Back to Cosmos

README

code/artificial_intelligence/src/image_processing/Cell-Segmentation/README.md

latest2.5 KB
Original Source
<h2> Cell Segmentation <a href="https://colab.research.google.com/github/Curovearth/Cell-Segmentation-and-Denoising/blob/main/Cell_Segmentation_.ipynb"></a></h2> <p>The study aims to determine a solution for the automatic segmentation and localization of cells. I have tried to utilise UNet++ architecture over UNet to detect cell nuclei and perform segmentation into individual objects.</p>

Image sets and experiment description

  • Dataset which was used was extracted from the mentioned repo data: <a href="https://github.com/Subham2901/Nuclei-Cell-segmentaion/tree/master/Data">Shubham2901/Nuclei Cell Segmentation</a>
DatasetImagesInput SizeModalityProvider
Cell Nuclei67096x96microscopyKaggle Data Science Bowl 2018
<p align=center>

<samp>Block diagram for the overall process</samp>

</p>

Results Obtained

<p>TP, FP, TN and FN are the numbers of true positive, false positive, true negative and false negative detections.</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
PrecisionRecallF-1 ScoreSupport
False0.980.990.985707370
True0.910.850.88846230
Accuracy0.976553600
Macro Avg0.950.920.936553600
Weighted Avg0.970.970.976553600

Final Result

<p align=center> </p>

Future of Cell Segmentation

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.