The FIVES dataset comprises 800 high-resolution, multi-disease color fundus photographs with pixel-wise manual annotations. These annotations were standardized through a crowdsourcing effort among medical experts. Each image was evaluated for quality based on factors like illumination, color distortion, blur, and contrast.
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Access the dataset: Download from FIVES: A Fundus Image Dataset for AI-based Vessel Segmentation.
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Prepare your data: Arrange the images and masks in the same folder, structured as follows:
${SvANet_ROOT}
|-- dataset
`-- |-- FIVES
`-- |--- train
|--- 1_A_train.jpg
|--- 2_A_train.jpg
|--- ...
|--- 216_D_train.jpg
|--- ...
|--- test
|--- 1_A_test.png
|--- 2_A_train.png
|--- ...
|--- 166_N_test.png
|--- ...
|--- mask
|--- 1_A_test.png
|--- 1_A_train.png
|--- ...
|--- 216_D_train.png
|--- ...
This dataset includes skin lesion images with corresponding binary masks.
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Access the dataset: Download from ISIC Challenge Datasets.
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Prepare your data: Arrange the images and masks in the same folder, structured as follows:
${SvANet_ROOT}
|-- dataset
`-- |-- ISIC2018
`-- |--- train
|--- ISIC_0000000.jpg
|--- ISIC_0000001.jpg
|--- ...
|--- ISIC_0013319.jpg
|--- ...
|--- val
|--- ISIC_0012255.jpg
|--- ISIC_0012346.jpg
|--- ...
|--- ISIC_0036291.jpg
|--- ...
|--- mask
|--- ISIC_0000000.png
|--- ISIC_0000001.png
|--- ...
|--- ISIC_0012346.png
|--- ...
PolypGen includes 8,037 frames from various hospitals, featuring both single and sequence frames with 3,762 positive and 4,275 negative samples.
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Access the dataset: Download from PolypGen dataset.
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Prepare your data: Arrange the images and masks in the same folder, structured as follows:
${SvANet_ROOT}
|-- dataset
`-- |-- PolypGen
`-- |--- train
|--- 4_endocv2021_positive_34.jpg
|--- 4_endocv2021_positive_954.jpg
|--- ...
|--- EndoCV2021_001114.jpg
|--- ...
|--- val
|--- EndoCV2021_C6_0100018.jpg
|--- EndoCV2021_C6_0100013.jpg
|--- ...
|--- C3_EndoCV2021_00162.jpg
|--- ...
|--- mask
|--- 4_endocv2021_positive_34.png
|--- 4_endocv2021_positive_954.png
|--- ...
|--- EndoCV2021_001114.png
|--- ...
This dataset includes 90 T1 CE-MRI scans of the liver, segmented into liver and tumor masks.
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Access the dataset: Download from A Tumor and Liver Automatic Segmentation.
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Slice into 2D sequences: Use niftiFileExtractor.py to slice MRI scans into 2D sequences.
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Prepare your data: Arrange the images and masks in the same folder, structured as follows:
${SvANet_ROOT}
|-- dataset
`-- |-- ATLAS
`-- |--- train
|--- im1.nii_0.jpg
|--- im1.nii_12.jpg
|--- ...
|--- im38.nii_12.jpg
|--- ...
|--- val
|--- im0.nii_0.jpg
|--- im0.nii_4.jpg
|--- ...
|--- im59.nii_28.jpg
|--- ...
|--- mask
|--- im0.nii_0.png
|--- im0.nii_4.png
|--- ...
|--- im38.nii_12.png
|--- ...
The KiTS23 challenge dataset for kidney and tumor segmentation.
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Access the dataset: Download from KiTS23 Dataset.
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Slice into 2D sequences: Use niftiFileExtractor.py to slice CT scans into 2D sequences.
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Prepare your data: Arrange the images and masks in the same folder, structured as follows:
${SvANet_ROOT}
|-- dataset
`-- |-- KiTS23
`-- |--- train
|--- case_00000_132.jpg
|--- case_00000_203.jpg
|--- ...
|--- case_00272_247.jpg
|--- ...
|--- val
|--- case_00009_28.jpg
|--- case_00009_37.jpg
|--- ...
|--- case_00514_441.jpg
|--- ...
|--- mask
|--- case_00000_132.png
|--- case_00000_203.png
|--- ...
|--- case_00514_441.png
|--- ...
