The code in https://www.kaggle.com/code/metric/surface-dice-metric/notebook has 2 modes: 2d and 3d.
\nCan I assume the 3d variant is used? Something similar to this:
\n solution = pd(os())\n solution] = solution(,n=,expand=True)\n\n score = surface_dice(solution, submission, ,, , ,)\nThe code in https://www.kaggle.com/code/metric/surface-dice-metric/notebook has 2 modes: 2d and 3d.
\nCan I assume the 3d variant is used? Something similar to this:
\n solution = pd(os())\n solution] = solution(,n=,expand=True)\n\n score = surface_dice(solution, submission, ,, , ,)\nSurface Dice Metric is often used to assess the accuracy of medical image segmentation tasks, especially in 3D images such as CT or MRI scans. This metric measures the similarity between the 3D segmented surface predicted by the model and the real surface.
\nIn the Kaggle Notebook link you mentioned, the Surface Dice indicator offers both 2D and 3D modes. If a dataset contains sequences of 3D images (e.g., a series of MRI slices), calculating the Surface Dice metric in 3D mode takes into account the spatial relationships between successive slices, providing a more comprehensive evaluation of the prediction quality in 3D space.
\nIn the code snippet you provided, the surface_dice.score function appears to be used to calculate the score, and it accepts parameters such as \"id\" (to identify a single 3D image or patient scan) and \"rle\" (Run-length Encoding, a compression format used to represent a segmentation mask). Other parameters like \"group\" and \"slice\" are used to organize 2D slices into 3D images.
\nThe choice of using the 3D variant of the Surface Dice metric depends on whether your task requires the evaluation of the prediction surface in 3D space. If your task involves 3D image data, and you need to assess the accuracy of the predicted 3D structure, then using the 3D variant is appropriate.
\nWhen implementing this metric, ensure that your predictions and true labels are both organized in the same way. They should arrange the slices in the same order and direction. Proper alignment and sorting are crucial for calculating the 3D Surface Dice indicator, as any confusion in the slice order can result in invalid indicators.
", "rawMarkdown": "Surface Dice Metric is often used to assess the accuracy of medical image segmentation tasks, especially in 3D images such as CT or MRI scans. This metric measures the similarity between the 3D segmented surface predicted by the model and the real surface.\n\nIn the Kaggle Notebook link you mentioned, the Surface Dice indicator offers both 2D and 3D modes. If a dataset contains sequences of 3D images (e.g., a series of MRI slices), calculating the Surface Dice metric in 3D mode takes into account the spatial relationships between successive slices, providing a more comprehensive evaluation of the prediction quality in 3D space.\n\nIn the code snippet you provided, the surface_dice.score function appears to be used to calculate the score, and it accepts parameters such as \"id\" (to identify a single 3D image or patient scan) and \"rle\" (Run-length Encoding, a compression format used to represent a segmentation mask). Other parameters like \"group\" and \"slice\" are used to organize 2D slices into 3D images.\n\nThe choice of using the 3D variant of the Surface Dice metric depends on whether your task requires the evaluation of the prediction surface in 3D space. If your task involves 3D image data, and you need to assess the accuracy of the predicted 3D structure, then using the 3D variant is appropriate.\n\nWhen implementing this metric, ensure that your predictions and true labels are both organized in the same way. They should arrange the slices in the same order and direction. Proper alignment and sorting are crucial for calculating the 3D Surface Dice indicator, as any confusion in the slice order can result in invalid indicators.", "votes": null }, { "id": "2564529", "postDate": "12/17/2023 07:32:35", "content": "I mean: specifically for this contest, is the surface dice called in a way that it calculates the 3d score, as in called with the parameters image_id_column_name and slice_id_column_name set.
Thanks ChatGPT
", "rawMarkdown": "Thanks ChatGPT", "votes": null }, { "id": "2566395", "postDate": "12/18/2023 18:04:25", "content": "Yes, 3d variant is used.
", "rawMarkdown": "Yes, 3d variant is used.", "votes": null }, { "id": "2567570", "postDate": "12/19/2023 18:35:17", "content": "Thanks for the feedback!
", "rawMarkdown": "Thanks for the feedback!", "votes": null } ], "comments": [ { "id": 2564332, "author_name": "", "author_url": "", "post_date": "12/17/2023 03:23:32", "content": "Surface Dice Metric is often used to assess the accuracy of medical image segmentation tasks, especially in 3D images such as CT or MRI scans. This metric measures the similarity between the 3D segmented surface predicted by the model and the real surface.
