Can't Visualise results

[Shubham0209]

@zsyzzsoft Thank you so much for all your help till now.

Actually, I wanted to visualize the results as well like moving images, warped moving images, optical flow etc. For that I ran eval.py file and I added the keys as mentioned by you in eval.py file like keys = ['pt_mask', 'landmark_dists', 'jaccs', 'dices', 'jacobian_det','real_flow','image_fixed', 'warped_moving']

But in end I couldn't see any images anywhere, just the .txt file of evaluation results in evaluate folder. Could you please let me know what other changes do I have to make to visualize the results.

[zsyzzsoft]

You need to parse the validation results of the added keys manually as in these lines.

[Shubham0209]

Can you please help me with the code snippet of how to do it for visualizing the optical flow, and from it I can follow and do the same for visualizing the warped image etc.

Thank you

[zsyzzsoft]

You may follow these lines as an example and copy the related functions to eval.py

[Shubham0209]

I used the following lines in eval.py to praise the validation results.

            im_flow = RenderFlow(results['real_flow'][0])
            skimage.io.imsave(os.path.join('flow.png'), im_flow)

            warped_img = results['warped_moving'][0]
            show_image(warped_img, os.path.join('warped.png'))

            fixed_img = results['image_fixed'][3]
            show_image(fixed_img, os.path.join('fixed.png'))

            moving_img = results['image_moving'][3]
            show_image(moving_img, os.path.join('moving.png'))

And I got the following images:

Moving Image

Optical flow

Warped image

Fixed image

I don't think that they are accurate results, since the moving and fixed image appear to be exactly same. And moreover the summary of results after running the eval.py file is as follows:

Summary Dice score: nan (nan) Jacc score: 0.0 (0.0) Landmark distance: 0.0 (0.0) Jacobian determinant: 0.1843898892402649 (2.9802322387695312e-08) - (It came out to be same for every image pair).

Could you please help me!!

[Shubham0209]

@zsyzzsoft What does warped_moving_0, warped_moving_1 etc and real_flow_0, real_flow_1 etc indicate?

Why my moving image (i.e results['image_moving']) and fixed image (i.e results['image_fixed']) ?

Why my warped moving (results['warped_moving']) and results['warped_moving'_1] also same ?

PS: I have run train.py and eval.py on my own data of size 28828896.

[zsyzzsoft]

Looks like there is something wrong with your dataset config.

[Shubham0209]

I have pairs of moving and fixed images (lets say 68_mov and 68_fix) and I also have their respective segmentation masks (68_mov_seg and 68_fix_seg). To create a validation .h5 file, I have created a group 68_mov and in it I have 2 members or datasets: 'segmentation' corresponding to 68_mov_seg and 'volume' corresponding to 68_mov. This is followed by group 68_fix and in it I have 2 members or datasets: 'segmentation' corresponding to 68_fix_seg and 'volume' corresponding to 68_fix. And since I have to perform pairwise matching I run the command python eval.py -c weights/Mar14-0215 -g 1 --paired. Please correct if I am wrong somewhere.

Still I get output as

d068_fix_img.nii d068_mov_img.nii nan 0.0 0.0 0.16778272
d068_mov_img.nii d068_fix_img.nii nan 0.0 0.0 0.16778272
d070_fix_img.nii d070_mov_img.nii nan 0.0 0.0 0.16778272
d070_mov_img.nii d070_fix_img.nii nan 0.0 0.0 0.16778272
d071_fix_img.nii d071_mov_img.nii nan 0.0 0.0 0.16778272
d071_mov_img.nii d071_fix_img.nii nan 0.0 0.0 0.16778272

Moreover, as my dataset siye is 28828896 you told me this 'The network assumes that input resolution is a multiple of 64. In your case, you may remove the 6-th level layers (conv6, conv6_1, pred6, and upsamp6to5) in the network as a workaround.' and hence I removed these parts from network/base_networks.py. Below is code from base_networks.py

class VTN(Network):
    def __init__(self, name, flow_multiplier=1., channels=16, **kwargs):
        super().__init__(name, **kwargs)
        self.flow_multiplier = flow_multiplier
        self.channels = channels

    def build(self, img1, img2):
        '''
            img1, img2, flow : tensor of shape [batch, X, Y, Z, C]
        '''
        concatImgs = tf.concat([img1, img2], 4, 'concatImgs')

