code/artificial_intelligence/src/image_processing/Cell-Segmentation/README.md
| Dataset | Images | Input Size | Modality | Provider |
|---|---|---|---|---|
| Cell Nuclei | 670 | 96x96 | microscopy | Kaggle Data Science Bowl 2018 |
<samp>Block diagram for the overall process</samp>
</p>| <b>Precision Score</b> | 0.91 |
| <b>Recall Score</b> | 0.85 |
| <b>F-1 Score</b> | 0.88 |
| <b>Sensitivity</b> | 0.85 |
| <b>Specificity</b> | 0.99 |
| <b>IOU</b> | 0.79 |
| <b>AUC</b> | 0.92 |
| Precision | Recall | F-1 Score | Support | |
|---|---|---|---|---|
| False | 0.98 | 0.99 | 0.98 | 5707370 |
| True | 0.91 | 0.85 | 0.88 | 846230 |
| Accuracy | 0.97 | 6553600 | ||
| Macro Avg | 0.95 | 0.92 | 0.93 | 6553600 |
| Weighted Avg | 0.97 | 0.97 | 0.97 | 6553600 |
With the rise of size and complexity of cell images, the requirements for cells segmentation methods are also increasing. The basic image processing algo developed decades ago should not be the golden standards for dealing with these challenging cell segmentation problems any more.
On the contrary, development of more effective image processing algorithms is more promising for the progress of cell segmentation. In the meantime, comparison of these newly developed algorithms and teaching the biologists to use these newly developed algorithms are also very important. Hence, the open access and authoritative platforms are necessary for researchers all over the world to share, learn, and teach the data, codes, and algorithms.