Validation and tuning of in situ transcriptomics image processing workflows with crowdsourced annotations
Fig 3
Images are filtered and subdivided so that they are easier for workers to annotate.
(A) The raw image (which contains 268 spots) is pre-processed with a gaussian high pass filter, a Laplace filter, and a maximum intensity projection over z. (B) Crowded spots detected and bound. Rough, first-pass spot-calling enables the detection of crowded spots and subsequent automatic subdivision into smaller images. (C) True positive = consensus in concordance with expert annotation, false positive = consensus not in concordance with expert annotation, and false negative = no consensus found for an expert annotation. The distance between a correct consensus annotation and the nearest expert annotation is no more than the user-defined correctness threshold. The distance between a detected expert annotation and the nearest consensus annotation is also no more than the user-defined correctness threshold. (D) Applying cropping resulted in precision and recall of 97% and 87%, improvements of 50% and 64%, respectively, over the un-cropped image.