Effect of Learning Parameters on the Performance of the U-Net Architecture for Cell Nuclei Segmentation from Microscopic Cell Images.

Nuclei segmentation of cells is the preliminary and essential step of pathological image analysis. However, robust and accurate cell nuclei segmentation is challenging due to the enormous variability of staining, cell sizes, morphologies, cell adhesion, or overlapping of the nucleus. The automation process to find the cell's nuclei is a giant leap in this direction and have an important step toward bioimage analysis employing software tools. This article extensively analyzes deep U-Net architecture and has been applied to the Data Science Bowl dataset to segment the cell nuclei. The dataset undergoes various pre-processing tasks such as resizing, intensity normalization, and data augmentation prior to segmentation. The complete dataset then undergoes rigorous training and validation process to find the optimized hyperparameters and then the optimized model selection. The mean (m) ± standard deviation (sd) of Intersection over Union (IOU), and F1-Score (Dice score) have been calculated along with Accuracy during the training and validation process, respectively. The optimized U-net model results in training IOU of 0.94 ± 0.16 (m ± s), F1-score of 0.94 ± 0.17 (m ± s), training accuracy of 95.54, and validation accuracy of 95.45. With that model, we have applied a completely independent test cohort of the dataset and obtained the mean IOU of 0.93, F1-score of 0.9311, and mean Accuracy of 94.12, respectively.