Workshop on Statistical and Shape-Based Image Analysis With Applications in Medicine
July 10-12, 2019,
Room 202, Astro-Mathematics Building, National Taiwan University
News
[2019/07/16] Workshop recordings available on the website!
Register for the workshop before 17:00, July 8!
About the Workshop
Please join us for our inaugural MARCH workshop, an exciting 3-day event on statistical shape analysis and modeling. The workshop includes a full day course on statistical shape modeling from Marcel Lüthi and Ghazi Boubabene from the Graphics Vision Research Group at the University of Basel. They and other distinguished speakers will lead lectures and topical breakout sessions for participant teams throughout the event.
Statistical shape modeling has been a fundamental workhorse of medical image processing for decades. The fundamental idea is simple: build models that parameterize the shape of an object of interest (e.g., an organ, or bone) and leverage appropriate optimization techniques to fit an individual object. Shape modeling is essential in research and clinical practice. In this workshop, we will hear from leaders across the world who are developing the next generation of shape modeling tools and applying them to problems like tracking organ motion and neurodegenerative diseases, image analysis, surgical planning, implant design, and minimally-invasive surgery. A particular thread of the workshop will be practical short course sessions in which attendees will have the opportunity to learn first hand from our speakers, leveraging open source software to accomplish real-world shape-based modeling tasks with medical imaging data.
During the event, participant teams will have the opportunity to present their own work during social forums to support team formation for the rest of the MARCH program, and be paired with faculty mentors who will work with them throughout the year.
Schedule
July 10, 2019 (Wed)
[Opening Ceremony]
9:15-9:30 Opening Ceremony
[Short Course]
9:30-12:30 Statistical Shape Modelling - Computing the Human Anatomy (Marcel Luethi and Ghazi Bouabene)
[Group Photo & Lunch Time]
12:30-14:00
[Short Course]
14:00-17:00 Statistical Shape Modelling - Computing the Human Anatomy (Marcel Luethi and Ghazi Bouabene)
[Tea Time]
17:00-18:00 Ice-Breaking Tea Time
July 11, 2019 (Thu)
[Talks]
9:30-10:20 Analyzing the Changing Anatomy: Anatomical Regression Analysis (Sarang Joshi)
10:30-11:20 Open Source Shape Analysis (Jared Vicory)
11:30-12:20 Building a Cardiovascular Imaging Database for AI Research (Wen-Jeng Lee)
[Lunch Time]
12:20-14:00
[Short Course]
14:00-15:50 Digital Technologies in Neurosurgery: An End-to-End Case Series (Anthony Costa)
[Talks]
16:00-17:30 Lightning Presentations (NTU Students)
July 12, 2019 (Fri)
[Talks]
9:30-10:20 Computational Conformal Geometry with Applications (Wen-Wei Lin)
10:30-11:20 Shape from Scoping (Mathias Unberath)
11:30-12:20 Deep Learning for Medical Image Registration – Data or Prior (Yipeng Hu)
[Closing Ceremony]
12:20-12:30
[Lunch]
12:30-14:00
[Q&A Session and Roundtable Discussion]
14:00-17:00
Venue
Room 202, Astronomy-Mathematics Building, National Taiwan University
(Room 202, No. 1, Sec. 4, Roosevelt Road, Taipei, Taiwan)
Map: http://goo.gl/iutx
Transportation: https://visitorcenter.ntu.edu.tw/eng/p5-transportation.php
Speakers, Titles and Abstracts
July 10, 2019 9:30-17:00
Marcel Luethi
Department of mathematics and computer science, University of Basel, Switzerland
Ghazi Bouabene
Graphics Vision Research Group, University of Basel, Switzerland
Statistical Shape Modelling - Computing the Human Anatomy
Statistical shape models are one of the most important technologies in medical image analysis. With this technology, the computer learns the characteristic shape variations of an organ. The model resulting from this analysis may then be used in implant design, image analysis, surgery planning and many other fields. In this course, we will give an introduction to statistical shape models, from the unifying perspective of Gaussian processes. Besides presenting an overview of the main theoretical ideas underlying these models, we will show how these methods can be applied in practice. In tutorial sessions, the course participants will build their own shape model and apply it for the reconstruction of partial surfaces, using the open source software Scalismo.
