{
  "id": 342665,
  "title": "What's the meaning of 'pixel_size' and 'tissue_thickness'?",
  "url": "/competitions/hubmap-organ-segmentation/discussion/342665",
  "author_name": "",
  "post_date": "2022-08-08T09:12:09.490122500Z",
  "votes": 5,
  "comment_count": 5,
  "views": 0,
  "content": "<p>I found that there are two information about 'pixel_size' and 'tissue_thickness' in the csv data, but i don't know what's the meaning of these information and how to use them. Would anyone be willing to explain what these information do?<br>\nFor example, if i want to cut out some of the images, do I need to use the 'pixel_size' and 'tissue_thickness' to do some transformations? or i can directly cut out the images? 🙏</p>",
  "messages": [
    {
      "id": "1889553",
      "postDate": "08/08/2022 09:12:09",
      "content": "<p>I found that there are two information about 'pixel_size' and 'tissue_thickness' in the csv data, but i don't know what's the meaning of these information and how to use them. Would anyone be willing to explain what these information do?<br>\nFor example, if i want to cut out some of the images, do I need to use the 'pixel_size' and 'tissue_thickness' to do some transformations? or i can directly cut out the images? 🙏</p>",
      "rawMarkdown": "I found that there are two information about 'pixel_size' and 'tissue_thickness' in the csv data, but i don't know what's the meaning of these information and how to use them. Would anyone be willing to explain what these information do?\nFor example, if i want to cut out some of the images, do I need to use the 'pixel_size' and 'tissue_thickness' to do some transformations? or i can directly cut out the images? 🙏",
      "votes": null
    },
    {
      "id": "1889708",
      "postDate": "08/08/2022 10:49:36",
      "content": "<p>Everything is explained in data section of the competition… Here is explained what you have questioned it. Basicaly you can ignore those info since they are not important IMHO.</p>\n<p>pixel_size - The height/width of a single pixel from this image in micrometers. All HPA images have a pixel size of 0.4 µm. For HuBMAP imagery the pixel size is 0.5 µm for kidney, 0.2290 µm for large intestine, 0.7562 µm for lung, 0.4945 µm for spleen, and 6.263 µm for prostate.</p>\n<p>tissue_thickness - The thickness of the biopsy sample in micrometers. All HPA images have a thickness of 4 µm. The HuBMAP samples have tissue slice thicknesses 10 µm for kidney, 8 µm for large intestine, 4 µm for spleen, 5 µm for lung, and 5 µm for prostate.</p>",
      "rawMarkdown": "Everything is explained in data section of the competition... Here is explained what you have questioned it. Basicaly you can ignore those info since they are not important IMHO.\n\npixel_size - The height/width of a single pixel from this image in micrometers. All HPA images have a pixel size of 0.4 µm. For HuBMAP imagery the pixel size is 0.5 µm for kidney, 0.2290 µm for large intestine, 0.7562 µm for lung, 0.4945 µm for spleen, and 6.263 µm for prostate.\n\ntissue_thickness - The thickness of the biopsy sample in micrometers. All HPA images have a thickness of 4 µm. The HuBMAP samples have tissue slice thicknesses 10 µm for kidney, 8 µm for large intestine, 4 µm for spleen, 5 µm for lung, and 5 µm for prostate.",
      "votes": null
    },
    {
      "id": "1890307",
      "postDate": "08/08/2022 16:49:17",
      "content": "<p>I cannot speak to the magnitude of effect either of these will have on your analysis, but I will explain them a bit for you (beyond the description provided by the competition):</p>\n<p>pixel_size reflects the scale at which the image was taken. A smaller number would mean the image is more zoomed-in whereas a larger number would mean the image is more zoomed-out. If your model is sensitive to scale, differences here could have an impact and you may wish to rescale the images to account for the differences.</p>\n<p>tissue_thickness reflects the physical thickness of the tissue shown in the image. For tissues of the same type, a thicker tissue could mean that more layers of cells are shown. I am unfamiliar with the thickness of these cells, so I am not sure how much of a difference this would make.</p>",
      "rawMarkdown": "I cannot speak to the magnitude of effect either of these will have on your analysis, but I will explain them a bit for you (beyond the description provided by the competition):\n\npixel_size reflects the scale at which the image was taken. A smaller number would mean the image is more zoomed-in whereas a larger number would mean the image is more zoomed-out. If your model is sensitive to scale, differences here could have an impact and you may wish to rescale the images to account for the differences.\n\ntissue_thickness reflects the physical thickness of the tissue shown in the image. For tissues of the same type, a thicker tissue could mean that more layers of cells are shown. I am unfamiliar with the thickness of these cells, so I am not sure how much of a difference this would make.",
      "votes": null
    },
    {
      "id": "1892395",
      "postDate": "08/10/2022 04:47:10",
      "content": "<p>Thanks for your clear reply!</p>",
      "rawMarkdown": "Thanks for your clear reply!",
      "votes": null
    },
    {
      "id": "1892413",
      "postDate": "08/10/2022 04:54:22",
