Khater, Ismail M. Aroca-Ouellette, Stephane T. Meng, Fanrui Nabi, Ivan Robert Hamarneh, Ghassan The process of obtaining the class labels for the Cav1 blobs using wide-field CAVIN1/PTRF mask. The class labels are necessary to train the machine learning models to identify the Cav1 blobs types automatically. <p>(A) The first row shows the imaged wide-field TIRF CAVIN1/PTRF mask before and after morphological closing. The morphological closing operation is used to close the small holes in the consecutive regions of CAVIN1/PTRF mask. The CAVIN1/PTRF regions are delineated in yellow to highlight the locations of the CAVIN1/PTRF regions in the cell. (B) The second row shows the Cav1 blobs and the overlay of the Cav1 blobs with the wide-field CAVIN1/PTRF mask to label the blobs into PTRF+ and PTRF-. The caveolae structures have a minimum of 60 Cav1 molecule per blob [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0211659#pone.0211659.ref009" target="_blank">9</a>] that can be used to stratify the PTRF+ blobs into PTRF+≥ 60 and PTRF+< 60. Our goal is to use machine learning approaches to automatically identify the PTRF+≥ 60 blobs (caveolar domains) from the rest of the non-caveolar domains (i.e. PTRF+< 60 and PTRF-) using different features and data representations of the blobs.</p> classification;Caveolae;transcript release factor;molecule localization microscopy;3 D cluster;PC 3-PTRF cells;adaptor protein polymerase;PC 3 prostate cancer cells;scaffold;approach;method;caveolae;point clouds;10 PC 3;accuracy;Cav 1 proteins;plasma membrane invaginations;PointNet model;SMLM data;CAVIN;CNN;super-resolution microscopy images;molecule localization microscopy data 2019-08-26
    https://plos.figshare.com/articles/figure/The_process_of_obtaining_the_class_labels_for_the_Cav1_blobs_using_wide-field_CAVIN1_PTRF_mask_The_class_labels_are_necessary_to_train_the_machine_learning_models_to_identify_the_Cav1_blobs_types_automatically_/9731924
10.1371/journal.pone.0211659.g002