Dataset related to article "A comprehensive pipeline for automatic cyst segmentation and counting on µCT scans from PKD animal models"
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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2026
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| _version_ | 1866901746361565184 |
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| author | Mangili, Andrea Arrigoni, Alberto Sangalli, Fabio Caroli, Anna |
| author_facet | Mangili, Andrea Arrigoni, Alberto Sangalli, Fabio Caroli, Anna |
| contents | <p>The dataset contains two files:</p> <p>1) 1_Segmentation_pipeline.zip, which contains:</p> <ul> <li>Dataset 1 <ul> <li>Training Set <ul> <li>Images: resampled µCT scans used to train the models</li> <li>Ground Truth: semi-automatic annotations of training samples</li> </ul> </li> <li>Test Set <ul> <li>Images: resampled µCT scans used to test the models</li> <li>Ground Truth: semi-automatic annotations of test samples</li> <li>Inferences <ul> <li>n5 Model: predicted mask of D1 test sample by the model trained using 5 samples + summary.json</li> <li>n5 one-channel Model: predicted mask of D1 test sample by the one-channel model trained using 5 samples + summary.json</li> <li>n10 Model: predicted mask of D1 test sample by the model trained using 10 samples + summary.json</li> <li>n15 Model: predicted mask of D1 test sample by the model trained using 15 samples + summary.json</li> <li>n20 Model: predicted mask of D1 test sample by the model trained using 20 samples + summary.json</li> </ul> </li> </ul> </li> </ul> </li> <li>Dataset 2 <ul> <li>Images: resampled µCT scans used to test the models</li> <li>Ground Truth: semi-automatic annotations of test samples</li> <li>Inferences <ul> <li>n5 Model: predicted mask of D1 test sample by the model trained using 5 samples + summary.json</li> <li>n5 one-channel Model: predicted mask of D1 test sample by the one-channel model trained using 5 samples + summary.json</li> <li>n10 Model: predicted mask of D1 test sample by the model trained using 10 samples + summary.json</li> <li>n15 Model: predicted mask of D1 test sample by the model trained using 15 samples + summary.json</li> <li>n20 Model: predicted mask of D1 test sample by the model trained using 20 samples + summary.json</li> </ul> </li> </ul> </li> </ul> <p>2) 2_Cyst_counting_pipeline.zip, which contains:</p> <ul> <li>Optimization set <ul> <li>Cyst masks: input masks (0 background, 1 kidney, 2 cysts) used in the optimization set</li> <li>O1_count.xlsx: Operator 1 manual counts on the Optimization set samples</li> </ul> </li> <li>Evaluation set <ul> <li>Cyst masks: input masks (0 background, 1 kidney, 2 cysts) used in the evaluation set</li> <li>O1_O2_counts.xlsx: Operator 1 and Operator 2 manual counts on the Evaluation set samples</li> </ul> </li> </ul> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19049543 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Dataset related to article "A comprehensive pipeline for automatic cyst segmentation and counting on µCT scans from PKD animal models" Mangili, Andrea Arrigoni, Alberto Sangalli, Fabio Caroli, Anna <p>The dataset contains two files:</p> <p>1) 1_Segmentation_pipeline.zip, which contains:</p> <ul> <li>Dataset 1 <ul> <li>Training Set <ul> <li>Images: resampled µCT scans used to train the models</li> <li>Ground Truth: semi-automatic annotations of training samples</li> </ul> </li> <li>Test Set <ul> <li>Images: resampled µCT scans used to test the models</li> <li>Ground Truth: semi-automatic annotations of test samples</li> <li>Inferences <ul> <li>n5 Model: predicted mask of D1 test sample by the model trained using 5 samples + summary.json</li> <li>n5 one-channel Model: predicted mask of D1 test sample by the one-channel model trained using 5 samples + summary.json</li> <li>n10 Model: predicted mask of D1 test sample by the model trained using 10 samples + summary.json</li> <li>n15 Model: predicted mask of D1 test sample by the model trained using 15 samples + summary.json</li> <li>n20 Model: predicted mask of D1 test sample by the model trained using 20 samples + summary.json</li> </ul> </li> </ul> </li> </ul> </li> <li>Dataset 2 <ul> <li>Images: resampled µCT scans used to test the models</li> <li>Ground Truth: semi-automatic annotations of test samples</li> <li>Inferences <ul> <li>n5 Model: predicted mask of D1 test sample by the model trained using 5 samples + summary.json</li> <li>n5 one-channel Model: predicted mask of D1 test sample by the one-channel model trained using 5 samples + summary.json</li> <li>n10 Model: predicted mask of D1 test sample by the model trained using 10 samples + summary.json</li> <li>n15 Model: predicted mask of D1 test sample by the model trained using 15 samples + summary.json</li> <li>n20 Model: predicted mask of D1 test sample by the model trained using 20 samples + summary.json</li> </ul> </li> </ul> </li> </ul> <p>2) 2_Cyst_counting_pipeline.zip, which contains:</p> <ul> <li>Optimization set <ul> <li>Cyst masks: input masks (0 background, 1 kidney, 2 cysts) used in the optimization set</li> <li>O1_count.xlsx: Operator 1 manual counts on the Optimization set samples</li> </ul> </li> <li>Evaluation set <ul> <li>Cyst masks: input masks (0 background, 1 kidney, 2 cysts) used in the evaluation set</li> <li>O1_O2_counts.xlsx: Operator 1 and Operator 2 manual counts on the Evaluation set samples</li> </ul> </li> </ul> |
| title | Dataset related to article "A comprehensive pipeline for automatic cyst segmentation and counting on µCT scans from PKD animal models" |
| url | https://doi.org/10.5281/zenodo.19049543 |