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Integrating the BIDS Neuroimaging Data Format and Workflow Optimization for Large-Scale Medical Image Analysis.

التفاصيل البيبلوغرافية
العنوان: Integrating the BIDS Neuroimaging Data Format and Workflow Optimization for Large-Scale Medical Image Analysis.
المؤلفون: Bao S; Computer Science, Vanderbilt University, Nashville, TN, USA. shunxing.bao@vanderbilt.edu.; Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA. shunxing.bao@vanderbilt.edu., Boyd BD; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA., Kanakaraj P; Computer Science, Vanderbilt University, Nashville, TN, USA., Ramadass K; Computer Science, Vanderbilt University, Nashville, TN, USA., Meyer FAC; Department of Psychology, Vanderbilt University, Nashville, TN, USA., Liu Y; Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA., Duett WE; Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA., Huo Y; Computer Science, Vanderbilt University, Nashville, TN, USA.; Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.; Data Science Institute, Vanderbilt University, Nashville, TN, USA., Lyu I; Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea., Zald DH; Department of Psychology, Vanderbilt University, Nashville, TN, USA.; Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA., Smith SA; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.; Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.; Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA., Rogers BP; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.; Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.; Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA., Landman BA; Computer Science, Vanderbilt University, Nashville, TN, USA.; Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.; Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.; Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.; Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
المصدر: Journal of digital imaging [J Digit Imaging] 2022 Dec; Vol. 35 (6), pp. 1576-1589. Date of Electronic Publication: 2022 Aug 03.
نوع المنشور: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.
اللغة: English
بيانات الدورية: Publisher: Springer Country of Publication: United States NLM ID: 9100529 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1618-727X (Electronic) Linking ISSN: 08971889 NLM ISO Abbreviation: J Digit Imaging Subsets: MEDLINE
أسماء مطبوعة: Publication: <2008-2023>: New York : Springer
Original Publication: Philadelphia, PA : W.B. Saunders, c1988-
مواضيع طبية MeSH: Neuroimaging*/methods , Software*, Humans ; Brain ; Workflow
مستخلص: A robust medical image computing infrastructure must host massive multimodal archives, perform extensive analysis pipelines, and execute scalable job management. An emerging data format standard, the Brain Imaging Data Structure (BIDS), introduces complexities for interfacing with XNAT archives. Moreover, workflow integration is combinatorically problematic when matching large amount of processing to large datasets. Historically, workflow engines have been focused on refining workflows themselves instead of actual job generation. However, such an approach is incompatible with data centric architecture that hosts heterogeneous medical image computing. Distributed automation for XNAT toolkit (DAX) provides large-scale image storage and analysis pipelines with an optimized job management tool. Herein, we describe developments for DAX that allows for integration of XNAT and BIDS standards. We also improve DAX's efficiencies of diverse containerized workflows in a high-performance computing (HPC) environment. Briefly, we integrate YAML configuration processor scripts to abstract workflow data inputs, data outputs, commands, and job attributes. Finally, we propose an online database-driven mechanism for DAX to efficiently identify the most recent updated sessions, thereby improving job building efficiency on large projects. We refer the proposed overall DAX development in this work as DAX-1 (DAX version 1). To validate the effectiveness of the new features, we verified (1) the efficiency of converting XNAT data to BIDS format and the correctness of the conversion using a collection of BIDS standard containerized neuroimaging workflows, (2) how YAML-based processor simplified configuration setup via a sequence of application pipelines, and (3) the productivity of DAX-1 on generating actual HPC processing jobs compared with earlier DAX baseline method. The empirical results show that (1) DAX-1 converting XNAT data to BIDS has similar speed as accessing XNAT data only; (2) YAML can integrate to the DAX-1 with shallow learning curve for users, and (3) DAX-1 reduced the job/assessor generation latency by finding recent modified sessions. Herein, we present approaches for efficiently integrating XNAT and modern image formats with a scalable workflow engine for the large-scale dataset access and processing.
(© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)
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معلومات مُعتمدة: UL1 TR000445 United States TR NCATS NIH HHS; Grant T32-EB021937 United States EB NIBIB NIH HHS; R01 EB017230 United States EB NIBIB NIH HHS; T32 EB021937 United States EB NIBIB NIH HHS; S10 OD020154 United States OD NIH HHS; Grant UL1 RR024975-01 United States RR NCRR NIH HHS; P50 HD103537 United States HD NICHD NIH HHS; UL1 RR024975 United States RR NCRR NIH HHS; Grant 2 UL1 TR000445-06 United States TR NCATS NIH HHS
فهرسة مساهمة: Keywords: BIDS format; Large-scale processing; Workflow engine
تواريخ الأحداث: Date Created: 20220803 Date Completed: 20221230 Latest Revision: 20231202
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC9712842
DOI: 10.1007/s10278-022-00679-8
PMID: 35922700
قاعدة البيانات: MEDLINE
الوصف
تدمد:1618-727X
DOI:10.1007/s10278-022-00679-8