The data of the TissueNet dataset is tissue excision data with corresponding binary masks.
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Access the dataset: Download from DeepCell Datasets.
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Prepare your data: Arrange the images and masks in the same folder, structured as follows:
${SvANet_ROOT}
|-- dataset
`-- |-- TissueNet
`-- |--- train
|--- 20191121_MIBI_whole_cell_breast_train_346_345.jpg
|--- 20191121_MIBI_whole_cell_breast_train_347_346.jpg
|--- ...
|--- 20200919_CODEX_CRC_train_947_946.jpg
|--- ...
|--- test
|--- 20191121_MIBI_whole_cell_breast_test_184_183.jpg
|--- 20191121_MIBI_whole_cell_breast_test_185_184.jpg
|--- ...
|--- 20200924_CyCIF_Lung_LN_test_1253_1252.jpg
|--- ...
|--- mask
|--- 20191121_MIBI_whole_cell_breast_test_184_183.png
|--- 20191121_MIBI_whole_cell_breast_test_185_184.png
|--- ...
|--- 20200924_CyCIF_Lung_LN_test_1253_1252.png
|--- ...
If you use the datasets and our data pre-processing codes, we kindly request that you consider citing our paper as follows:
@article{jin2022fives,
title={{FIVES}: A fundus image dataset for artificial Intelligence based vessel segmentation},
author={Jin, Kai and Huang, Xingru and Zhou, Jingxing and Li, Yunxiang and Yan, Yan and Sun, Yibao and Zhang, Qianni and Wang, Yaqi and Ye, Juan},
journal={Scientific Data},
volume={9},
number={1},
pages={475},
year={2022},
publisher={Nature Publishing Group UK London}
}
@article{codella2019skin,
title={Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration ({ISIC})},
author={Codella, Noel and Rotemberg, Veronica and Tschandl, Philipp and Celebi, M Emre and Dusza, Stephen and Gutman, David and Helba, Brian and Kalloo, Aadi and Liopyris, Konstantinos and Marchetti, Michael and others},
journal={arXiv preprint arXiv:1902.03368},
year={2019}
}
@article{ali2023multi,
title={A multi-centre polyp detection and segmentation dataset for generalisability assessment},
author={Ali, Sharib and Jha, Debesh and Ghatwary, Noha and Realdon, Stefano and Cannizzaro, Renato and Salem, Osama E and Lamarque, Dominique and Daul, Christian and Riegler, Michael A and Anonsen, Kim V and others},
journal={Scientific Data},
volume={10},
number={1},
pages={75},
year={2023},
publisher={Nature Publishing Group UK London}
}
@article{quinton2023tumour,
title={A Tumour and Liver Automatic Segmentation ({ATLAS}) Dataset on Contrast-Enhanced Magnetic Resonance Imaging for Hepatocellular Carcinoma},
author={Quinton, F{\'e}lix and Popoff, Romain and Presles, Beno{\^\i}t and Leclerc, Sarah and Meriaudeau, Fabrice and Nodari, Guillaume and Lopez, Olivier and Pellegrinelli, Julie and Chevallier, Olivier and Ginhac, Dominique and others},
journal={Data},
volume={8},
number={5},
pages={79},
year={2023},
publisher={MDPI}
}
@misc{heller2023kits21,
title={The {KiTS21} challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase {CT}},
author={Nicholas Heller and Fabian Isensee and Dasha Trofimova and others},
year={2023},
eprint={2307.01984},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@article{greenwald2022whole,
title={Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning},
author={Greenwald, Noah F and Miller, Geneva and Moen, Erick and Kong, Alex and Kagel, Adam and Dougherty, Thomas and Fullaway, Christine Camacho and McIntosh, Brianna J and Leow, Ke Xuan and Schwartz, Morgan Sarah and others},
journal={Nature Biotechnology},
volume={40},
number={4},
pages={555--565},
year={2022},
publisher={Nature Publishing Group US New York}
}
@misc{dai2024svanet,
title={Exploiting Scale-Variant Attention for Segmenting Small Medical Objects},
author={Dai, Wei and Liu, Rui and Wu, Zixuan and Wu, Tianyi and Wang, Min and Zhou, Junxian and Yuan, Yixuan and Liu, Jun},
year={2024},
eprint={2407.07720},
archivePrefix={arXiv},
primaryClass={eess.IV},
url={https://arxiv.org/abs/2407.07720},
}