\nIn the Kaggle Notebook link you mentioned, the Surface Dice indicator offers both 2D and 3D modes. If a dataset contains sequences of 3D images (e.g., a series of MRI slices), calculating the Surface Dice metric in 3D mode takes into account the spatial relationships between successive slices, providing a more comprehensive evaluation of the prediction quality in 3D space.
\nIn the code snippet you provided, the surface_dice.score function appears to be used to calculate the score, and it accepts parameters such as \"id\" (to identify a single 3D image or patient scan) and \"rle\" (Run-length Encoding, a compression format used to represent a segmentation mask). Other parameters like \"group\" and \"slice\" are used to organize 2D slices into 3D images.
\nThe choice of using the 3D variant of the Surface Dice metric depends on whether your task requires the evaluation of the prediction surface in 3D space. If your task involves 3D image data, and you need to assess the accuracy of the predicted 3D structure, then using the 3D variant is appropriate.
\nWhen implementing this metric, ensure that your predictions and true labels are both organized in the same way. They should arrange the slices in the same order and direction. Proper alignment and sorting are crucial for calculating the 3D Surface Dice indicator, as any confusion in the slice order can result in invalid indicators.
", "votes": null, "replies": [ { "id": 2564529, "author_name": "limitz", "author_url": "", "post_date": "12/17/2023 07:32:35", "content": "I mean: specifically for this contest, is the surface dice called in a way that it calculates the 3d score, as in called with the parameters image_id_column_name and slice_id_column_name set.
Thanks ChatGPT
", "votes": null, "replies": [] } ] }, { "id": 2566395, "author_name": "yashvrdnjain", "author_url": "", "post_date": "12/18/2023 18:04:25", "content": "Yes, 3d variant is used.
", "votes": null, "replies": [ { "id": 2567570, "author_name": "limitz", "author_url": "", "post_date": "12/19/2023 18:35:17", "content": "Thanks for the feedback!
", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "2564247": "The code in https://www.kaggle.com/code/metric/surface-dice-metric/notebook has 2 modes: 2d and 3d. \n\nCan I assume the 3d variant is used? Something similar to this:\n\n```\n solution = pd.read_csv(os.path.join(\"test_rles.csv\"))\n solution[[\"group\",\"slice\"]] = solution[\"id\"].str.rsplit(\"_\",n=1,expand=True)\n \n score = surface_dice.score(solution, submission, \"id\",\"rle\", 0.0, \"group\",\"slice\")\n```", "2564332": "Surface Dice Metric is often used to assess the accuracy of medical image segmentation tasks, especially in 3D images such as CT or MRI scans. This metric measures the similarity between the 3D segmented surface predicted by the model and the real surface.\n\nIn the Kaggle Notebook link you mentioned, the Surface Dice indicator offers both 2D and 3D modes. If a dataset contains sequences of 3D images (e.g., a series of MRI slices), calculating the Surface Dice metric in 3D mode takes into account the spatial relationships between successive slices, providing a more comprehensive evaluation of the prediction quality in 3D space.\n\nIn the code snippet you provided, the surface_dice.score function appears to be used to calculate the score, and it accepts parameters such as \"id\" (to identify a single 3D image or patient scan) and \"rle\" (Run-length Encoding, a compression format used to represent a segmentation mask). Other parameters like \"group\" and \"slice\" are used to organize 2D slices into 3D images.\n\nThe choice of using the 3D variant of the Surface Dice metric depends on whether your task requires the evaluation of the prediction surface in 3D space. If your task involves 3D image data, and you need to assess the accuracy of the predicted 3D structure, then using the 3D variant is appropriate.\n\nWhen implementing this metric, ensure that your predictions and true labels are both organized in the same way. They should arrange the slices in the same order and direction. Proper alignment and sorting are crucial for calculating the 3D Surface Dice indicator, as any confusion in the slice order can result in invalid indicators.", "2564529": "I mean: specifically for this contest, is the surface dice called in a way that it calculates the 3d score, as in called with the parameters `image_id_column_name` and `slice_id_column_name` set.", "2564560": "Thanks ChatGPT", "2566395": "Yes, 3d variant is used.", "2567570": "Thanks for the feedback!" }, "source": "meta" }