File size: 16,973 Bytes
db2c651 e7c5a0b 2f9aa77 e7c5a0b db2c651 e7c5a0b a6aa8b1 e7c5a0b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-nc-3.0
multilinguality:
- monolingual
paperswithcode_id: null
pretty_name: Images of Cervical Cells with AgNOR Stain Technique
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- image-segmentation
- object-detection
task_ids:
- semantic-segmentation
- instance-segmentation
---
# Dataset Card for Images of Cervical Cells with AgNOR Stain Technique
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [CCAgT homepage](https://data.mendeley.com/datasets/wg4bpm33hj/)
- **Repository:** [CCAgT-utils](https://github.com/johnnv1/CCAgT-utils)
- **Paper:** [Semantic Segmentation for the Detection of Very Small Objects on Cervical Cell Samples Stained with the AgNOR Technique](https://dx.doi.org/10.2139/ssrn.4126881)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [João G. A. Amorim](mailto:joao.atkinson@posgrad.ufsc.br)
### Dataset Summary
The CCAgT (Images of Cervical Cells with AgNOR Stain Technique) dataset contains 9339 images (1600x1200 resolution where each pixel is 0.111µmX0.111µm) from 15 different slides stained using the AgNOR technique. Each image has at least one label. In total, this dataset has more than 63K instances of annotated object. The images are from the patients of the Gynecology and Colonoscopy Outpatient Clinic of the [Polydoro Ernani de São Thiago University Hospital of the Universidade Federal de Santa Catarina (HU-UFSC)](https://unihospital.ufsc.br/).
### Supported Tasks and Leaderboards
- `image-segmentation`: The dataset can be used to train a model for semantic segmentation or instance segmentation. Semantic segmentation consists in classifying each pixel of the image. Success on this task is typically measured by achieving high values of [mean iou](https://huggingface.co/spaces/evaluate-metric/mean_iou) or [f-score](https://huggingface.co/spaces/evaluate-metric/f1) for pixels results. Instance segmentation consists of doing object detection first and then using a semantic segmentation model inside detected objects. For instances results, this task is typically measured by achieving high values of [recall](https://huggingface.co/spaces/evaluate-metric/recall), [precision](https://huggingface.co/spaces/evaluate-metric/precision) and [f-score](https://huggingface.co/spaces/evaluate-metric/f1).
- `object-detection`: The dataset can be used to train a model for object detection to detect the nuclei categories or the nucleolus organizer regions (NORs), which consists of locating instances of objects and then classifying each one. This task is typically measured by achieving a high values of [recall](https://huggingface.co/spaces/evaluate-metric/recall), [precision](https://huggingface.co/spaces/evaluate-metric/precision) and [f-score](https://huggingface.co/spaces/evaluate-metric/f1).
### Languages
The class labels in the dataset are in English.
## Dataset Structure
### Data Instances
An example looks like the one below:
#### `semantic segmentation` (default configuration)
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1200x1600 at 0x276021C5EB8>,
'annotation': <PIL.PngImagePlugin.PngImageFile image mode=L size=1200x1600 at 0x385021C5ED7>
}
```
#### `object detection`
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1200x1600 at 0x276021C5EB8>,
'objects': {
'bbox': [
[36, 7, 13, 32],
[50, 7, 12, 32]
],
'label': [1, 5]
}
```
#### `instance segmentation`
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1200x1600 at 0x276021C5EB8>,
'objects': {
'bbox': [
[13.3, 7.5, 47.6, 38.3],
[10.2, 7.5, 50.7, 38.3]
],
'segment': [
[[36.2, 7.5, 13.3, 32.1, 52.1, 40.6, 60.9, 45.8, 50.1, 40, 40, 33.2, 35.2]],
[[10.2, 7.5, 10.3, 32.1, 52.1, 40.6, 60.9, 45.8, 50.1, 40, 40, 33.2, 35.2]],
],
'label': [1, 5]
}
```
### Data Fields
The data annotations have the following fields:
#### `semantic segmentation` (default configuration)
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `annotation`: A `PIL.Image.Image` object containing the annotation mask. The mask has a single channel and the following pixel values are possible: `BACKGROUND` (0), `NUCLEUS` (1), `CLUSTER` (2), `SATELLITE` (3), `NUCLEUS_OUT_OF_FOCUS` (4), `OVERLAPPED_NUCLEI` (5), `NON_VIABLE_NUCLEUS` (6) and `LEUKOCYTE_NUCLEUS` (7).
