## 3 Results
### Impact of Compression Artifacts Removal on Nuclei Segmentation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Segmentation** & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Mean**} & \multicolumn{3}{c}{**Median**} \\ \cline{3-8}  & & & \(\Delta DICE\) & \(\Delta AJI\) & \(\Delta PQ\) & \(\Delta DICE\) & \(\Delta AJI\) & \(\Delta PQ\) \\ \hline \multirow{8}{*}{**CPM-17**} & **Hover-Net** & **EPIIC** & \(0.0002\) & \(-0.0030\) & \(0.0005\) & \(0.0018\) & \(0.0018\) & \(0.0105\) \\  & **EPIIC Sobel** & \(-0.0010\) & \(0.0045\) & \(0.0056\) & \(0.0013\) & \(0.0068\) & \(0.0072\) \\  & **Hover-Net** & **FBCNN** & \(-0.0054\) & \(-0.0044\) & \(0.0016\) & \(-0.0029\) & \(-0.0001\) & \(-0.0024\) \\  & **Gaussian Filter** & \(-0.0052\) & \(-0.0026\) & \(-0.0026\) & \(-0.0020\) & \(0.0014\) & \(-0.0005\) \\  & **Guided Filter** & \(-0.0069\) & \(-0.0100\) & \(-0.0058\) & \(-0.0003\) & \(-0.0025\) & \(-0.0024\) \\ \cline{2-8}  & **STARDIST** & **EPIIC** & \(0.0026\) & \(0.0087\) & \(0.0169\) & \(0.0061\) & \(0.0140\) & \(0.0207\) \\  & **EPIIC Sobel** & \(0.0038\) & \(0.0135\) & \(0.0217\) & \(0.0048\) & \(0.0144\) & \(0.0251\) \\  & **FBCNN** & \(-0.0018\) & \(-0.0045\) & \(0.0025\) & \(-0.0018\) & \(-0.0056\) & \(0.0014\) \\  & **Gaussian Filter** & \(-0.0058\) & \(-0.0083\) & \(-0.0053\) & \(-0.0036\) & \(-0.0062\) & \(-0.0043\) \\  & **Guided Filter** & \(0.0075\) & \(0.0089\) & \(0.0082\) & \(0.0080\) & \(0.0119\) & \(0.0105\) \\ \hline \multirow{8}{*}{**Kumar**} & **Hover-Net** & **EPIIC** & \(0.0066\) & \(0.0028\) & \(0.0234\) & \(0.0034\) & \(0.0006\) & \(0.0254\) \\  & **EPIIC Sobel** & \(0.0008\) & \(-0.0073\) & \(0.0155\) & \(0.0013\) & \(-0.0011\) & \(0.0231\) \\  & **FBCNN** & \(0.0007\) & \(-0.0068\) & \(0.0124\) & \(-0.0058\) & \(-0.0119\) & \(0.0106\) \\  & **Gaussian Filter** & \(0.0036\) & \(-0.0085\) & \(0.0172\) & \(0.0000\) & \(-0.0055\) & \(0.0229\) \\  & **Guided Filter** & \(0.0063\) & \(-0.0007\) & \(0.0110\) & \(0.0034\) & \(0.0022\) & \(0.0135\) \\ \cline{2-8}  & **EPIIC** & \(0.0068\) & \(0.0043\) & \(0.0020\) & \(0.0107\) & \(0.0009\) & \(0.0003\) \\  & **EPIIC Sobel** & \(0.0044\) & \(-0.0020\) & \(0.0060\) & \(0.0067\) & \(-0.0035\) & \(0.0050\) \\  & **FBCNN** & \(-0.0185\) & \(-0.0071\) & \(-0.0194\) & \(-0.0051\) & \(0.0016\) & \(-0.0146\) \\  & **Gaussian Filter** & \(-0.0130\) & \(-0.0078\) & \(-0.0041\) & \(-0.0041\) & \(-0.0056\) & \(-0.0088\) \\  & **Guided Filter** & \(-0.0236\) & \(-0.0217\) & \(-0.0252\) & \(-0.0136\) & \(-0.0183\) & \(-0.0244\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The table presents the performance of competing methods on the Kumar and CPM-17 datasets for both segmentation methods. The best results are underlined. Figure 6: Box plots highlighting the performance of competing methods on the CPM-17 and Kumar datasets for the Hover-Net segmentation algorithm. The graphs show the differences between the results obtained for the model without correction and the models after removing compression artifacts. The black line indicates the value of zero for a better interpretation. Figure 7: Box plots highlighting the performance of competing methods on the CPM-17 and Kumar datasets for the STARDIST segmentation algorithm. The graphs show the differences between the results obtained for the model without correction and the models after removing compression artifacts. The black line indicates the value of zero for a better interpretation. Figure 8: Example visual results on the Kumar and CPM-17 datasets with Hover-Net models. The ground truth is presented on the left side at the intersection of the rows. In the first row, the black lines indicate the segmented nuclear instances predicted by each model. For better visualization, we included prediction maps for each image in the second row. The green color indicates TP regions, blue FN, and red FP.