Application of convolutional neural networks towards nuclei segmentation in localization-based super-resolution fluorescence microscopy images

Table 3 Average test accuracy scores for CNNs trained and tested on super-resolution imagery

DataSet		Mask R-CNN			ANCIS			StarDist
Train set	Test set	F1	FN/FP	H	F1	FN/FP	H	F1	FN/FP	H
Colon tissue	Colon	0.793	0.177/0.225	9.76	0.739	0.315/0.122	10.68	0.725	0.253/0.126	10.62
	Prostate	0.646	0.221/0.121	8.82	0.505	0.496/0.118	9.64	0.673	0.357/0.071	9.17
	Cell downsize	0.872	0.053/0.17	5.65	0.601	0.201/0.134	13.21	0.847	0.137/0.105	6.39
Cell Line A (discrete)	Cell line	0.832	0.11/0.3	8.21	0.902	0.101/0.078	7.05	0.799	0.107/0.263	8.87
Cell Line B (all textures)	Cell line	0.831	0.125/0.116	7.91	0.952	0.03/0.128	6.39	0.859	0.076/0.31	8.11
Cell Line B (all textures)	Colon upsize	0.423	0.607/0.424	17.43	0.489	0.551/0.076	14.51	0.39	0.467/0.645	17.06
Combined (Colon & Cell)	Colon	0.676	0.169/0.564	10.59	0.753	0.305/0.11	11.13	0.612	0.396/0.397	11.39
Combined (Colon & Cell)	Cell line	0.885	0.021/0.236	5.54	0.943	0.041/0.107	6.76	0.92	0.037/0.174	5.45
Colon Blur	Colon blur	0.752	0.246/0.25	9.87	0.729	0.349/0.136	11.06	0.658	0.387/0.141	11.02
Colon HEQ	Colon HEQ	0.769	0.183/0.287	10.01	0.733	0.328/0.127	11.17	0.696	0.348/0.123	10.94
Cell A Blur	Cell blur	0.791	0.12/0.333	6.87	0.858	0.112/0.118	6.83	0.761	0.153/0.288	9.22
Cell A HEQ	Cell HEQ	0.81	0.105/0.363	6.64	0.867	0.098/0.126	6.57	0.785	0.14/0.285	8.99

F1-Score (F1), false negative percent and false positive percent (FN/FP), and Hausdorff distance (H) for Mask R-CNN, ANCIS and StarDist network models trained on the STORM colon tissue dataset, and cell line datasets A & B. An additional combined training dataset included downsized cell line dataset A, colon tissue and Kaggle datasets. Testing was conducted on the 512 × 512 colon and prostate tissue test sets as well as on the 512 × 512 cell line test set, downsized (256 × 256) cell line test set and upsized (1024 × 1024) colon test set. Training and testing were also conducted on histogram equalized (HEQ) and blurred (Blur) versions of the colon tissue and cell line A datasets. Pre-processing was conducted on the 512 × 512 versions of the colon tissue and cell line A datasets for both the training and test images. The results indicate that the original data provided the best test accuracy over the pre-processed images for all cases, suggesting no advantage to be gained by these processes. Top results for each test set are indicated by bold numbering

ISSN: 1471-2105