In this project we explore the feasibility of employing geometric deep learning or point cloud-based deep learning for extracting useful features from shapes in medical images.
Figure 1: Shape Analysis in different medical datasets [5]
In this project, we explore the feasibility of employing geometric deep learning or point cloud-based deep learning for extracting useful features from shapes in medical images.
Anatomical shape or morphology is an important pointer in population studies. „How does this sample differ from the rest?“. On the other hand, shape is not apparent, especially in 3D medical imaging. Moreover, typical analysis algorithms are image-centered, with no explicit processing of shape information. Therefore, in this project, we side-step image analysis, and move to alternate representations of 3D objects in the form of meshes and point clouds. We check the feasibility of learning useful information from these representations, which eventually can be used for augmenting the learning from images.
Two directions of exploration according to student interest and skills, both with the option of the data type (meshes or point clouds):
Anjany Sekuboyina Fernando Navarro Bjoern Menze
[2] Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis. Anjany Sekuboyina et al.
[3] Discriminative and Generative Models for Anatomical Shape Analysis on Point Clouds with Deep Neural Networks. Benjamın Gutierrez-Becker, Ignacio Sarasua, Christian Wachinger.
ProjectForm | |
---|---|
Title: | Shape Analysis on Meshes or Point Clouds from Medical Data |
Abstract: | In this project we explore the feasibility of employing geometric deep learning or point cloud-based deep learning for extracting useful features from shapes in medical images. |
Student: | |
Director: | Prof. Bjoern Menze |
Supervisor: | Anjany Sekuboyina Fernando Navarro |
Type: | DA/MA/BA |
Area: | |
Status: | open |
Start: | 01.09.2021 |
Finish: | |
Thesis (optional): | |
Picture: |