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SpatialVisVR is a VR platform tailored for advanced visualization and analysis of medical images in immuno-oncology. It allows real-time capture and comparison of mIF and mIHC images via mobile devices. Leveraging deep learning, it matches and displays similar images, supporting up to 100 protein channels.

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jaiprakash1824/SpatialVisVR

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SpatialVisVR: A Virtual Reality Framework for Spatial Data Visualization

This repository encompasses MainApis, searchUIApis, the search_engine_algorithm, and a compressed Unity project complete with an apk file. To effectively deploy and test the project, follow the step-by-step guide provided below.

Overview

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Execution Steps:

  1. Network Connectivity: Ensure all devices are connected to the same Wi-Fi network.

  2. Environment Setup: Install the Python environment specified in the requirements.txt file.

  3. Launching the APIs: Run app1.py from both the MainApis and searchUIApis directories in separate terminal instances.

  4. Unity Project Configuration:

    • Navigate to the Unity project directory. (As the project is very large it not been attached)
    • Update the API endpoint with the appropriate IP address.
    • Subsequently, build the application and deploy it to the VR headset.
  5. Search Execution (Optional):

    • To initiate a search, launch the Droid Cam or ip webcam application on your smartphone.
    • Execute PathologyPipeline.py from the search_engine_algorithm directory. This activates the phone's camera.
    • Focus the camera on the desired slide.
    • In the VR application, tap the "update" button to synchronize and display the search results.

By adhering to this guide, users can seamlessly visualize spatial data in a virtual reality environment.

Demo

https://github.com/jaiprakash1824/SpatialVisVR/blob/main/demo/SpatialVisVR.mp4

About

SpatialVisVR is a VR platform tailored for advanced visualization and analysis of medical images in immuno-oncology. It allows real-time capture and comparison of mIF and mIHC images via mobile devices. Leveraging deep learning, it matches and displays similar images, supporting up to 100 protein channels.

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