• Medical Imaging De-Identification Initiative (MIDI) - 2 presentations

• A DICOM dataset for evaluation of medical image de-identification
  • Dr. Fred Prior (UAMS)

  • A growing number of tools and procedures claim to properly de-identify image data. Based on our decade of experience managing the Cancer Imaging Archive (TCIA) on behalf of NCI, we developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM objects were selected from datasets published in TCIA.  Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM Attributes to mimic typical clinical imaging exams. The DICOM Standard and TCIA curation audit logs guided the insertion of synthetic PHI into standard and non-standard DICOM data elements. An answer key was created based on our knowledge of the placement of synthetic data and the DICOM standard’s guidelines for what actions should be taken in regard to the synthetic PHI.  A TCIA curation team tested the utility of the evaluation dataset and answer key.

• Medical Image De-Identification using Cloud Services
  • Dr. Benjamin Kopchick

  • Patient privacy rules require the removal of Protected Health Information (PHI) before sharing images publicly. Manual de-identification is no longer scalable due to the rapid increase in imaging data volume. Our goal was to configure and test the efficacy of a cloud service for automated medical image de-identification (MIDI).  One such service for DICOM images is Google Cloud Platform’s Healthcare API. Training and test datasets for validation of image de-identification, specifically prepared by the placement of synthetic PHI in DICOM headers and image pixel data, were obtained from The Cancer Imaging Archive. The customized MIDI pipeline correctly performed 99.8% of expected actions on DICOM header data elements. For image pixel data, one false-positive case was noted, while all sensitive information was correctly removed from image pixel data. Throughput averaged at 58.4 images per second. This implementation of the MIDI pipeline holds promise for automated de-identification at scale. However, verification by a human expert is still currently recommended.

Establishing next generation dynamic susceptibility contrast MRI based biomarkers for neuro-oncologic applications

Dr. Chad Quarles, PhD

Dynamic susceptibility contrast (DSC) MRI is one of the most widely used physiologic imaging techniques in neuro-oncology, enabling the differentiation of glioma grades, identification of tumor components in non-enhancing glioma, reliable detection of recurrence, and early detection of therapy response. This presentation will highlight how an improved understanding of the biophysics of the contrast mechanisms underlying DSC-MRI enabled the recent standardization of acquisition protocols for multi-site clinical trials, is leading to the field’s first benchmark for software validation, and is informing the development of advanced pulse sequences and analysis strategies tailored to specific clinical challenges faced in the management of brain cancer patients. 

MP4 file

Transcript

• Can you sue an algorithm?
  • Dr. Saurabh Jha (University of Pennsylvania, Perelman School of Medicine)

MP4 file

Transcript

• Precision Surgery: Intraoperative molecular imaging to improve margin detection

  Dr. Eben Rosenthal, Professor of Otolaryngology, Head & Neck Surgery and Radiology, Stanford University
  

Abstract:

Cancer is nearly always a surgically treated disease. Almost 80% of patients with early stage solid tumors undergo surgery at some point within their treatment course. A major gap in quality of care remains the high rate of tumor-positive margins in head and neck cancer (HNC) following surgical resections. Positive margin rates are directly correlated with lower survival but have remained unchanged at 25% for the last two decades! Primary factors that have impeded improving the rate of tumor-positive margins include subjective surgeon assessment as well as the limited amount of the tissue that can be sampled for intraoperative frozen-section analysis. We have demonstrated that use of intraoperative molecular imaging (IMI) can objectively identify the area on the tumor specimen most likely to contain a tumor-positive margin (“sentinel margin”). In a prospective evaluation, a fluorescently-labeled tumor-specific contrast agent is administered intravenously to the patient several days prior to surgery. After the surgical resection, the specimen is evaluated with IMI, in which near infrared imaging is used to identify the location of the sentinel margin on the surgical specimen. This evaluation is compared to subjective assessments of the deep tumor margin by palpation, considered the standard of care. It is expected that IMI imaging will be more accurate in identifying the sentinel margin, and will shorten the time to histological diagnosis while maintaining tissue orientation and high histological image quality. The translation of these new technologies has the potential to double the five-year survival rate of patients with HNC as well offer the potential to improve care for other cancer types as well. 