#### `object detection`
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `objects`: a dictionary containing bounding boxes and labels of the cell objects
- `bbox`: a list of bounding boxes (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) corresponding to the objects present on the image
- `label`: a list of integers representing the category (7 categories to describe the objects in total; two to differentiate nucleolus organizer regions), with the possible values including `NUCLEUS` (0), `CLUSTER` (1), `SATELLITE` (2), `NUCLEUS_OUT_OF_FOCUS` (3), `OVERLAPPED_NUCLEI` (4), `NON_VIABLE_NUCLEUS` (5) and `LEUKOCYTE_NUCLEUS` (6).
#### `instance segmentation`
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `objects`: a dictionary containing bounding boxes and labels of the cell objects
- `bbox`: a list of bounding boxes (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) corresponding to the objects present on the image
- `segment`: a list of segments in format of `[polygon_0, ..., polygon_n]`, where each polygon is `[x0, y0, ..., xn, yn]`.
- `label`: a list of integers representing the category (7 categories to describe the objects in total; two to differentiate nucleolus organizer regions), with the possible values including `NUCLEUS` (0), `CLUSTER` (1), `SATELLITE` (2), `NUCLEUS_OUT_OF_FOCUS` (3), `OVERLAPPED_NUCLEI` (4), `NON_VIABLE_NUCLEUS` (5) and `LEUKOCYTE_NUCLEUS` (6).
### Data Splits
The data is split randomly using the fixed seed into training, test and validation set. The training data contains 70% of the images and the testing and the validation data contain 15% of the images each. In total, the training set contains 6533 images and the testing and the validation set 1403 images each.
<details>
<summary>
Click here to see additional statistics:
</summary>
| Slide id | Diagnostics | images | annotations | NUCLEUS | CLUSTER | SATELLITE | NUCLEUS_OUT_OF_FOCUS | OVERLAPPED_NUCLEI | NON_VIABLE_NUCLEUS | LEUKOCYTE_NUCLEUS |
| :-------: | :---------: | :----: | :---------: | :-----: | :------: | :-------: | :------------------: | :---------------: | :---------------: | :-------: |
| A | CIN 3 | 1311 | 3164 | 763 | 1038 | 922 | 381 | 46 | 14 | 0 |
| B | SCC | 561 | 911 | 224 | 307 | 112 | 132 | 5 | 1 | 130 |
| C | AC | 385 | 11420 | 2420 | 3584 | 1112 | 1692 | 228 | 477 | 1907 |
| D | CIN 3 | 2125 | 1258 | 233 | 337 | 107 | 149 | 12 | 8 | 412 |
| E | CIN 3 | 506 | 11131 | 2611 | 6249 | 1648 | 476 | 113 | 34 | 0 |
| F | CIN 1 | 318 | 3365 | 954 | 1406 | 204 | 354 | 51 | 326 | 70 |
| G | CIN 2 | 249 | 2759 | 691 | 1279 | 336 | 268 | 49 | 51 | 85 |
| H | CIN 2 | 650 | 5216 | 993 | 983 | 425 | 2562 | 38 | 214 | 1 |
| I | No lesion | 309 | 474 | 56 | 55 | 19 | 170 | 2 | 23 | 149 |
| J | CIN 1 | 261 | 1786 | 355 | 304 | 174 | 743 | 18 | 33 | 159 |
| K | No lesion | 1503 | 13102 | 2464 | 6669 | 638 | 620 | 670 | 138 | 1903 |
| L | CIN 2 | 396 | 3289 | 842 | 796 | 387 | 1209 | 27 | 23 | 5 |
| M | CIN 2 | 254 | 1500 | 357 | 752 | 99 | 245 | 16 | 12 | 19 |
| N | CIN 3 | 248 | 911 | 258 | 402 | 67 | 136 | 10 | 6 | 32 |
| O | AC | 262 | 2904 | 792 | 1549 | 228 | 133 | 88 | 52 | 62 |
| **Total** | - | 9339 | 63190 | 14013 | 25710 | 6478 | 9270 | 1373 | 1412 | 4934 |
Lesion types:
- Cervical intraepithelial neoplasia 1 - CIN 1
- Cervical intraepithelial neoplasia 2 - CIN 2
- Cervical intraepithelial neoplasia 3 - CIN 3
- Squamous cell carcinoma - SCC
- Adenocarcinoma - AC
- No lesion
</details>
## Dataset Creation
### Curation Rationale
CCAgT was built to provide a dataset for machines to learn how to identify nucleus and nucleolus organizer regions (NORs).
### Source Data
#### Initial Data Collection and Normalization
The images are collected as patches/tiles of whole slide images (WSIs) from cervical samples stained with AgNOR technique to allow the detection of nucleolus organizer regions (NORs). NORs are DNA loops containing genes responsible for the transcription of ribosomal RNA located in the cell nucleolus. They contain a set of argyrophilic proteins, selectively stained by silver nitrate, which can be identified as black dots located throughout the nucleoli area and called AgNORs.
