Human embryonic stem cell classification: random network with autoencoded feature extractor.

SIGNIFICANCE: Automated understanding of human embryonic stem cell (hESC) videos is essential for the quantified analysis and classification of various states of hESCs and their health for diverse applications in regenerative medicine. AIM: This paper aims to develop an ensemble method and bagging of deep learning classifiers as a model for hESC classification on a video dataset collected using a phase contrast microscope. APPROACH: The paper describes a deep learning-based random network (RandNet) with an autoencoded feature extractor for the classification of hESCs into six different classes, namely, (1) cell clusters, (2) debris, (3) unattached cells, (4) attached cells, (5) dynamically blebbing cells, and (6) apoptotically blebbing cells. The approach uses unlabeled data to pre-train the autoencoder network and fine-tunes it using the available annotated data. RESULTS: The proposed approach achieves a classification accuracy of 97.23 ± 0.94 % and outperforms the state-of-the-art methods. Additionally, the approach has a very low training cost compared with the other deep-learning-based approaches, and it can be used as a tool for annotating new videos, saving enormous hours of manual labor. CONCLUSIONS: RandNet is an efficient and effective method that uses a combination of subnetworks trained using both labeled and unlabeled data to classify hESC images.

Extraction of Blebs in Human Embryonic Stem Cell Videos.

Blebbing is an important biological indicator in determining the health of human embryonic stem cells (hESC). Especially, areas of a bleb sequence in a video are often used to distinguish two cell blebbing behaviors in hESC: dynamic and apoptotic blebbings. This paper analyzes various segmentation methods for bleb extraction in hESC videos and introduces a bio-inspired score function to improve the performance in bleb extraction. Full bleb formation consists of bleb expansion and retraction. Blebs change their size and image properties dynamically in both processes and between frames. Therefore, adaptive parameters are needed for each segmentation method. A score function derived from the change of bleb area and orientation between consecutive frames is proposed which provides adaptive parameters for bleb extraction in videos. In comparison to manual analysis, the proposed method provides an automated fast and accurate approach for bleb sequence extraction.

Bio-Driven Cell Region Detection in Human Embryonic Stem Cell Assay.

This paper proposes a bio-driven algorithm that detects cell regions automatically in the human embryonic stem cell (hESC) images obtained using a phase contrast microscope. The algorithm uses both statistical intensity distributions of foreground/hESCs and background/substrate as well as cell property for cell region detection. The intensity distributions of foreground/hESCs and background/substrate are modeled as a mixture of two Gaussians. The cell property is translated into local spatial information. The algorithm is optimized by parameters of the modeled distributions and cell regions evolve with the local cell property. The paper validates the method with various videos acquired using different microscope objectives. In comparison with the state-of-the-art methods, the proposed method is able to detect the entire cell region instead of fragmented cell regions. It also yields high marks on measures such as Jacard similarity, Dice coefficient, sensitivity and specificity. Automated detection by the proposed method has the potential to enable fast quantifiable analysis of hESCs using large data sets which are needed to understand dynamic cell behaviors.