Crop dataset by optic disc? about optic-nerve-cnn HOT 13 OPEN

@SamuelMarks this is, of course, a good approach. You can take validation indices for e.g. RIM-ONE v.3 that I used (generated as test_idx in this notebook), then either obtain predictions for optic nerve using this notebook or use pre-calculated ground truth ROIs (h5f['RIM-ONE v3/512 px/disc_locations'], where h5f is h5py object for RIM-ONE v.3 database.) The ground truth bounding boxes are already calculated with 20 pixels margin from each side; you can find h5py files creation notebook here.

Also note that the provided pretrained networks can perform much worse on images from different / biased domains, as a small number of training samples was used for all networks.

If you'd like to try predicting the diagnosis, I can recommend this paper as a very decent paper about CNNs for glaucoma diagnosis, which extract more relevant features for glaucoma than pure CDR.

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Yes, you can, sure

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@SamuelMarks, you are welcome.
Not sure I am getting your idea right... I am actually already leaving some extra gap (20 pixels in 512 x 512 resolution) near each side of a bounding box. (I admit the code is probably organized not the best way to understand it)
Or you suggest adding a pre-trained network for optic disc segmentation that will provide a bounding box on the fly?

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@seva100 Hmm, maybe it's easier if I explain the pipeline I'm envisioning:

1. Take new set of fundus images (that your network hasn't trained on)
2. Crop this new set of images so that only the optic nerve (+ a little more) is visible
3. Train this new cropped set of images on a new convolutional neural network aimed at diagnosis

So my question is, using your network + tools, how do I do the first two steps?

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@seva100 Actually I am still a little confused about how to set this up.

Can you provide a worked example? - I.e.: given dataset of fundus images in folder_dir, crop and create new fundus images with just the optic nerve in new_folder_dir?

PS: Will have the Python 3 compatibility PR to you sometime today.

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@SamuelMarks If you use hdf5 datasets I provide and they reside in folder_dir, they you have disc_locations attribute in hdf5 which provides you the bounding boxes:

import os
import skimage
from skimage.transform import resize
import h5py
from imageio import imsave

h5f = h5py.File(os.path.join("folder_dir", "RIM-ONE v3.hdf5"))
images = h5f['RIM-ONE v3/512 px/images']
disc_locations = h5f['RIM-ONE v3/512 px/disc_locations']
# disc_locations[i] contains (min_i, min_j, max_i, max_j)
output_res = (256, 256, 3)
for i in range(len(images)):
    min_i, min_j, max_i, max_j = disc_locations[i]
    cropped = images[i, min_i:max_i, min_j:max_j]
    resized = resize(cropped, output_res)
    imsave(os.path.join('new_folder_dir', '{}.png'.format(i)), resized)

If you are only provided with the ground truth masks or masks predicted by some network, you can for example use the following function to get the bounding boxes from OD masks:

import numpy as np
from operator import attrgetter
import skimage
from keras import backend as K

def bboxes_from_disc_masks(Y, gap=1.0 / 25.0):
    """Accepts:
    * Y -- numpy array of shape (N, H, W, C) if TF ordering is enabled, or (N, C, H, W) is enabled,
        with binary values --- masks of OD
    * gap --- float; percentage of image side length, which will be left as a margin from each side.
    Returns:
    * disc_locations -- numpy array of shape (Y.shape[0], 4) --- inferred locations of OD, where
        disc_locations[i] contains (min_i, min_j, max_i, max_j)
    """
    disc_locations = np.empty((Y.shape[0], 4), dtype=np.float64)

    for i in range(Y.shape[0]):
        if K.image_dim_ordering() == 'th':
            disc = Y[i, 0]
            h, w = Y.shape[2:4]
        else:
            disc = Y[i, ..., 0]
            h, w = Y.shape[1:3]

        labeled = skimage.measure.label(disc)
        region_props = skimage.measure.regionprops(labeled)
        if len(region_props) == 0:    # no component found (this can theoretically happen due to the data augmentation)
            disc_locations[i][0] = 0
            disc_locations[i][1] = 0
            disc_locations[i][2] = 1
            disc_locations[i][3] = 1
        else:
            component = max(region_props, key=attrgetter('area'))    # there should be only 1 component, 
                                                                     # so this is a safety measure
            gap_i = int(float(gap) / h)
            gap_j = int(float(gap) / w)
            disc_locations[i][0] = max(component.bbox[0] - gap_i, 0) / float(h)
            disc_locations[i][1] = max(component.bbox[1] - gap_j, 0) / float(w)
            disc_locations[i][2] = min(component.bbox[2] + gap_i, h - 1) / float(h)
            disc_locations[i][3] = min(component.bbox[3] + gap_j, w - 1) / float(w)
    return disc_locations

I hope this answers your question.

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Thanks, so @seva100 if I had no ground truths, this would still [nominally] work, for cropping out the optic nerve?

(e.g.: only using ground-truths that you provided in RIM-ONE)

For example, taking this fundus image from wikipedia and cropping such that only the optic disc + 20px remain

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@SamuelMarks if you don't have the ground truth information, you either need to crop the images by hand or extract bounding boxes from the output of a segmentation model.

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So this isn't a segmentation model then?

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This repository contains segmentation models, if you ask about this

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Let me rephrase: can I use this network to automatically crop out the optic nerve (+20px) from my fundus photo dataset?

[Given that my fundus photo dataset is wholly unannotated in this respect]

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@SamuelMarks Hello, I am also trying to crop the fundus image around the optic disk area. I tried to use the pre-trained model RIM-ONE v3, but I could not load it successfully. How did you do the cropping? Could you please provide some code for reference?
Thanks a lot.

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@xqhuang123 You'll want to tag @seva100

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