#### Who are the source language producers?
The dataset was built using images from examinations (a gynecological exam, colposcopy and biopsy) of 15 women patients who were treated at the Gynecology and Colposcopy Outpatient Clinic of the [University Hospital Professor Polydoro Ernani de São Thiago of Federal University of Santa Catarina (HU-UFSC)](https://unihospital.ufsc.br/) and had 6 different diagnoses in their oncological exams. The samples were collected by the members of the Clinical Analyses Department: Ane Francyne Costa, Fabiana Botelho De Miranda Onofre, and Alexandre Sherlley Casimiro Onofre.
### Annotations
#### Annotation process
The instances were annotated using the [labelbox](https://labelbox.com/) tool. The satellite category was labeled as a single dot, and the other categories were labeled as polygons. After the annotation process, all annotations were reviewed.
#### Who are the annotators?
Members of the Clinical Analyses Department and the Image Processing and Computer Graphics Lab. — LAPiX from [Universidade Federal de Santa Catarina (UFSC)](https://en.ufsc.br/).
- Tainee Bottamedi
- Vinícius Sanches
- João H. Telles de Carvalho
- Ricardo Thisted
### Personal and Sensitive Information
This research was approved by the UFSC Research Ethics Committee (CEPSH), protocol number 57423616.3.0000.0121. All involved patients were informed about the study's objectives, and those who agreed to participate signed an informed consent form.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset's purpose is to help spread the AgNOR as a support method for cancer diagnosis since this method is not standardized among pathologists.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Satellite annotation is not as accurate for pixel-level representation due to single-point annotations.
## Additional Information
### Dataset Curators
Members of the Clinical Analyses Department from [Universidade Federal de Santa Catarina (UFSC)](https://en.ufsc.br/) collected the dataset samples: Ane Francyne Costa, Fabiana Botelho De Miranda Onofre, and Alexandre Sherlley Casimiro Onofre.
### Licensing Information
The files associated with this dataset are licensed under an [Attribution-NonCommercial 3.0 Unported](https://creativecommons.org/licenses/by-nc/3.0/) license.
Users are free to adapt, copy or redistribute the material as long as they attribute it appropriately and do not use it for commercial purposes.
### Citation Information
```bibtex
% Dataset oficial page
@misc{CCAgTDataset,
doi = {10.17632/WG4BPM33HJ.2},
url = {https://data.mendeley.com/datasets/wg4bpm33hj/2},
author = {Jo{\~{a}}o Gustavo Atkinson Amorim and Andr{\'{e}} Vict{\'{o}}ria Matias and Tainee Bottamedi and Vin{\'{i}}us Sanches and Ane Francyne Costa and Fabiana Botelho De Miranda Onofre and Alexandre Sherlley Casimiro Onofre and Aldo von Wangenheim},
title = {CCAgT: Images of Cervical Cells with AgNOR Stain Technique},
publisher = {Mendeley},
year = {2022},
copyright = {Attribution-NonCommercial 3.0 Unported}
}
% Dataset second version
% pre-print:
@article{AtkinsonAmorim2022,
doi = {10.2139/ssrn.4126881},
url = {https://doi.org/10.2139/ssrn.4126881},
year = {2022},
publisher = {Elsevier {BV}},
author = {Jo{\~{a}}o Gustavo Atkinson Amorim and Andr{\'{e}} Vict{\'{o}}ria Matias and Allan Cerentini and Fabiana Botelho de Miranda Onofre and Alexandre Sherlley Casimiro Onofre and Aldo von Wangenheim},
title = {Semantic Segmentation for the Detection of Very Small Objects on Cervical Cell Samples Stained with the {AgNOR} Technique},
journal = {{SSRN} Electronic Journal}
}
% Dataset first version
% Link: https://arquivos.ufsc.br/d/373be2177a33426a9e6c/
% Paper:
@inproceedings{AtkinsonSegmentationAgNORCBMS2020,
author={Jo{\~{a}}o Gustavo Atkinson Amorim and Luiz Antonio Buschetto Macarini and Andr{\'{e}} Vict{\'{o}}ria Matias and Allan Cerentini and Fabiana Botelho De Miranda Onofre and Alexandre Sherlley Casimiro Onofre and Aldo von Wangenheim},
booktitle={2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)},
title={A Novel Approach on Segmentation of AgNOR-Stained Cytology Images Using Deep Learning},
year={2020},
pages={552-557},
doi={10.1109/CBMS49503.2020.00110},
url={https://doi.org/10.1109/CBMS49503.2020.00110}
}
```
### Contributions
Thanks to [@johnnv1](https://github.com/johnnv1) for adding this dataset. |