{"id":3323,"date":"2019-05-18T13:23:34","date_gmt":"2019-05-18T17:23:34","guid":{"rendered":"https:\/\/www.med.unc.edu\/bigs2\/?page_id=3323&preview_id=3323"},"modified":"2020-10-13T17:55:10","modified_gmt":"2020-10-13T21:55:10","slug":"hecd","status":"publish","type":"page","link":"https:\/\/www.med.unc.edu\/bigs2\/hecd\/","title":{"rendered":"HECD: Pipeline for H and E image preprocessing and cell detection"},"content":{"rendered":"
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Introduction<\/strong><\/h4>\n
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  1. To enhance the cell detection accuracy, the preprocessing of H&E is necessary and helpful. Usually, the stain normalization is first applied on H and E image, then the blue ratio transformation is done to make the nuclei outstanding. Next, the OTSU thresholding method is adopted to remove the information in the background.<\/li>\n
  2. The key idea for cell detection is the nuclei detection. For the normal cell, the nuclei size is typically invariant across cells (e.g., Lymphocytes). Thus, to detect the nucleus, a moving block method is considered to extract the training\/testing samples and corresponding label information. A bounding box with fixed size screens the whole image. For each patch image, if the nuclei are at the center, the patch image is labeled as positive, otherwise, negative.<\/li>\n
  3. Normally, there is an issue in data unbalance. To address it, the data augmentation (e.g., flip, rotation, and add noise) is considered to increase the number of positive samples.<\/li>\n
  4. Different neural networks are considered here: deep CNN (VGG16), ResNet, CapsNet, and convolutional autoencoder.<\/li>\n<\/ol>\n

    Pipeline<\/strong><\/h4>\n

    All the codes are in the folder named code<\/p>\n

    1. Stain normalization<\/h5>\n

    Go to the folder stain_normalisation_toolbox. Run the MATLAB function run-all.m. You need to change the data folder path in the function according to you case.<\/p>\n

    2. Deep CNN<\/h5>\n

    Go to the folder nuclei_norm.
    \nFirst, run the python code data_norm.py, which will preprocess the normalized images to extract the patch images and corresponding labels. The augmentation will be called if necessary. You need to change the data folder path in the function according to you case.
    \nSecond, run the python code train_norm.py, the model will be trained and the performance on the testing dataset will be assessed as well.
    \nThe shell scripts for running on server are available within the same folder.<\/p>\n

    3. ResNet<\/h5>\n

    Go to the folder nuclei_resnet.<\/p>\n

    First, run the python code data_norm.py, which will preprocess the normalized images to extract the patch images and corresponding labels. The augmentation will be called if necessary. You need to change the data folder path in the function according to you case.<\/p>\n

    Second, run the python code train_resnet.py, the ResNet will be trained and the performance on the testing dataset will be assessed as well.
    \nThe shell scripts for running on server are available within the same folder.<\/p>\n

    4. CapsNet<\/h5>\n

    Go to the folder CapsNet-Keras.<\/p>\n

    First, run the python code data.py, which will preprocess the normalized images to extract the patch images and corresponding labels. The augmentation will be called if necessary. You need to change the data folder path in the function according to you case.<\/p>\n

    Second, run the python code capsulenet.py, the CapsNet will be trained and the performance on the testing dataset will be assessed as well.
    \nThe shell scripts for running on server are available within the same folder.<\/p>\n

    5. Convolutional autoencoder (CAE)<\/h5>\n

    Go to the folder crcnucleus.<\/p>\n

    Run the python code test_xcae_crcnucleus.py, the CAE will be trained and the performance on the testing dataset will be assessed as well. You need to change the data folder path in the function according to you case.<\/p>\n

    Key words:<\/strong> H and E; CNN.<\/p>\n

     <\/p>\n

    Software Download<\/a><\/h4>\n<\/div>\n<\/div>\n<\/div>\n
    \n

     <\/p>\n<\/div>\n<\/div>\n

    <\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

    Introduction To enhance the cell detection accuracy, the preprocessing of H&E is necessary and helpful. Usually, the stain normalization is first applied on H and E image, then the blue ratio transformation is done to make the nuclei outstanding. Next, the OTSU thresholding method is adopted to remove the information in the background. The key … Read more<\/a><\/p>\n","protected":false},"author":55348,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-3323","page","type-page","status-publish","hentry","odd"],"acf":[],"yoast_head":"\nHECD: Pipeline for H and E image preprocessing and cell detection - BIG-S2<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"HECD: Pipeline for H and E image preprocessing and cell detection - BIG-S2\" \/>\n<meta property=\"og:description\" content=\"Introduction To enhance the cell detection accuracy, the preprocessing of H&E is necessary and helpful. 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