Tissuenet dataset instance segmentation ground truth about deepcell-tf HOT 11 CLOSED

Yes, we included images that are blurry, out of focus, partially cropped, etc. This helps the model generalize better

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Hi @tsly123, the reason that the ground truth images have dimensions is because one set is for the whole-cell labels, and the other is for the nuclear labels. Each of these represent instance ground truth labels of the dataset.

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Thank you for your response.

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Hi,

I'm able to extract the instance masks. However, when I check the dataset Tissuenet 1.1. The test set has some broken samples. I visualized them using the code in Tissuenet README.
The broken sample's ID:
test_err = [ 68, 69, 286, 463, 504, 506, 510, 511, 529, 545, 547, 681, 737, 757, 955, 1017, 1247]

import os

import numpy as np
import skimage.io as io

from deepcell.utils.plot_utils import create_rgb_image
from deepcell.utils.plot_utils import make_outline_overlay

npz_dir = 'path_to_dir_with_NPZs'
test_dict = np.load(os.path.join(npz_dir, 'tissuenet_v1.1_test.npz'))

test_X, test_y = test_dict['X'], test_dict['y']
rgb_images = create_rgb_image(selected_X, channel_colors=['green', 'blue'])
cv2.imwrite(save_path, rgb_images[68]) # 68, see ID above

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Thanks for the prompt and detailed response @ngreenwald !

With the original question addressed, I will go ahead and close this as there's nothing actionable here (aside from maybe documenting the data format in greater detail - doc suggestions welcome!)

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Yes, we included images that are blurry, out of focus, partially cropped, etc. This helps the model generalize better

Can you make the original image (without pre-processing) pubically avaliable? Or can you please send me a private link to download it: [email protected]?

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Hi @anthonyweidai ,

I think what you downloaded in version 1.1 are the raw images.

See this topic
#618 (comment)

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Hi @ngreenwald and @rossbar ,

I print the meta of Tissuenet 1.1 and it looks like this:

from deepcell.datasets import TissueNet

tissuenet = TissueNet(version='1.1')
X_val, y_val, meta_val = tissuenet.load_data(split='test')

                                               filename                                         experiment  pixel_size  screening_passed  time_step               specimen
0                                              filename                                         experiment  pixel_size  screening_passed  time_step               specimen
1                                              filename                                         experiment  pixel_size  screening_passed  time_step               specimen
2                                              filename                                         experiment  pixel_size  screening_passed  time_step               specimen
3     ../../labels/static/2d/Tissue-Spleen/20200424_...  ../../labels/static/2d/Tissue-Spleen/20200424_...         0.5      Not screened       None                 Spleen
4     ../../labels/static/2d/Tissue-Spleen/20200424_...  ../../labels/static/2d/Tissue-Spleen/20200424_...         0.5      Not screened       None                 Spleen
...                                                 ...                                                ...         ...               ...        ...                    ...
1322  ../../labels/static/2d/Tissue-Lung/20200210_Cy...  ../../labels/static/2d/Tissue-Lung/20200210_Cy...         0.5      Not screened       None  lymph node metastasis
1323  ../../labels/static/2d/Tissue-Lung/20200424_TB...  ../../labels/static/2d/Tissue-Lung/20200424_TB...         0.5      Not screened       None                   Lung
1324  ../../labels/static/2d/Tissue-Lung/20200424_TB...  ../../labels/static/2d/Tissue-Lung/20200424_TB...         0.5      Not screened       None                   Lung
1325  ../../labels/static/2d/Tissue-Lung/20200424_TB...  ../../labels/static/2d/Tissue-Lung/20200424_TB...         0.5      Not screened       None                   Lung
1326  ../../labels/static/2d/Tissue-Lung/20200424_TB...  ../../labels/static/2d/Tissue-Lung/20200424_TB...         0.5      Not screened       None                   Lun

It have 4 header rows. So I assume that each row after 4 header rows correspond to the image in X_val and y_val. In other words, the order of images in X_val and y_val and rows after 4 header rows, i.e. meta[4:], is matched. Is this correct?
I ask this because I want to get the label specimen for instance segmentation.

In addition, can I take the specimen as the label for each image?

Could you kindly comment on these 2 questions?
Thank you very much.

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I believe so, but I think some of these additional columns are to make TissueNet compatible with all the other datasets being generated, so I'm not 100% sure

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Hi @anthonyweidai ,

I think what you downloaded in version 1.1 are the raw images.

See this topic #618 (comment)

Then, who is right? The questioner said there are broken samples. And @ngreenwald said they pre-processed some images. BTW, the questioner said the downloaded Tissuenet version is also 1.1.

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Hi @anthonyweidai ,
I think what you downloaded in version 1.1 are the raw images.
See this topic #618 (comment)

Then, who is right? The questioner said there are broken samples. And @ngreenwald said they pre-processed some images. BTW, the questioner said the downloaded Tissuenet version is also 1.1.

I got it. Thanks. These images are not actually "raw". But you can still see how they look like.

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