“A Deep Learning Framework for Efficient Registration of MRI and Histopathology Images of the Prostate”
Wei Shao, PhD
Postdoctoral Research Fellow
Department of Radiology
Stanford University
“Applications of Generative Adversarial Networks (GANs) in Medical Imaging”
Saeed Seyyedi, PhD
Paustenbach Research Fellow
Department of Radiology
Stanford University
Join via Zoom: https://stanford.zoom.us/j/593016899
Refreshments will be provided
ABSTRACT (Shao)
Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, MRI interpretation suffers from high interobserver variability and often misses clinically significant cancers. Registration of histopathology images from patients who have undergone surgical resection of the prostate onto pre-operative MRI images allows direct mapping of cancer location onto MR images. This is essential for the discovery and validation of novel prostate cancer signatures on MRI. Traditional registration approaches can be computationally expensive and require a careful choice of registration hyperparameters. We present a deep learning-based pipeline to accelerate and simplify MRI-histopathology image registration in prostate cancer. Our pipeline consists of preprocessing, transform estimation by deep neural networks, and postprocessing. We refined the registration neural networks, originally trained with 19,642 natural images, by adding 17,821 medical images of the prostate to the training set. The pipeline was evaluated using 99 prostate cancer patients. The addition of the images to the training set significantly (p < 0.001) improved the Dice coefficient and reduced the Hausdorff distance. Our pipeline also achieved comparable accuracy to an existing state-of-the-art algorithm while reducing the computation time from 4.4 minutes to less than 2 seconds.
ABSTRACT (Seyyedi)
Generative adversarial networks (GANs) are advanced types of neural networks where two networks are trained simultaneously to perform two tasks of generation and discrimination. GANs have gained a lot of attention to tackle well known and challenging problems in computer vision applications including medical image analysis tasks such as medical image de-noising, detection and classification, segmentation and reconstruction.In this talk, we will introduce some of the recent advancements of GANs in medical imaging applications and will discuss the recent developments of GAN models to resolve real world imaging challenges.
Ron Kikinis, MD
Director of the Surgical Planning Laboratory
Professor of Radiology
Department of Radiology
Brigham and Women’s Hospital
Harvard Medical School
Title: Evolving Health Care from an Artisanal Organization into an Industrial Enterprise
Refreshments will be provided
Join via Zoom: https://stanford.zoom.us/j/996417088
Abstract: During the last decade, results from basic research in the fields of genetics and immunology have begun to impact treatment in a variety of diseases. Checkpoint therapy, for instance has fundamentally changed the treatment and survival of some patients with melanoma. The medical workplace has transformed from an artisanal organization into an industrial enterprise environment. Workflows in the clinic are increasingly standardized. Their timing and execution are monitored through omnipresent software systems. This has resulted in an acceleration of the pace of care delivery. Imaging and image post-processing have rapidly evolved as well, enabled by ever-increasing computational power, novel sensor systems and novel mathematical approaches. Organizing the data and making it findable and accessible is an ongoing challenge and is investigated through a variety of research efforts. These topics will be reviewed and discussed during the lecture.
About:
Dr. Kikinis is the founding Director of the Surgical Planning Laboratory, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, and a Professor of Radiology at Harvard Medical School. This laboratory was founded in 1990. Before joining Brigham & Women’s Hospital in 1988, he trained as a resident in radiology at the University Hospital in Zurich, and as a researcher in computer vision at the ETH in Zurich, Switzerland. He received his M.D. degree from the University of Zurich, Switzerland, in 1982. In 2004 he was appointed Professor of Radiology at Harvard Medical School. In 2009 he was the inaugural recipient of the MICCAI Society “Enduring Impact Award”. On February 24, 2010 he was appointed the Robert Greenes Distinguished Director of Biomedical Informatics in the Department of Radiology at Brigham and Women’s Hospital. On January 1, 2014, he was appointed “Institutsleiter” of Fraunhofer MEVIS and Professor of Medical Image Computing at the University of Bremen. Since then he is commuting every two months between Bremen and Boston.
During the mid-80’s, Dr. Kikinis developed a scientific interest in image processing algorithms and their use for extracting relevant information from medical imaging data. Due to the explosive increase of both the quantity and complexity of imaging data this area of research is of ever-increasing importance. Dr. Kikinis has led and has participated in research in different areas of science. His activities include technological research (segmentation, registration, visualization, high performance computing), software system development, and biomedical research in a variety of biomedical specialties. The majority of his research is interdisciplinary in nature and is conducted by multidisciplinary teams. The results of his research have been reported in a variety of peer-reviewed journal articles. He is an author and co-author of over 350 peer-reviewed articles.
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Tessa Cook, MD, PhD
Assistant Professor of Radiology
Perelman School of Medicine
University of Pennsylvania
Title: Deploying AI in the Clinical Radiology Workflow: Challenges, Opportunities, and Examples
Abstract: Although many radiology AI efforts are focused on pixel-based tasks, there is great potential for AI to impact radiology care delivery and workflow when applied to reports, EMR data, and workflow data. Radiology-pathology correlation, identification of follow-up recommendations, and report segmentation can be used to increase meaningful feedback to radiologists as well as to automate tasks that are currently manual and time-consuming. When deploying AI within the clinical workflow, there are many challenges that may slow down or otherwise affect the integration. Careful consideration of the way in which radiologists may expect to interact with AI results should be undertaken to meaningfully deploy radiology AI in a safe and effective way.
Thursday MIPS Roundtable: “Ask the Radiologist about COVID-19”
Hosted by Dr. Heike Daldrup-Link & Dr. Gunilla Jacobson
Join us virtually to ask anything you would like to know about COVID-19 and the current status in our community. This week we will open the floor for questions. Please prepare questions related to COVID-19. Feel free to submit questions in advance to Ashley Williams (ashleylw@stanford.edu) or submit them in the chat on Zoom.