        dims = 3
        c = self.channels
        conv1 = convolveLeakyReLU('conv1',   concatImgs, c,   3, 2)  # 64 * 64 * 64
        conv2 = convolveLeakyReLU('conv2',   conv1,      c*2,   3, 2)  # 32 * 32 * 32
        conv3 = convolveLeakyReLU('conv3',   conv2,      c*4,   3, 2)
        conv3_1 = convolveLeakyReLU('conv3_1', conv3,      c*4,   3, 1)
        conv4 = convolveLeakyReLU('conv4',   conv3_1,    c*8,  3, 2)  # 16 * 16 * 16
        conv4_1 = convolveLeakyReLU('conv4_1', conv4,      c*8,  3, 1)
        conv5 = convolveLeakyReLU('conv5',   conv4_1,    c*16,  3, 2)  # 8 * 8 * 8
        conv5_1 = convolveLeakyReLU('conv5_1', conv5,      c*16,  3, 1)
        # conv6 = convolveLeakyReLU('conv6',   conv5_1,    c*32,  3, 2)  # 4 * 4 * 4
        # conv6_1 = convolveLeakyReLU('conv6_1', conv6,      c*32,  3, 1)
        # # 16 * 32 = 512 channels

        shape0 = concatImgs.shape.as_list()
        shape1 = conv1.shape.as_list()
        shape2 = conv2.shape.as_list()
        shape3 = conv3.shape.as_list()
        shape4 = conv4.shape.as_list()
        shape5 = conv5.shape.as_list()
        #shape6 = conv6.shape.as_list()

        #pred6 = convolve('pred6', conv6_1, dims, 3, 1)
        #upsamp6to5 = upconvolve('upsamp6to5', pred6, dims, 4, 2, shape5[1:4])
        # deconv5 = upconvolveLeakyReLU('deconv5', conv6_1, shape5[4], 4, 2, shape5[1:4])
        # concat5 = tf.concat([conv5_1, deconv5, upsamp6to5], 4, 'concat5')

        pred5=convolve('pred5', conv5_1, dims, 3, 1)
        upsamp5to4 = upconvolve('upsamp5to4', pred5, dims, 4, 2, shape4[1:4])
        deconv4 = upconvolveLeakyReLU('deconv4', conv5_1, shape4[4], 4, 2, shape4[1:4])
        concat4 = tf.concat([conv4_1, deconv4, upsamp5to4], 4, 'concat4')  # channel = 512+256+2

        # pred5 = convolve('pred5', concat5, dims, 3, 1)
        # upsamp5to4 = upconvolve('upsamp5to4', pred5, dims, 4, 2, shape4[1:4])
        # deconv4 = upconvolveLeakyReLU('deconv4', concat5, shape4[4], 4, 2, shape4[1:4])
        # concat4 = tf.concat([conv4_1, deconv4, upsamp5to4], 4, 'concat4')  # channel = 512+256+2

        pred4 = convolve('pred4', concat4, dims, 3, 1)
        upsamp4to3 = upconvolve('upsamp4to3', pred4, dims, 4, 2, shape3[1:4])
        deconv3 = upconvolveLeakyReLU('deconv3', concat4, shape3[4], 4, 2, shape3[1:4])
        concat3 = tf.concat([conv3_1, deconv3, upsamp4to3],4, 'concat3')  # channel = 256+128+2

        pred3 = convolve('pred3', concat3, dims, 3, 1)
        upsamp3to2 = upconvolve('upsamp3to2', pred3, dims, 4, 2, shape2[1:4])
        deconv2 = upconvolveLeakyReLU('deconv2', concat3, shape2[4], 4, 2, shape2[1:4])
        concat2 = tf.concat([conv2, deconv2, upsamp3to2],4, 'concat2')  # channel = 128+64+2

        pred2 = convolve('pred2', concat2, dims, 3, 1)
        upsamp2to1 = upconvolve('upsamp2to1', pred2, dims, 4, 2, shape1[1:4])
        deconv1 = upconvolveLeakyReLU('deconv1', concat2, shape1[4], 4, 2, shape1[1:4])
        concat1 = tf.concat([conv1, deconv1, upsamp2to1], 4, 'concat1')
        pred0 = upconvolve('upsamp1to0', concat1, dims, 4, 2, shape0[1:4])

        return {'flow': pred0 * 20 * self.flow_multiplier}

You have been of great help to me. Please help again!!