pages:
101351K
Citation:
Proc SPIE Int Soc Opt Eng. 2017 Feb 11; 10135: 101351K.
PubMed ID:
28615793
PMCID:
PMC5467894
Appears in Collections:
NCIGT, SLICER, SPL
Sponsors:
P41 EB015898/EB/NIBIB NIH HHS/United States
Generated Citation:
Mehrtash A., Pesteie M., Hetherington J., Behringer P.A., Kapur T., Wells III W.M., Rohling R., Fedorov A., Abolmaesumi P. DeepInfer: Open-Source Deep Learning Deployment Toolkit for Image-Guided Therapy. Proc SPIE Int Soc Opt Eng. 2017 Feb 11; 10135: 101351K. PMID: 28615793. PMCID: PMC5467894.
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Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision mod- els. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose “DeepInfer“ – an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

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Mehrtash-SPIEIntSocOptEng2017-fig2.jpg (77.388kB)