Welcome to our MICCAI (2024) tutorial website for MedShapeNet. Here, you can find essential information about the program, speakers, organizers, and useful links:

• Program
• Speakers
• Organizers
• MedShapeNet Website
• Paper
• Python API (MedShapeNetCore) with Google Colab examples.

We invite you to join our tutorial. If you have any questions, please contact us.

MedShapeNet Overview

Before the deep learning era, statistical shape models (SSMs) were widely used in medical imaging. MedShapeNet builds upon this foundation, aiming to bridge computer vision methods to medical problems and clinical applications, inspired by benchmarks like ShapeNet and Princeton ModelNet.

MedShapeNet boasts a collection of over 100,000 medical shapes, covering bones, organs, vessels, muscles, and surgical instruments. These shapes can be searched, viewed in 3D, and downloaded individually using our shape search engine. It’s important to note that MedShapeNet is intended for research and educational purposes only. MedShapNet usefullness was demonstrated by its incorporation in research papers.

Tutorial

The tutorial spans 4.5 hours and focuses on 3D shape analysis in medical imaging. It includes a hands-on session utilizing MedShapeNetCore. Topics covered include motivation, shape acquisition, processing pipelines, and selected use cases. Our organizing team includes esteemed members such as Dr. Zongwei Zhou, Dr. Jiancheng Yang, and Dr. Beatriz Paniagua. For more detailed information, please refer to the program section.

Speakers

TBA

Prof. Dr. Xiaojun Chen

Professor Xiaojun Chen, a Full Professor at Shanghai Jiao Tong University (SJTU), China, is a leading authority in biomedical engineering and computer-assisted surgery. With over 200 peer-reviewed articles and 20 patents to his name, his research spans crucial areas such as biomedical image analysis, AI in biomedical physics, and medical robotics. Notably, he has received prestigious awards including the National Science & Technology Progress Award of China (2019) and the “France Talent Innovation (FTI) Program” Award. His international recognition is evident through visiting professorships at Harvard Medical School, CNRS in France, and others.

Dr. Zongwei Zhou

Dr. Zongwei Zhou, a postdoctoral researcher at Johns Hopkins University, is recognized for his groundbreaking work in reducing annotation efforts for computer-aided detection and diagnosis. His accolades, including the AMIA Doctoral Dissertation Award and the MICCAI Young Scientist Award, underscore his contributions to the field. Dr. Zhou’s recognition by Stanford University further highlights his impact and expertise.

Dr. Jiancheng Yang

Dr. Jiancheng Yang, a researcher at EPFL, collaborates with Prof. Pascal Fua on AI for health, medical image analysis, and 3D vision. With over 50 publications in esteemed journals and conferences, including MICCAI and NeurIPS, Dr. Yang’s research is highly regarded. His success in AI competitions and leadership in organizing MICCAI challenges demonstrate his versatility and expertise.

MSc. Gijs Luijten

M.Sc. Gijs Luijten a PhD candidate on the FWF enFaced 2.0 project, specializes in AR applications for maxillofacial surgery. With experience in 3D scanning, printing, and augmented reality at Radboudumc Nijmegen. He gained experience in Unity and Unreal Engine development, evident in contributions to a surgical scene datasets, HL2 applications, and help with organization of an ISBI challenge. Additionally, he is venturing into machine learning integration for medical shapes and data in extended reality applications.

MSc. Jana Fragemann

M.Sc. Jana Frageman is a mathematician by training, having completed her masters at the University Duisburg Essen. In her PhD research at IKIM she is working on analyzing the latent space representations of supervised and unsupervised generative models for radiological data. She organized prestige events in Essen such as ETIM, and co-organized the MICCAI workshop Medical Applications with Disentanglements (MAD)

Program

Please note that the programme schedule may be subject to change in order to accommodate key-note speakers. Additionally, the time slot for the MICCAI ShapeMi workshop is not yet confirmed, but every effort will be made to enable those interested in the ShapeMi workshop to attend both shape-related events. Both shape-oriented events will take place on day one of the MICCAI 2024 conference.

The tutorial focuses on 3D shape analysis in medical imaging and comprises two main sessions:

• Content:
  • Comprehensive background of the MedShapeNet database
  • An overview of classical as well as modern learning-based shape analysis methods
  • Concept of shape in machine learning and medical image analysis tasks
  • Highlighting a dataset and its potential use within the MedShapenet API
• Format: Presentation/discussion style

• Content:
  • Introduction of MedShapeNet and its Python toolbox
  • Installation, environment setup, implementation, and execution of selected use cases
  • Getting started with Google Colab environment setup
  • Warming up with dataset download and shape processing functions
  • Development of minimal solutions for selected use cases related to medical shape analysis
  • Selected use cases
• Format: Speaker-led demonstration with hands on possiblities

Learning Objectives

• Gain an overview of classical and modern learning-based shape analysis methods.
• Learn to utilize MedShapeNet for shape data retrieval from its web interface and develop data-driven machine learning models for medical applications.
• Explore the Python API of MedShapeNet, including its usage in machine learning frameworks like MONAI and TensorFlow.
• Develop intuition and ability to integrate shape concepts into machine learning and medical image analysis tasks.

Python Toolbox (API)
for 3D Medical Shape Analysis

MedShapeNetCore is a subset of MedShapeNet, containing more lightweight 3D anatomical shapes in the format of mask, point cloud and mesh. The shape data are stored as numpy arrays in nested dictonaries in npz format Zenodo. This API provides means to downloading, accessing and processing the shape data via Python, which integrates MedShapeNetCore seamless into Python-based machine learning workflows.