Inkyu Moon, "Automated three-dimensional morphologybased clustering of human erythrocytes with regular shapes: stomatocytes, discocytes, and echinocytes," J. Biomed. Opt. 22(7), 076015 (2017), doi: 10.1117/1.JBO.22.7.076015. Abstract. We present unsupervised clustering methods for automatic grouping of human red blood cells (RBCs) extracted from RBC quantitative phase images obtained by digital holographic microscopy into three RBC clusters with regular shapes, including biconcave, stomatocyte, and sphero-echinocyte. We select some good features related to the RBC profile and morphology, such as RBC average thickness, sphericity coefficient, and mean corpuscular volume, and clustering methods, including density-based spatial clustering applications with noise, k-medoids, and k -means, are applied to the set of morphological features. The clustering results of RBCs using a set of three-dimensional features are compared against a set of two-dimensional features. Our experimental results indicate that by utilizing the introduced set of features, two groups of biconcave RBCs and old RBCs (suffering from the sphero-echinocyte process) can be perfectly clustered. In addition, by increasing the number of clusters, the three RBC types can be effectively clustered in an automated unsupervised manner with high accuracy. The performance evaluation of the clustering techniques reveals that they can assist hematologists in further diagnosis. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Cardiomyocytes derived from human pluripotent stem cells are a promising tool for disease modeling, drug compound testing, and cardiac toxicity screening. Bio-image segmentation is a prerequisite step in cardiomyocyte image analysis by digital holography (DH) in microscopic configuration and has provided satisfactory results. In this study, we quantified multiple cardiac cells from segmented 3-dimensional DH images at the single-cell level and measured multiple parameters describing the beating profile of each individual cell. The beating profile is extracted by monitoring dry-mass distribution during the mechanical contraction-relaxation activity caused by cardiac action potential. We present a robust two-step segmentation method for cardiomyocyte low-contrast image segmentation based on region and edge information. The segmented single-cell contains mostly the nucleus of the cell since it is the best part of the cardiac cell, which can be perfectly segmented. Clustering accuracy was assessed by a silhouette index evaluation for k-means clustering and the Dice similarity coefficient (DSC) of the final segmented image. 3D representation of single of cardiomyocytes. The cell contains mostly the nucleus section and a small area of cytoplasm.
Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) are a class of Recurrent Neural Networks (RNN) suitable for sequential data processing. Bidirectional LSTM (BLSTM) enables a better understanding of context by learning the future time steps in a bidirectional manner. Moreover, GRU deploys reset and update gates in the hidden layer, which is computationally more efficient than a conventional LSTM. This paper proposes an efficient network model based on deep BLSTM-GRU for ciphertext classification aiming to mark the category to which the ciphertext belongs. The proposed model performance was evaluated using well-known evaluation metrics on two publicly available datasets encrypted with various classical cipher methods and performance was compared against onedimensional convolutional neural network (1D-CNN) and various other deep learning-based approaches. The experimental results showed that the BLSTM-GRU cell unit network model achieved a high classification accuracy of up to 95.8%. To the best of our knowledge, this is the first time an RNN-based model has been applied for the ciphertext classification.INDEX TERMS Recurrent neural networks, bidirectional long short-term memory, gated recurrent unit, ciphertext classification, 1D-convolutional neural networks.
This paper investigates the rhythm strip and parameters of synchronization of human induced pluripotent stem cell (iPS) derived cardiomyocytes. The synchronization is evaluated from quantitative phase images of beating cardiomyocytes which are obtained using the time-lapse digital holographic imaging method. By quantitatively monitoring the dry mass redistribution, digital holography provides the physical contraction-relaxation signal caused by autonomous cardiac action potential. In order to analyze the synchronicity at the cell-to-cell level, we extracted single cardiac muscle cells, which contain the nuclei, from the phase images of cardiomyocytes containing multiple cells resulting from the fusion of kmeans clustering and watershed segmentation algorithms. We demonstrate that mature cardiomyocyte cell synchronization can be automatically evaluated by time-lapse microscopic holographic imaging. Our proposed method can be applied for studies on cardiomyocyte disorders and drug safety testing systems.
Cloud storage has become eminent, with an increasing amount of data being produced daily; this has led to substantial concerns related to privacy and unauthorized access. To secure privacy, users can protect their private data by uploading encrypted data to the cloud. Data encryption allows computations to be performed on encrypted data without the data being decrypted in the cloud, which requires enormous computation resources and prevents unauthorized access to private data. Data analysis such as classification, and image query and retrieval can preserve data privacy if the analysis is performed using encrypted data. This paper proposes an image-captioning method that generates captions over encrypted images using an encoder–decoder framework with attention and a double random phase encoding (DRPE) encryption scheme. The images are encrypted with DRPE to protect them and then fed to an encoder that adopts the ResNet architectures to generate a fixed-length vector of representations or features. The decoder is designed with long short-term memory to process the features and embeddings to generate descriptive captions for the images. We evaluate the predicted captions with BLEU, METEOR, ROUGE, and CIDEr metrics. The experimental results demonstrate the feasibility of our privacy-preserving image captioning on the popular benchmark Flickr8k dataset.
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