NuSeT: A deep learning tool for reliably separating and analyzing crowded cells
Fig 2
Improved normalization performance by foreground normalization and synthetic training.
(A) The visual effects of normalizing sparse/dense samples using whole-image normalization showing images having inconsistent nuclear signals after normalization. (B) Foreground normalization during training and testing. During training, only pixels belonging to cell nuclei are used to normalize the image. During testing, a coarse segmentation prediction is generated by the model, and pixels belonging to the predicted nuclei are used to perform foreground normalization. The model then makes final predictions based on the normalized input images. (C) Distribution of pixel intensities over an entire training dataset after different normalizations, showing foreground normalization has wider dynamic range. (D) The visual effects of normalizing sparse/dense samples using foreground normalization showing images have a higher dynamic range and more consistent nuclear signals. (E, F) Line charts showing that the object-level performance (E) and the pixel-level performance (F) of the foreground normalization model depend on the pixel-level performance of the whole-image normalization model. Error bars represent three individual experiments. (G) Examples of synthetic images with labels used during training. Our algorithm can generate synthetic nuclei-shaped blobs with different sizes, as well as different types of artifacts to increase the robustness of the model. Overlapping nuclei were introduced to enhance NuSeT performance in touching nuclei separation. (H) Representative examples comparing the performances of different segmentation approaches. Training without synthetic images mis-identified artifacts (stripes) as foreground. The addition of synthetic data improved artifact detection. Switching to foreground normalization led the best performance including robust identification of imaging artifact, detected of more nuclei, and better separation of touching nuclei compared to Mask R-CNN and U-Net.