Meeting URL: https://stanford.zoom.us/j/186504700
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 186 504 700c talking about the future of medicine and science.
Thursday MIPS Roundtable
Hosted by Dr. Kathy Ferrara & Dr. Gunilla Jacobson
Join us virtually to ask anything you would like to know about career plans, collaborations, new expertise and online lectures during the shutdown. Feel free to submit questions in advance to Ashley Williams (ashleylw@stanford.edu) or submit them in the chat on Zoom.
1:30-2:00 PM | Dr. Kathy Ferrara, Ph.D.
“Developing a career plan, collaborations and new expertise during the shutdown”
Professor of Radiology
Stanford University
2:00-2:30 PM | Dr. Angie Louie, Ph.D.
“How to create online lectures using Camtasia, Playposit and Canvas”
Professor of Biomedical Engineering
UC Davis
Meeting URL: https://stanford.zoom.us/j/186504700
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 186 504 700
Thursday MIPS Roundtable: ePad: A Web-based Imaging Informatics Platform for Image Annotation and Quantitative Analysis
Come learn about ePAD (https://epad.stanford.edu) from Dr. Rubin’s lab, a freely-accessible web-based informatics platform for universal access to radiology images, annotations, and quantitative analysis. We’ll also open the floor to a discussion on integrating ePAD into the SCi3 preclinical imaging workflow and how together we can crowdsource data sharing and reuse, and accelerate quantitative imaging research for everyone.
Daniel Rubin, PhD
Professor of Biomedical Data Science, of Radiology, of Medicine, and, by courtesy, Ophthalmology and of Computer Science
Jason Thanh Lee, PhD
Director of the Clark Center Facility
Laura Pisani, PhD
Associate Director of the Clark Center Facility
Frezghi Habet, PhD
Director of the Porter Drive Facility
MIPS Roundtables will be every Thursday from 1:30-2:30pm showcasing various topics and are open to all interested.
Please note Zoom information does change week to week.
4/9 Meeting URL: https://stanford.zoom.us/j/114866791
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 114 866 791
Thursday MIPS Roundtable: Dr. Michelle James & Dr. Ted Graves
1:30-2:00 PM – Michelle L. James, Ph.D.
Neuroimmune Imaging Research and Discovery (NiRD) Lab
Assistant Professor of Radiology and of Neurology
Stanford University
2:00-2:30 PM – Edward “Ted” Graves, Ph.D.
Imaging Radiobiology Laboratory, Division of Radiation Oncology Medical Physics
“Using Imaging to Study Radiation Biology”
Associate Professor of Radiation Oncology (Radiation Physics) and, by courtesy, of Radiology
Stanford University
MIPS Roundtables will be every Thursday from 1:30-2:30pm showcasing various topics and are open to all interested.
Please note Zoom information does change week to week.
4/16 Meeting URL: https://stanford.zoom.us/j/406957830
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 406 957 830
Radiomics and Radio-Genomics: Opportunities for Precision Medicine
Zoom: https://stanford.zoom.us/j/99904033216?pwd=U2tTdUp0YWtneTNUb1E4V2x0OTFMQT09
Pallavi Tiwari, PhD
Assistant Professor of Biomedical Engineering
Associate Member, Case Comprehensive Cancer Center
Director of Brain Image Computing Laboratory
School of Medicine | Case Western Reserve University
Abstract:
In this talk, Dr. Tiwari will focus on her lab’s recent efforts in developing radiomic (extracting computerized sub-visual features from radiologic imaging), radiogenomic (identifying radiologic features associated with molecular phenotypes), and radiopathomic (radiologic features associated with pathologic phenotypes) techniques to capture insights into the underlying tumor biology as observed on non-invasive routine imaging. She will focus on clinical applications of this work for predicting disease outcome, recurrence, progression and response to therapy specifically in the context of brain tumors. She will also discuss current efforts in developing new radiomic features for post-treatment evaluation and predicting response to chemo-radiation treatment. Dr. Tiwari will conclude with a discussion on her lab’s findings in AI + experts, in the context of a clinically challenging problem of post-treatment response assessment on routine MRI scans.
Thursday MIPS Roundtable: Thera(g)nostics: Current clinical use and future needs
1:30-2:15 PM | Dr. Carina Mari Aparici, M.D.
Thera(g)nostics: Current clinical use and future needs
Bringing the clinical needs with preclinical research efforts
Clinical Professor of Radiology – Nuclear Medicine
Director, Targeted Radionuclide Therapy Program
Stanford University
2:15-2:30 PM | Dr. Gunilla Jacobson, Ph.D.
MIPS Theranostics Interest Group
How to join and learn more
Deputy Director
Molecular Imaging Program at Stanford, Radiology
Stanford University
MIPS Roundtables will be every Thursday from 1:30-2:30pm showcasing various topics and are open to all interested.
Please note Zoom information does change week to week.
4/23 Meeting URL: https://stanford.zoom.us/j/639510777
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 639 510 777
Thursday MIPS Roundtable: Faculty Lab Showcase
1:30-2:00 PM | Dr. Guillem Pratx, Ph.D.
The Physical Oncology Lab
Assistant Professor of Radiation Oncology (Radiation Physics)
Stanford University
2:00-2:30 PM | Dr. Craig Levin, Ph.D.
Molecular Imaging Instrumentation Laboratory
Professor of Radiology and, by courtesy, of Physics,
of Electrical Engineering and of Bioengineering
Stanford University
MIPS Roundtables will be every Thursday from 1:30-2:30pm showcasing various topics and are open to all interested.
Please note Zoom information does change week to week.
4/30 Meeting URL: https://stanford.zoom.us/j/630252651
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 630 252 651