Understanding cell behaviors can provide new knowledge on the development of different pathologies. Focal adhesion (FA) sites are important sub-cellular structures that are involved in these processes. To better facilitate the study of FA sites, deep learning (DL) can be used to predict FA site morphology based on limited datasets (e.g., cell membrane images). However, calculating the accuracy score of these predictions can be challenging due to the discrete/point pattern like nature of FA sites. In the present work, a new image similarity metric, discrete protein metric (DPM), was developed to calculate FA prediction accuracy. This metric measures differences in distribution (d), shape/size (s), and angle (a) of FA sites between the predicted image and its ground truth image. Performance of the DPM was evaluated by comparing it to three other commonly used image similarity metrics: Pearson correlation coefficient (PCC), feature similarity index (FSIM), and Intersection over Union (IoU). A sensitivity analysis was performed by comparing changes in each metric value due to quantifiable changes in FA site location, number, aspect ratio, area, or orientation. Furthermore, accuracy score of DL-generated predictions was calculated using all four metrics to compare their ability to capture variation across samples. Results showed better sensitivity and range of variation for DPM compared to the other metrics tested. Most importantly, DPM had the ability to determine which FA predictions were quantitatively more accurate and consistent with qualitative assessments. The proposed DPM hence provides a method to validate DL-generated FA predictions and can be extended to evaluating other predicted or segmented discrete structures of biomedical relevance.
Gaining insight into different cell behaviors is key to better understanding different pathologies. These behaviors may be explained in part through close observation of 3D cell morphology. Therefore, the objective of this research was to develop a machine learning (ML) framework that can predict 3D subcellular morphological variation of endothelial cells (ECs) to generate digital twins. ECs were cultured and their membrane, nucleus, and focal adhesion (FA) sites were stained and imaged with confocal microscopy. The multicellular confocal z stacks were segmented resulting in a total of 60 single-cell stacks. Fifty randomly picked cells were augmented 20-fold to train the ML framework, and the remaining 10 were used for an independent test of prediction accuracy. The ML framework was based on an open-source conditional generative adversarial network (cGAN), which was expanded to make 3D predictions using membrane only as input to predict nucleus and FA morphology. After training the framework, the results on the independent test showed an average prediction accuracy of ~87% for nucleus and ~70% for FA sites. The predictions were used to build a digital twin of each EC and compared to their respective ground truth, showing an average ~79% global accuracy and ~84% accuracy in FA-Nucleus distribution. The results presented show the effectiveness of the developed ML framework to generate digital twins of ECs using limited amount of data. These digital twins can be used to couple EC morphology with different behaviors. The ML framework can be potentially expanded to predict morphology of other subcellular structures as well as to study other types of cells.
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