      "content": "<p>thank you for your reply， and i will try to directly cut out these images😃</p>",
      "rawMarkdown": "thank you for your reply， and i will try to directly cut out these images😃",
      "votes": null
    },
    {
      "id": "1892556",
      "postDate": "08/10/2022 06:50:13",
      "content": "<p>If you find replies useful you can upvote them.</p>",
      "rawMarkdown": "If you find replies useful you can upvote them.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1889708,
      "author_name": "urosjarc",
      "author_url": "",
      "post_date": "08/08/2022 10:49:36",
      "content": "<p>Everything is explained in data section of the competition… Here is explained what you have questioned it. Basicaly you can ignore those info since they are not important IMHO.</p>\n<p>pixel_size - The height/width of a single pixel from this image in micrometers. All HPA images have a pixel size of 0.4 µm. For HuBMAP imagery the pixel size is 0.5 µm for kidney, 0.2290 µm for large intestine, 0.7562 µm for lung, 0.4945 µm for spleen, and 6.263 µm for prostate.</p>\n<p>tissue_thickness - The thickness of the biopsy sample in micrometers. All HPA images have a thickness of 4 µm. The HuBMAP samples have tissue slice thicknesses 10 µm for kidney, 8 µm for large intestine, 4 µm for spleen, 5 µm for lung, and 5 µm for prostate.</p>",
      "votes": null,
      "replies": [
        {
          "id": 1892413,
          "author_name": "tanxxx",
          "author_url": "",
          "post_date": "08/10/2022 04:54:22",
          "content": "<p>thank you for your reply， and i will try to directly cut out these images😃</p>",
          "votes": null,
          "replies": []
        },
        {
          "id": 1892556,
          "author_name": "urosjarc",
          "author_url": "",
          "post_date": "08/10/2022 06:50:13",
          "content": "<p>If you find replies useful you can upvote them.</p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 1890307,
      "author_name": "steelejake",
      "author_url": "",
      "post_date": "08/08/2022 16:49:17",
      "content": "<p>I cannot speak to the magnitude of effect either of these will have on your analysis, but I will explain them a bit for you (beyond the description provided by the competition):</p>\n<p>pixel_size reflects the scale at which the image was taken. A smaller number would mean the image is more zoomed-in whereas a larger number would mean the image is more zoomed-out. If your model is sensitive to scale, differences here could have an impact and you may wish to rescale the images to account for the differences.</p>\n<p>tissue_thickness reflects the physical thickness of the tissue shown in the image. For tissues of the same type, a thicker tissue could mean that more layers of cells are shown. I am unfamiliar with the thickness of these cells, so I am not sure how much of a difference this would make.</p>",
      "votes": null,
      "replies": [
        {
          "id": 1892395,
          "author_name": "tanxxx",
          "author_url": "",
          "post_date": "08/10/2022 04:47:10",
          "content": "<p>Thanks for your clear reply!</p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "1889553": "I found that there are two information about 'pixel_size' and 'tissue_thickness' in the csv data, but i don't know what's the meaning of these information and how to use them. Would anyone be willing to explain what these information do?\nFor example, if i want to cut out some of the images, do I need to use the 'pixel_size' and 'tissue_thickness' to do some transformations? or i can directly cut out the images? 🙏",
    "1889708": "Everything is explained in data section of the competition... Here is explained what you have questioned it. Basicaly you can ignore those info since they are not important IMHO.\n\npixel_size - The height/width of a single pixel from this image in micrometers. All HPA images have a pixel size of 0.4 µm. For HuBMAP imagery the pixel size is 0.5 µm for kidney, 0.2290 µm for large intestine, 0.7562 µm for lung, 0.4945 µm for spleen, and 6.263 µm for prostate.\n\ntissue_thickness - The thickness of the biopsy sample in micrometers. All HPA images have a thickness of 4 µm. The HuBMAP samples have tissue slice thicknesses 10 µm for kidney, 8 µm for large intestine, 4 µm for spleen, 5 µm for lung, and 5 µm for prostate.",
    "1890307": "I cannot speak to the magnitude of effect either of these will have on your analysis, but I will explain them a bit for you (beyond the description provided by the competition):\n\npixel_size reflects the scale at which the image was taken. A smaller number would mean the image is more zoomed-in whereas a larger number would mean the image is more zoomed-out. If your model is sensitive to scale, differences here could have an impact and you may wish to rescale the images to account for the differences.\n\ntissue_thickness reflects the physical thickness of the tissue shown in the image. For tissues of the same type, a thicker tissue could mean that more layers of cells are shown. I am unfamiliar with the thickness of these cells, so I am not sure how much of a difference this would make.",
    "1892395": "Thanks for your clear reply!",
    "1892413": "thank you for your reply， and i will try to directly cut out these images😃",
    "1892556": "If you find replies useful you can upvote them."
  },
  "source": "meta"
}