I want to train a Neural Network to detect a vessel in CT Perfusion data. For that, I need to annotate some DICOM images using the “3D slicer”. I want the network to detect the vessel by putting a rectangular box on the region. I want to use the ‘MarkupsPlane’ to annotate the images.
Should I save the annotation and DICOM images as “.nrrd” and “.json” files to read in the neural network? I am guessing the “.json” file will work as the ground-truth label for detection.
Also, some of the patients’ data were collected with a scanner that creates 190 slices per scan and some were collected with a scanner that creates 158 slices. So, half of the “.nrrd” has a size of 512x512x190 and the rest has 512x512x158. Can I resize them to a common size (e.g. 128x128x128) before feeding them in the neural net?
Thanks @Fernando . But I want to use TensorFlow for implementing the Neural Net. In that case, what should I use? I guess I can use TorchIO only if I implement my DNN using pytorch.
Also, is “.json” file enough for ground truth labeling?
Why do you want to use TensorFlow? I think it’s possible to mix TorchIO and TensorFlow, but I haven’t tried. I don’t know any currently-maintained framework for medical images with TensorFlow, we are all using PyTorch now (MONAI, TorchIO, nnU-Net, Rising, batchgenerators, medicaltorch…). Maybe you can use pymia. But this is off-topic.
I think JSON is fine, as all you need is 6 numbers representing the bounding box. You could even use a text file, a CSV, a .npy file… It doesn’t really matter, as long as you can read it during training.
I have never used PyTorch before. I am comfortable with TensorFlow only. Maybe I will switch over to PyTorch in future.
I have attached a pic of my “.json” file generated by 3D Slicer after using markups Plane. I am not sure which 6 numbers you meant. In my “.json”, there are 3 values in ‘position’, 9 values in ‘orientation’. Also some other numbers for ‘color’ and stuff.
I am not familiar with the new Markups system, but that seems to be the contents of a .mrk.json file used to store fiducial points in a markups node. Basically, each 3D point can be represented with 3 coordinates. You mentioned a “rectangular box”, so I assumed you wanted to save an .acsv file representing an ROI node, which is a 3D bounding box that can be represented using 6 numbers, e.g., the center and the axes lengths.
If you fully define a plane then you will have 3 control points. First control point position is the center, second control points position determines is the x axis direction and x size, third control point determines rotation around x axis and y size of the plane.
You might find the new Markups ROI easier to use. The ROI is axis-aligned by default and specified by center, orientation, and size fields.
Thanks @lassoan . I am going to use the ROI. I think it is better suited and easier to understand. I have attached the “.acsv” file generated for ROI bounding box. It has 6 values. I think the top 3 points are the center coordinates for x, y , and z. The bottom 3 points represent the length of the box along the x, y, and z-axis. Is this right?
