Datasets:

Modalities:
Text
Formats:
json
Languages:
English
Size:
< 1K
ArXiv:
Libraries:
Datasets
pandas
License:
MedHK23 commited on
Commit
ea5d7f2
·
verified ·
1 Parent(s): 89102f3

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +7 -2
README.md CHANGED
@@ -11,14 +11,19 @@ size_categories:
11
  - n<1K
12
  ---
13
 
 
 
 
 
14
 
15
  cite:
16
 
 
17
  @misc{yang2023segmentation,
18
  title={Segmentation and Vascular Vectorization for Coronary Artery by Geometry-based Cascaded Neural Network},
19
  author={Xiaoyu Yang and Lijian Xu and Simon Yu and Qing Xia and Hongsheng Li and Shaoting Zhang},
20
  year={2023},
21
  eprint={2305.04208},
22
- archivePrefix={arXiv},
23
- primaryClass={id='eess.IV' full_name='Image and Video Processing' is_active=True alt_name=None in_archive='eess' is_general=False description='Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.'}
24
  }
 
 
11
  - n<1K
12
  ---
13
 
14
+ # Coronary Artery Dataset (CCA)
15
+
16
+
17
+ This dataset collects many cases of CTA images with coronary artery disease named the CCA dataset.
18
 
19
  cite:
20
 
21
+ ```bibtex
22
  @misc{yang2023segmentation,
23
  title={Segmentation and Vascular Vectorization for Coronary Artery by Geometry-based Cascaded Neural Network},
24
  author={Xiaoyu Yang and Lijian Xu and Simon Yu and Qing Xia and Hongsheng Li and Shaoting Zhang},
25
  year={2023},
26
  eprint={2305.04208},
27
+ archivePrefix={arXiv}
 
28
  }
29
+ ```