10.1371/journal.pone.0211659.g002
Ismail M. Khater
Ismail M.
Khater
Stephane T. Aroca-Ouellette
Stephane T.
Aroca-Ouellette
Fanrui Meng
Fanrui
Meng
Ivan Robert Nabi
Ivan Robert
Nabi
Ghassan Hamarneh
Ghassan
Hamarneh
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.
Public Library of Science
2019
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 17:25:07
Figure
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
<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>