iNPH Analysis

Overview

High level Overview of the Flow Chart

This study examines whether quantifiable changes can be detected in ventricular volume in Idiopathic Normal Pressure Hydrocephalus (iNPH) patients that undergo ventriculo-peritoneal shunt procedures.

How to Run iNPH Analysis Module:

STEP 1. Login

From the BisQue Homepage, the top right-hand corner has the Sign In button.

If you do not have an account, you can create one. If you already have an account, login using your credentials.

STEP 2. Upload Input Scans

BisQue supports many of the popular medical imaging file formats, i.e. NIFTI, DICOM.

Here is the sample input image pair that is already present on BisQue.

• Pre_Scan.dcm

• Post_Scan.dcm

One can test the module using the above pair, or use their own pair of scans as input.

STEP 3. Go To iNPH Analysis Module Homepage

Once you are logged in successfully, you can access the NPH Prediction module by Clicking Here or by using the Menu bar at the top of the homepage.

From the Menu Bar. Using the Menu bar at the top of the screen, go to Analyze --> Segmentation --> iNPH Analysis. You might have to scroll down a little bit since we are adding more modules.

STEP 4. Run iNPH Analysis Module

Once on the NPH Prediction homepage,

• Select an image you uploaded.

  • Click on Select an Image Button under Pre Scan and Post Scan

  • It should navigate you to Resource Browser, where you should be able to select an image that is uploaded.

  • You should be able to see the selected images on the module page.

  

• Select a PyTorch Model, which is the magic box that is trained to predict the presence of iNPH by looking at the input scan.

  • Click on Select file button.

  • Cick on Gear Icon on top right corner of Resource Browser. Tick the box that corresponds to Show public data.

  

  • In the search bar, type filename:inph_analyser.pt and hit Enter. This should filter out the required pytorch files. Now, select the first model in the list and hit Select.

  

• Hit RUN Button.

• It is expected to take around 200 seconds to 500 seconds of runtime, depending upon the input file size, cluster compute availability, and various other factors.

• Visualize the Results

  

• One can export the data into a CSV or XML file as well.