July 11, 2019 9:30-10:20
Sarang Joshi
Scientific Computing and Imaging Institute, University of Utah, USA
Analyzing the Changing Anatomy: Anatomical Regression Analysis
In this talk I will present computational and analytical tools we have been developing at University of Utah for the analysis of anatomical image ensembles that are designed to capture changes in anatomy. The fundamental analytical framework we have been using is that of regression analysis where the dependent variable is the anatomical configuration while the independent variable is application domain specific. I will exemplify the application of this general methodology to various medical imaging applications ranging from the analysis of Internal Organ Motion as imaged in 4D respiratory correlated CT imaging spanning few minutes to that of the study changes in brain anatomy associated with normal aging and neurodegenerative diseases such as Alzheimer’s spanning decades.
July 11, 2019 10:30-11:20
Jared Vicory
Kitware, Inc, USA
Open Source Shape Analysis
From price to reproducibility to ease of collaboration and dissemination, open source software is becoming increasingly important for scientific research. This is especially true in the biomedical domain. Having the newest and most advanced methodologies freely available can accelerate research efforts and positively impact millions of lives. In this talk I will take a single data set and demonstrate how to perform various shape analysis tasks using SlicerSALT, an open-source package for dissemination of advanced shape analysis techniques.
July 11, 2019 11:30-12:20
Wen-Jeng Lee
Department of Medical Imaging, National Taiwan University Hospital, Taiwan
Building a Cardiovascular Imaging Database for AI Research
Artificial intelligence (AI) has recently been successful applied to many applications, including medical imaging. The basic steps of medical imaging AI development are: 1. Find a clinical unmet need. 2. Define data cohort. 3. Collect medical images and clinical info. 4. Data cleaning and identification. 5. Data set annotation. 6. AI model building, validation and testing. Our team is composed of well-experienced cardiologists and radiologists from various medical centers in Taiwan, as well as researchers specialized at AI/ database management/imaging processing/law of science and technology from different universities. The goal is to build a multi-modality coronary artery disease-based imaging bank, including coronary computed tomographic angiography (CCTA), single photon emission computed tomography (SPECT), invasive coronary angiography (ICA), intravascular ultrasound (IVUS), optical coherence tomography (OCT) and fractional flow reserve (FFR). In this presentation, I will talk about our experience in building a large imaging database for AI research and our preliminary results.
July 11, 2019 14:00-17:00
Anthony Costa
Sinai BioDesign and AISINAI, Icahn School of Medicine at Mount Sinai, USA
Digital Technologies in Medicine: An End-to-End Case Series
We will follow the story of a patient through one of the most significant milestones of their life: a neurosurgical procedure. Translational digital technologies are used throughout the patient pipeline, from engagement and education to diagnosis, surgical decision support, intraoperative performance augmentation, patient monitoring, recovery, and rehabilitation. Didactic lectures introducing the state-of-the-art in each topic area will be accompanied with interactive engineering exercises supported by provided Jupyter notebooks. At the end of the course, students will have an appreciation of many new ways their work in machine learning can impact clinical practice at the level of an individual patient.
Course Pre-request: Please familiarize yourself with iPython/Jupyter notebooks and come familiar with their use. They will be made available on github prior to the course.
