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+---
+layout: pw42-project
+
+permalink: /:path/
+
+project_title: 'DeCA: Dense Correspondence Analysis Toolkit for Shape Analysis'
+category: Quantification and Computation
+
+key_investigators:
+
+- name: Sara Rolfe
+  affiliation: Seattle Children's
+  country: USA
+
+- name: Murat Maga
+  affiliation: Seattle Children's
+  country: USA
+
+---
+
+# Project Description
+
+<!-- Add a short paragraph describing the project. -->
+
+
+DeCA (Dense Correspondence Analysis) is an open-source tool for biologists and other researchers using 3D imaging. DeCA integrates biological insights in the form of homologous landmark points with dense surface registration to provide highly detailed shape analysis of smooth and complex structures that are typically challenging to analyze with sparse manual landmarks alone.
+
+Currently, DeCA exists as a prototype that can be run within 3D Slicer. We have collected preliminary feedback from initial users to improve the interface and workflow. The goal of this project is make and test these updates and publish DeCA as an extension.
+
+
+
+## Objective
+
+<!-- Describe here WHAT you would like to achieve (what you will have as end result). -->
+
+
+1. Objective A. Implement an improved workflow for the DeCA module
+2. Objective B.. Publish DeCA extension
+
+
+
+## Approach and Plan
+
+<!-- Describe here HOW you would like to achieve the objectives stated above. -->
+
+
+1. Update the DeCA interface to simplify running analysis
+3. Demo/test improvements and collect feedback
+4. Document workflow
+5. Publish extension
+
+
+
+
+## Progress and Next Steps
+
+<!-- Update this section as you make progress, describing of what you have ACTUALLY DONE.
+     If there are specific steps that you could not complete then you can describe them here, too. -->
+
+
+1. Document changes from initial user testing
+
+
+
+# Illustrations
+
+<!-- Add pictures and links to videos that demonstrate what has been accomplished. -->
+
+
+![Image](https://github.com/user-attachments/assets/195e437a-abb5-49e1-bd8d-59ed6a00535a)
+
+<img width="600" alt="DeCA prototype" src="https://github.com/user-attachments/assets/18504eca-2b44-4362-93b7-c953c514b0cd" />
+
+
+
+
+
+# Background and References
+
+<!-- If you developed any software, include link to the source code repository.
+     If possible, also add links to sample data, and to any relevant publications. -->
+
+
+Source: [https://github.com/smrolfe/DeCA](https://github.com/smrolfe/DeCA)
+
+Publications: 
+- Rolfe, S. M., and A. Murat Maga. "DeCA: A Dense Correspondence Analysis Toolkit for Shape Analysis." International Workshop on Shape in Medical Imaging. Cham: Springer Nature Switzerland, 2023.
+
+- Rolfe, S. M., Mao, D., & Maga, A. M. (2024). Streamlining Asymmetry Quantification in Fetal Mouse Imaging: A Semi-Automated Pipeline Supported by Expert Guidance. bioRxiv, 2024-10.
+