Each of our tested neural networks came packaged with a pre-trained model using the Kaggle stage 1 dataset. We evaluated the performance of these broadly trained nuclei detection models on our super-resolution images by calculating the F1-Score of the segmentation results. Images used for segmentation included our STORM images of nuclei from human colon tissue and cell line test sets (original 5120 × 5120 image size downsized to 512 × 512). The F1-Scores were calculated at an IoU threshold of 0.7, using Caicedo’s method [21]. Additionally, we tested the effect of resizing by testing the Kaggle models on the same datasets downsized to 256 × 256. To evaluate whether converting the super-resolution images into their lower-resolution versions improve the performance of nuclei segmentation, we also tested the models on blurred and histogram equalized versions of our 256 × 256 test sets.

Note: The content above has been extracted from a research article, so it may not display correctly.

Please log in to submit your questions online.
Your question will be posted on the Bio-101 website. We will send your questions to the authors of this protocol and Bio-protocol community members who are experienced with this method. you will be informed using the email address associated with your Bio-protocol account.

We use cookies on this site to enhance your user experience. By using our website, you are agreeing to allow the storage of cookies on your computer.