{ "id": 268597, "title": "Notebook to run BraTS Pre-processing in Kaggle kernel", "url": "/competitions/rsna-miccai-brain-tumor-radiogenomic-classification/discussion/268597", "author_name": "", "post_date": "2021-08-28T02:32:59.665084700Z", "votes": 15, "comment_count": 6, "views": 0, "content": "

Hi All,
\nThe BraTS preprocessing pipeline is described here:
\nhttps://cbica.github.io/CaPTk/preprocessing_brats.html

\n

I have created a Notebook to run BraTS Pre-processing in Kaggle kernel:
\nhttps://www.kaggle.com/mpsampat/running-brats-pre-processing-pipeline

\n

In this notebook the following steps are down (from the website above)

\n
    \n
  1. Installation of the BraTS Pre-processing binary:
    2. \n
  2. The following steps are run:
    3. \n
  3. Re-orientation to LPS/RAI
    4. \n
  4. Image registration to SRI-24 Atlas [4] which includes the following steps
    5. \n
  5. N4 Bias correction (This is a TEMPORARY STEP, and is not applied in the final co-registered output images. It is only use to facilitate optimal registration.)
    6. \n
  6. Rigid Registration of T1, T2, FLAIR to T1CE
    7. \n
  7. Rigid Registration of T1CE to SRI-24 atlas
    8. \n
  8. Applying transformation to the reoriented images
    9. \n
  9. All the images are normalized to this template:
    10. \n
  10. https://www.nitrc.org/projects/sri24/
    11. \n
", "messages": [ { "id": "1493528", "postDate": "08/28/2021 02:32:59", "content": "

Hi All,
\nThe BraTS preprocessing pipeline is described here:
\nhttps://cbica.github.io/CaPTk/preprocessing_brats.html

\n

I have created a Notebook to run BraTS Pre-processing in Kaggle kernel:
\nhttps://www.kaggle.com/mpsampat/running-brats-pre-processing-pipeline

\n

In this notebook the following steps are down (from the website above)

\n
    \n
  1. Installation of the BraTS Pre-processing binary:
    2. \n
  2. The following steps are run:
    3. \n
  3. Re-orientation to LPS/RAI
    4. \n
  4. Image registration to SRI-24 Atlas [4] which includes the following steps
    5. \n
  5. N4 Bias correction (This is a TEMPORARY STEP, and is not applied in the final co-registered output images. It is only use to facilitate optimal registration.)
    6. \n
  6. Rigid Registration of T1, T2, FLAIR to T1CE
    7. \n
  7. Rigid Registration of T1CE to SRI-24 atlas
    8. \n
  8. Applying transformation to the reoriented images
    9. \n
  9. All the images are normalized to this template:
    10. \n
  10. https://www.nitrc.org/projects/sri24/
    11. \n
", "rawMarkdown": "Hi All, \nThe BraTS preprocessing pipeline is described here: \nhttps://cbica.github.io/CaPTk/preprocessing_brats.html\n\nI have created a Notebook to run BraTS Pre-processing in Kaggle kernel: \nhttps://www.kaggle.com/mpsampat/running-brats-pre-processing-pipeline\n\nIn this notebook the following steps are down (from the website above)\n1. Installation of the BraTS Pre-processing binary: \n2. The following steps are run: \n3. Re-orientation to LPS/RAI\n4. Image registration to SRI-24 Atlas [4] which includes the following steps\n5. N4 Bias correction (This is a TEMPORARY STEP, and is not applied in the final co-registered output images. It is only use to facilitate optimal registration.)\n6. Rigid Registration of T1, T2, FLAIR to T1CE\n7. Rigid Registration of T1CE to SRI-24 atlas\n8. Applying transformation to the reoriented images\n9. All the images are normalized to this template: \n10. https://www.nitrc.org/projects/sri24/", "votes": null }, { "id": "1493529", "postDate": "08/28/2021 02:35:24", "content": "

This is the list and explanation of the final co-registered images:

\n
    \n
  1. T1_to_SRI.nii.gz : Co-registered T1 image
    2. \n
  2. T1CE_to_SRI.nii.gz: Co-registered T1CE image
    3. \n
  3. T2_to_SRI.nii.gz: Co-registered T2 image
    4. \n
  4. FL_to_SRI.nii.gz: Co-registered FLAIR image
    5. \n
", "rawMarkdown": "This is the list and explanation of the final co-registered images:\n\n1. T1_to_SRI.nii.gz : Co-registered T1 image\n2. T1CE_to_SRI.nii.gz: Co-registered T1CE image\n3. T2_to_SRI.nii.gz: Co-registered T2 image\n4. FL_to_SRI.nii.gz: Co-registered FLAIR image", "votes": null }, { "id": "1493531", "postDate": "08/28/2021 02:36:20", "content": "

I still need to check if these steps can be run on the full test-set during inference without timing out.
\nIf anyone has ideas to speed things up (for example with parallel processing) please do share in the comments.