July 12, 2019 9:30-10:20
Wen-Wei Lin
Department of Applied Mathematics, National Chiao Tung University, Taiwan
Computational Conformal Geometry with Applications
Manifold parameterizations have been applied to various fields of commercial industries. Several efficient algorithms for the computation of triangular surface mesh parameterizations have been proposed in recent years. However, the computation of tetrahedral volumetric mesh parameterizations is more challenging due to the fact that the number of mesh points would become enormously large when the higher-resolution mesh is considered and the bijectivity of parameterizations is more difficult to guarantee. In this talk, we develop a novel volumetric stretch energy minimization algorithm for volume-preserving parameterizations of simply connected 3-manifolds with a single boundary under the restriction that the boundary is a spherical area-preserving mapping. In addition, our algorithm can also be applied to compute spherical angle- and area-preserving parameterizations of genus-zero closed surfaces, respectively. Several numerical experiments indicate that the developed algorithms are more efficient and reliable compared to other existing algorithms. Numerical results on applications of the manifold partition and the mesh processing for three-dimensional printing are demonstrated thereafter to show the robustness of the proposed algorithm.
July 12, 2019 10:30-11:20
Mathias Unberath
Department of Computer Science, Johns Hopkins, USA
Shape from Scoping – Self-supervised Depth Estimation and Reconstruction from Sinus Endoscopy
Recent advances in computer vision, including leaps in machine learning systems, fuel cutting edge research on contextual and task-aware computer assistance solutions that streamline workflows while catering to the physicians' needs to enable improved clinical decision making. In this talk, I will highlight some of our recent work aiming to push the boundaries in navigated sinus endoscopy. I will focus on self-supervised learning from monocular video without photometric constancy, demonstrating applications in monocular depth estimation, correspondence finding, and photo-realistic dense reconstruction.
July 12, 2019 11:30-12:20
Yipeng Hu
Medical Image Computing, University College London, UK
Deep Learning for Medical Image Registration – Data or Prior
Recent medical image registration methods based on deep neural networks have seemly converged to so-called end-to-end learning approaches, in which the networks are trained to directly predict spatial transformation between a given pair of unprocessed images. Besides fast inference that enables sub-second volumetric registration, some have shown superior accuracy over the classical methods. I will first give a technical overview of the classical medical image registration and the state-of-the-art deep-learning-based examples, including the unsupervised and the (weakly-) supervised methods. I will conclude this talk with an introduction to our latest development using a more data-driven attempt to replace the transformation-predicting alternatives.
Recordings
2019/07/10:
Opening: https://youtu.be/RRjUTjk6jcI
Statistical Shape Modelling - Computing the Human Anatomy (Marcel Luethi and Ghazi Bouabene)
01: https://youtu.be/bkAFstjDZWU
02: https://youtu.be/QTGHVaY7pSg
03: https://youtu.be/YNwzs4bHXuM
04: https://youtu.be/zGdotmCNOX4
05: https://youtu.be/8kXpiEJqjk8
06: https://youtu.be/ItAw5cfWMLE
07: https://youtu.be/G8moCGbjbIs
2019/07/11:
Analyzing the Changing Anatomy: Anatomical Regression Analysis (Sarang Joshi): https://youtu.be/VHPfLHmDsss
Open Source Shape Analysis (Jared Vicory): https://youtu.be/5b9X0LNf3yk
Building a Cardiovascular Imaging Database for AI Research (Wen-Jeng Lee): https://youtu.be/U-en_mAPoH4
Digital Technologies in Neurosurgery: An End-to-End Case Series (Anthony Costa): https://youtu.be/CwKu-Cdc11U
Presentations from Medical Data Analytics Laboratory (MeDA Lab): https://youtu.be/5S8pc19z3Vc
2019/07/12:
Shape from Scoping (Mathias Unberath): https://youtu.be/tTdyftUAiVM
Deep Learning for Medical Image Registration -Data or Prior (Yipeng Hu): https://youtu.be/BelXqC67sz0
Organizers
Anthony Costa (Icahn School of Medicine at Mount Sinai)
Cheyu Hsu (National Taiwan University Hospital)
Wei-Chih Liao (National Taiwan University Hospital)
Eric Oermann (Icahn School of Medicine at Mount Sinai)
Weichung Wang (National Taiwan University)
Contact Person
Ms. Claire Liu (劉馥瑤小姐), Tel: +886-2-3366-8819, Email: claireliu@ncts.ntu.edu.tw