Name
Abstract | ||
|---|---|---|
Statistical shape analysis plays a key role in various medical imaging applications. Such methods provide tools for registering, deforming, comparing, averaging, and modeling anatomical shapes. In this work, we focus on the application of a recent method for statistical shape analysis of parameterized surfaces to simulation of endometrial tissue shapes. The clinical data contains magnetic resonance imaging (MRI) endometrial tissue surfaces, which are used to learn a generative shape model. We generate random tissue shapes from this model, and apply elastic semi-synthetic deformations to them. This provides two types of simulated data: (1) MRI-type (without deformation) and (2) transvaginal ultrasound (TVUS)-type, which undergo an additional deformation due to the transducer׳s pressure. The proposed models can be used for validation of automatic, multimodal image registration, which is a crucial step in diagnosing endometriosis. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.neucom.2015.03.098 | Neurocomputing |
Keywords | Field | DocType |
Elastic deformation,Endometriosis,Simulation,Statistical shape model | Transducer,Computer vision,Pattern recognition,Medical imaging,Statistical shape analysis,Artificial intelligence,Statistical model,Deformation (mechanics),Deformation (engineering),Elasticity (economics),Image registration,Mathematics | Journal |
Volume | Issue | ISSN |
173 | P1 | 0925-2312 |
Citations | PageRank | References |
1 | 0.34 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kurtek, Sebastian | 1 | 246 | 21.52 |
| Qian Xie | 2 | 26 | 8.33 |
| Chafik Samir | 3 | 185 | 17.69 |
| Michel Canis | 4 | 26 | 3.91 |