PROPOSAL OF RATIOMETRIC INDEX FOR THE DIFFERENTIATION OF CELL PAINTED SUBORGANELLES USING DEEP CNN-BASED SEMANTIC SEGMENTATION

Abstract

Cell painting technique provides large amount of potential information for applications such as drug discovery, bioactivity prediction and cytotoxicity assessment. However, its utility is restricted due to the requirement of advanced, costly and specific instrumentation protocols. Therefore, creating cell painted images using simple microscopic data can provide a better alternative for these applications. This study investigates the applicability of deep network-based semantic segmentation to generate cell painted images of nuclei, endoplasmic reticulum (ER) and cytoplasm from a composite image. For this, 3456 composite images from a public dataset of Broad Bioimage Benchmark collection are considered. The corresponding ground truth images for nuclei, ER and cytoplasm are generated using Otsu’s thresholding technique and used as labeled dataset. Semantic segmentation network is applied to these data and optimized using stochastic gradient descent with momentum algorithm at a learning rate of 0.01. The segmentation performance of the trained network is evaluated using accuracy, loss, mean Boundary [Formula: see text] (BF) score, Dice Index, Jaccard Index and structural similarity index. Gradient weighted Class Activation Mapping (Grad-CAM) is employed to visualize significant image regions identified by the model. Further, a cellular index is proposed as a geometrical measure which is capable of differentiating the segmented cell organelles. The trained model yields 96.52% accuracy with a loss of 0.07 for 50 epochs. Dice Index of 0.93, 0.76 and 0.75 is achieved for nuclei, ER and cytoplasm respectively. It is observed that nuclei to cytoplasm provides comparatively higher percentage change (74.56%) in the ratiometric index than nuclei to ER and ER to cytoplasm. The achieved results demonstrate that the proposed study can predict the cell painted organelles from a composite image with good performance measures. This study could be employed for generating cell painted organelles from raw microscopy images without using specific fluorescent labeling.