Datasets
The datasets we use to validate are presented in the table below.
Dice Similarity Coefficient
For the performance measurement, we use the Dice Similarity Coefficient. Specifically, in our case, we visualize the ground truth, mask prediction, and overlap in three different colors. The yellow part of the image below is the overlap between the ground truth and prediction, the green part is the ground truth only, and the red part is the mask prediction only.
Below is the formula for the Dice Similarity Coefficient. We can replace X with the prediction mask and Y with the ground truth. The maximum score is 1 for a perfect overlap, whereas the lowest score we can get is 0 for no overlap.
Dice Similarity Coefficient [3]
Results
Citations
[1] Orlando, J. I., Fu, H., Breda, J. B., Van Keer, K., Bathula, D. R., Zheng, Y., … & Trucco, E. (2020). REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Medical Image Analysis, 59, 101570. https://doi.org/10.1016/j.media.2019.101570
[2] Kuş, Z., & Aydin, M. (2024). MedSegBench: A comprehensive benchmark for medical image segmentation in diverse data modalities. Scientific Data, 11, 1283. https://doi.org/10.1038/s41597-024-04159-2
[3] Swerdlow, M., Guler, Ö., Yaakov, R., & Armstrong, D. G. (2023). Simultaneous segmentation and classification of pressure injury image data using Mask-R-CNN. Computational and Mathematical Methods in Medicine, 2023, Article ID 3858997. https://doi.org/10.1155/2023/3858997