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--- |
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license: gpl-3.0 |
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language: |
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- en |
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tags: |
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- medical |
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- segmentation |
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- gcn |
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pretty_name: CCA |
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size_categories: |
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- n<1K |
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--- |
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# Coronary Artery Dataset (CCA) |
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This dataset, named the CCA dataset, consists of 200 cases of CTA images depicting coronary artery disease. |
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Out of these cases, 20 were allocated for training purposes, while the remaining 180 cases were reserved for testing. |
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We have made our training dataset publicly available for further use. |
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The collected images are acquired with an isotropic resolution of 0.5 mm. |
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Four radiologists participated in annotating the coronary artery internal diameter of 200 cases as ground truth, |
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that each case was independently labelled by three radiologists and the remaining radiologist selected |
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the best one among three annotations. |
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The cases were shuffled randomly and organized into a queue. |
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Once a radiologist labelled a case, they were assigned the subsequent case based on the random order |
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of the queue. This process guaranteed that each case received labels from three distinct radiologists. |
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Importantly, each radiologist remained unaware of the labels assigned by the other two radiologists, |
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ensuring the masking of the labels during the assessment. After three radiologists have finished |
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labeling, the remaining radiologist examines and selects the best one of three labels, |
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completing the combining of the radiologists’ decisions. |
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If you use this dataset, please cite: |
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```bibtex |
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@misc{yang2023segmentation, |
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title={Segmentation and Vascular Vectorization for Coronary Artery by Geometry-based Cascaded Neural Network}, |
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author={Xiaoyu Yang and Lijian Xu and Simon Yu and Qing Xia and Hongsheng Li and Shaoting Zhang}, |
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year={2023}, |
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eprint={2305.04208}, |
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archivePrefix={arXiv} |
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} |
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``` |