", "rawMarkdown": "I still need to check if these steps can be run on the full test-set during inference without timing out. \nIf anyone has ideas to speed things up (for example with parallel processing) please do share in the comments.", "votes": null }, { "id": "1493566", "postDate": "08/28/2021 03:19:55", "content": "

Hello, thank you for your work. The input dataset in your notebook is private. Can you make it public?

", "rawMarkdown": "Hello, thank you for your work. The input dataset in your notebook is private. Can you make it public?", "votes": null }, { "id": "1493575", "postDate": "08/28/2021 03:36:59", "content": "

@zzy990106 I just made it public. can you try again please ?

", "rawMarkdown": "zzy990106 I just made it public. can you try again please ?", "votes": null }, { "id": "1493687", "postDate": "08/28/2021 05:44:23", "content": "

It's available now, thank you very much

", "rawMarkdown": "It's available now, thank you very much", "votes": null }, { "id": "2295155", "postDate": "06/10/2023 16:23:49", "content": "

Thank you for the pre-processing pipeline. This is one component missing in my segmentation project. This will greatly help me!

", "rawMarkdown": "Thank you for the pre-processing pipeline. This is one component missing in my segmentation project. This will greatly help me!", "votes": null } ], "comments": [ { "id": 1493529, "author_name": "mpsampat", "author_url": "", "post_date": "08/28/2021 02:35:24", "content": "

This is the list and explanation of the final co-registered images:

\n
    \n
  1. T1_to_SRI.nii.gz : Co-registered T1 image
    2. \n
  2. T1CE_to_SRI.nii.gz: Co-registered T1CE image
    3. \n
  3. T2_to_SRI.nii.gz: Co-registered T2 image
    4. \n
  4. FL_to_SRI.nii.gz: Co-registered FLAIR image
    5. \n
", "votes": null, "replies": [] }, { "id": 1493531, "author_name": "mpsampat", "author_url": "", "post_date": "08/28/2021 02:36:20", "content": "

I still need to check if these steps can be run on the full test-set during inference without timing out.
\nIf anyone has ideas to speed things up (for example with parallel processing) please do share in the comments.

", "votes": null, "replies": [] }, { "id": 1493566, "author_name": "zzy990106", "author_url": "", "post_date": "08/28/2021 03:19:55", "content": "

Hello, thank you for your work. The input dataset in your notebook is private. Can you make it public?

", "votes": null, "replies": [ { "id": 1493575, "author_name": "mpsampat", "author_url": "", "post_date": "08/28/2021 03:36:59", "content": "

@zzy990106 I just made it public. can you try again please ?

", "votes": null, "replies": [] }, { "id": 1493687, "author_name": "zzy990106", "author_url": "", "post_date": "08/28/2021 05:44:23", "content": "

It's available now, thank you very much

", "votes": null, "replies": [] } ] }, { "id": 2295155, "author_name": "sandeshkatakam", "author_url": "", "post_date": "06/10/2023 16:23:49", "content": "

Thank you for the pre-processing pipeline. This is one component missing in my segmentation project. This will greatly help me!

", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "1493528": "Hi All, \nThe BraTS preprocessing pipeline is described here: \nhttps://cbica.github.io/CaPTk/preprocessing_brats.html\n\nI have created a Notebook to run BraTS Pre-processing in Kaggle kernel: \nhttps://www.kaggle.com/mpsampat/running-brats-pre-processing-pipeline\n\nIn this notebook the following steps are down (from the website above)\n1. Installation of the BraTS Pre-processing binary: \n2. The following steps are run: \n3. Re-orientation to LPS/RAI\n4. Image registration to SRI-24 Atlas [4] which includes the following steps\n5. N4 Bias correction (This is a TEMPORARY STEP, and is not applied in the final co-registered output images. It is only use to facilitate optimal registration.)\n6. Rigid Registration of T1, T2, FLAIR to T1CE\n7. Rigid Registration of T1CE to SRI-24 atlas\n8. Applying transformation to the reoriented images\n9. All the images are normalized to this template: \n10. https://www.nitrc.org/projects/sri24/", "1493529": "This is the list and explanation of the final co-registered images:\n\n1. T1_to_SRI.nii.gz : Co-registered T1 image\n2. T1CE_to_SRI.nii.gz: Co-registered T1CE image\n3. T2_to_SRI.nii.gz: Co-registered T2 image\n4. FL_to_SRI.nii.gz: Co-registered FLAIR image", "1493531": "I still need to check if these steps can be run on the full test-set during inference without timing out. \nIf anyone has ideas to speed things up (for example with parallel processing) please do share in the comments.", "1493566": "Hello, thank you for your work. The input dataset in your notebook is private. Can you make it public?", "1493575": "zzy990106 I just made it public. can you try again please ?", "1493687": "It's available now, thank you very much", "2295155": "Thank you for the pre-processing pipeline. This is one component missing in my segmentation project. This will greatly help me!" }, "source": "meta" }