Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies

Doran, Simon J.; Sa’d, Mohammad Al; Petts, James; d’Arcy, James A.; Alpert, Kate; Cho, Woonchan; Escudero, L.; Alle, Sachidanand; Harouni, Ahmed; Genereaux, Brad; Ziegler, Erik; Harris, Gordon J.; Aboagye, Eric O.; Sala, Evis; Koh, Dow‐Mu; Marcus, Dan

doi:10.3390/tomography8010040

Cited by 16 publications

(12 citation statements)

References 30 publications

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“…-Over 700 Open-Source Projects on Github (plus hundreds of commercial projects) -Widespread use in academia (XNAT, [9] TCIA, MIDRC, etc.) and Industry (AWS HealthImaging, Google HealthCloud, Nvidia, Yunu, Flywheel, FlexView, etc.)…”

Section: Resultsmentioning

confidence: 99%

A tale of two imaging informatics translational licensing models: commercial and open source

Harris

2024

Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications

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While many academic projects develop software, methods, and/or products that may be of broad interest, few are licensed for use outside the institution of origin. Within an academic setting, there are key challenges to building and sustaining healthcare technologies and translating them into widely available tools with a national or global community and user base. Hurdles include identifying broad and significant gaps and needs, acquiring funding for developers, project management, user support, implementing commercial grade development processes and user experience design, and choosing a sustainable financial model and licensing plan. In addition, moving beyond the academic sphere into the commercial realm requires an investment in business processes and skills, including the need for branding, marketing, sales, business development, operating, infrastructure, regulatory/compliance, legal, and fundraising expertise. This report will share experiences and insights based on imaging informatics platform licensing, illustrated with the following examples: First, a clinical trials imaging informatics platform will be discussed, developed initially to manage all the clinical trials imaging assessments within a premier Comprehensive Cancer Center. It was then licensed, initially through multi-center academic licensing, and now licensed commercially for use in over 4,200 active clinical trials at 22 organizations, including 12 NCI-designated cancer centers. Second, a web-based medical imaging framework and its underlying libraries will be covered, an open-source software platform that has become the standard for over 1,000 academic and industry software projects. Providing a road map for translational licensing from academia may help guide other projects to enable use beyond the institution of origin.

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Section: Resultsmentioning

confidence: 99%

A tale of two imaging informatics translational licensing models: commercial and open source

Harris

2024

Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications

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“…Manual segmentations were performed by a Radiologist in training (CM: 3 years’ experience; VB: 6 years’ experience) using Microsoft Advanced Medical Image Labeler (version 1.0.0.0, project InnerEye, Microsoft, Redmond, WA, USA) or the Open Health Imaging Foundation viewer (Open Health Imaging Foundation, Massachusetts Institute of Technology, Cambridge, MA, USA, version 3.2.0) via its plugin to XNAT, hosted at the local node of the repository established by the CRUK National Cancer Imaging Translational Accelerator (NCITA, ) ( 20 ). Challenging segmentations were verified by a board certified Gynaecological Radiologist (RW: 8 years’ experience as Consultant Radiologist).…”

Section: Methodsmentioning

confidence: 99%

Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study

Delgado-Ortet

Reinius²,

McCague³

et al. 2023

Front. Oncol.

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BackgroundHigh-Grade Serous Ovarian Carcinoma (HGSOC) is the most prevalent and lethal subtype of ovarian cancer, but has a paucity of clinically-actionable biomarkers due to high degrees of multi-level heterogeneity. Radiogenomics markers have the potential to improve prediction of patient outcome and treatment response, but require accurate multimodal spatial registration between radiological imaging and histopathological tissue samples. Previously published co-registration work has not taken into account the anatomical, biological and clinical diversity of ovarian tumours.MethodsIn this work, we developed a research pathway and an automated computational pipeline to produce lesion-specific three-dimensional (3D) printed moulds based on preoperative cross-sectional CT or MRI of pelvic lesions. Moulds were designed to allow tumour slicing in the anatomical axial plane to facilitate detailed spatial correlation of imaging and tissue-derived data. Code and design adaptations were made following each pilot case through an iterative refinement process.ResultsFive patients with confirmed or suspected HGSOC who underwent debulking surgery between April and December 2021 were included in this prospective study. Tumour moulds were designed and 3D-printed for seven pelvic lesions, covering a range of tumour volumes (7 to 133 cm3) and compositions (cystic and solid proportions). The pilot cases informed innovations to improve specimen and subsequent slice orientation, through the use of 3D-printed tumour replicas and incorporation of a slice orientation slit in the mould design, respectively. The overall research pathway was compatible with implementation within the clinically determined timeframe and treatment pathway for each case, involving multidisciplinary clinical professionals from Radiology, Surgery, Oncology and Histopathology Departments.ConclusionsWe developed and refined a computational pipeline that can model lesion-specific 3D-printed moulds from preoperative imaging for a variety of pelvic tumours. This framework can be used to guide comprehensive multi-sampling of tumour resection specimens.

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“…Additionally, through its integrated Open Health Imaging Foundation (OHIF) viewer, 12 XNAT provides the user with a powerful set of image visualization and annotation tools, including the ability to natively refine tumor annotations, perform measurements, and save those contours and segmentation objects back into XNAT (Fig. 3B).…”

Section: Methodsmentioning

confidence: 99%

Deep learning-based end-to-end scan-type classification, pre-processing, and segmentation of clinical neuro-oncology studies

Chakrabarty¹,

Abidi

Mousa

et al. 2023

Medical Imaging 2023: Imaging Informatics for Healthcare, Research, and Applications

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Modern neuro-oncology workflows are driven by large collections of high-dimensional MRI data obtained using varying acquisition protocols. The concomitant heterogeneity of this data makes extensive manual curation and pre-processing imperative prior to algorithmic use. The limited efforts invested towards automating this curation and processing are fragmented, do not encompass the entire workflow, or still require significant manual intervention. In this work, we propose an artificial intelligence-driven solution for transforming multi-modal raw neuro-oncology MRI Digital Imaging and Communications in Medicine (DICOM) data into quantitative tumor measurements. Our end-to-end framework classifies MRI scans into different structural sequence types, preprocesses the data, and uses convolutional neural networks to segment tumor tissue subtypes. Moreover, it adopts an expert-in-the-loop approach, where segmentation results may be manually refined by radiologists. This framework was implemented as Docker Containers (for command line usage and within the eXtensible Neuroimaging Archive Toolkit [XNAT]) and validated on a retrospective glioma dataset (n = 155) collected from the Washington University School of Medicine, comprising preoperative MRI scans from patients with histopathologically confirmed gliomas. Segmentation results were refined by a neuroradiologist, and performance was quantified using Dice Similarity Coefficient to compare predicted and expert-refined tumor masks. The scan-type classifier yielded a 99.71% accuracy across all sequence types. The segmentation model achieved mean Dice scores of 0.894 (± 0.225) for whole tumor segmentation. The proposed framework can automate tumor segmentation and characterization -thus streamlining workflows in a clinical setting as well as expediting standardized curation of large-scale neuro-oncology datasets in a research setting.

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Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies

Cited by 16 publications

References 30 publications

A tale of two imaging informatics translational licensing models: commercial and open source

A tale of two imaging informatics translational licensing models: commercial and open source

Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study

Deep learning-based end-to-end scan-type classification, pre-processing, and segmentation of clinical neuro-oncology studies

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