The goal of this half-day MICCAI2021 tutorial is to promote research of deep 2D-3D modeling and learning in medical image computing. Specifically, the participants will gain an understanding to effectively utilize 2D and 3D imaging data in deep learning systems on a variety of tasks, including image reconstruction, registration, segmentation, etc.

We will begin by introducing imaging physics on modalities of interests, and several tools/formulations that enable differentiable computation between 2D-3D images. We will then present a survey on methods on 2D-3D modelling both with and without differentiable operators. Finally, speakers will present more detailed spotlight talks on 2D-3D modelling in specific domains and tasks.

To best adapt to the online format, we will employ a combination of pre-recorded talks and live Q&A discussions. Please refer to the schedule section for video and live discussion links, which will be updated at the time of the tutorial.

Material and videos about the tutorial will be released on this website after the conclusion of MICCAI 2021.

• To gain basic understanding of the relationship between observed acquisitions and reconstructed images for various modalities.

• To survey recent literature that explore such relationships in a learning system, both implicitly and explicitly, on a variety of tasks

• To understand the advantages of 2D-3D modelling with differentiable operators.

• To look ahead for potential directions for improvement and take inspiration from the natural image counterparts.

Coffee break 16:00 - 16:10 UTC