MP4 file

Transcript

Orchestration of distributed image archives
(Jonas Almeida and Praphulla Bhawsar)

• Summary: Recent advances in the use of HTTP range requests to traverse bioformats*, coupled with a general move to “zero footprint” image informatics solutions, enable the creation of image archives as an exercise in governance. A particular feature of this configuration is that the images do not have to be copied or moved from their primary location. This has two interesting effects: a) the image owner remains in control of its governance, and b) training of AI classifiers can be federated across image sets that are not even shared.

  * Bremer E, Saltz J, Almeida JS. ImageBox 2 – Efficient and rapid access of image tiles from whole-slide images using serverless HTTP range requests. J Pathol Inform 2020;11:29

MP4 file

Transcript

Slides

The NIAID Office of Cyber Infrastructure and Computational Biology will share their TB Portals Program, including their efforts at imaging data collection, data dissemination, tool development, and data science research. https://tbportals.niaid.nih.gov

• Program introduction – Alex Rosenthal
• Demo #1 – Alyssa Long
• Demo #2 – Andrei Gabrielian
• Imaging data science research – Ziv Yaniv & Gabriel Rosenfeld

MP4 file

Transcript

• Imaging Data Commons (Andrey Fedorov, Dennis Bontempi)

MP4 file

Transcript

Slides

• MONAI (Stephen Aylward, Prerna Dogra, Jorge Cardoso)

  • Introduction to MONAI and the MONAI Community - Stephen Aylward

  • MONAI medical deep learning capabilities and roadmap - Jorge Cardoso 

  • MONAI and clinical workflows: Clara and federated learning - Brad Genereaux

• TCIA Update (Justin Kirby)

MP4 file

Transcript

• Kheops (Joël Spaltenstein, Osman Ratib)
• TCIA Update (Justin Kirby)

MP4 file

Transcript

• Computational Imaging for Precision Medicine: A quest for generalizable AI models  (Satish Viswanath)

• TCIA Update (John Freymann)

MP4 file

Transcript

• Distributed Learning of Deep Learning in Medical Imaging (Daniel Rubin)
• MedICI website (Benjamin Bearce)
• TCIA update (John Freymann)

• ACR's AI-LAB (Laura Coombs, Chris Treml)
• TCIA update (Justin Kirby)

• PathPresenter - a web-based digital pathology and image viewer (Rajendra Singh, Matthew Hanna)
• TCIA update (Justin Kirby)

• Medical Segmentation Decathlon: Generalizable 3D Semantic Segmentation (Amber Simpson)
• Imaging Data Commons Update (Todd Pihl)
• TCIA Update (Justin Kirby)

• Data Commons Overview (Todd Pihl)
• The Imaging Data Commons (Andrey Fedorov)
• TCIA Update (Justin Kirby)
• NBIA Update (Scott Gustafson)

• HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides (Andrew Janowczyk)
• RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning (Kenneth Philbrick)
• TCIA Update (Justin Kirby)

• Advanced Methods in Tissue Cytometry (Rupert Ecker)
• Presentation by the 4D Necleome Imaging Working Group (David Grünwald)
• TCIA Update (Justin Kirby)

Joint Session with the CPTAC Special Interest Group

• CPTAC Project Overview (Chris Kinsinger)
• CPTAC Image Data at TCIA (Justin Kirby)
• CPTAC Proteomics Data at the Proteomics Data Commons (R. Rajesh Thangudu)
• CPTAC Genomic Data at the Genomics Data Commons (Ana Robles)
• Using the CPTAC Data Portal (R. Rajesh Thangudu)