PW42_2025_GranCanaria: Add project DeployingOvsegInSlicer

PW42_2025_GranCanaria/Projects/DeployingOvsegInSlicer/README.md

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+---
+layout: pw42-project
+
+permalink: /:path/
+
+project_title: Deploying OvSeg in Slicer
+category: Segmentation / Classification / Landmarking
+
+key_investigators:
+
+- name: Paolo Zaffino
+  affiliation: Magna Graecia University of Catanzaro
+  country: Italy
+
+- name: Thomas Buddenkotte
+  affiliation: University Medical Center HamburgEppendorf
+  country: Germany
+
+---
+
+# Project Description
+
+<!-- Add a short paragraph describing the project. -->
+
+
+[OvSeg](https://github.com/ThomasBudd/ovseg/) is a deep learning-based library for the segmentation of high-grade serous ovarian cancer on CT images. Right now, to obtain the segmentations the user has to write some lines of Python code, making the tool not directly usable by non-technical people. It would be great to expose this algorithm in Slicer to be used in a codeless manner.
+
+
+
+## Objective
+
+<!-- Describe here WHAT you would like to achieve (what you will have as end result). -->
+
+
+1. Expose OvSeg in 3D Slicer in order to provide a scalar volume as input and obtain the segmentations as a segmentation node
+
+
+
+
+## Approach and Plan
+
+<!-- Describe here HOW you would like to achieve the objectives stated above. -->
+
+
+1. Create a Slicer extension
+2. Let the extension to install OvSeg via pip
+3. Let the extension pull the CT volume, run the inference and push back the segmentations
+
+
+
+
+## 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. Describe specific steps you **have actually done**.
+
+
+
+
+# Illustrations
+
+<!-- Add pictures and links to videos that demonstrate what has been accomplished. -->
+
+
+_No response_
+
+
+
+# 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. -->
+
+
+1. [https://github.com/ThomasBudd/ovseg/](https://github.com/ThomasBudd/ovseg/)
+2. [https://www.repository.cam.ac.uk/items/d7d9011c-2518-4a7a-8b85-01b086d672fc](https://www.repository.cam.ac.uk/items/d7d9011c-2518-4a7a-8b85-01b086d672fc)
+3. [https://eurradiolexp.springeropen.com/articles/10.1186/s41747-023-00388-z](https://eurradiolexp.springeropen.com/articles/10.1186/s41747-023-00388-z)
+