Human Induced Pluripotent Stem Cell Reprogramming Prediction in Microscopy Images using LSTM based RNN

Chang, Yuan‐Hsiang; Abe, Kuniya; Yokota, Hideo; Sudo, Kazuhiro; Nakamura, Yukio; Chu, Slo‐Li; Hsu, Chih-Yung; Tsai, Ming-Dar

doi:10.1109/embc.2019.8857568

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…Upon a demographical analysis of the collected data, the United States was the highest contributing country in this field with 9 studies out of 27 (33.3%) [ 25 , 26 , 29 , 30 , 38 , 45 , 46 , 47 , 48 ], followed by China (13.5%) [ 27 , 32 , 41 , 42 ], Japan (11.1%) [ 28 , 33 , 37 ], and Brazil [ 50 , 51 ] and Taiwan [ 32 , 40 ] (both 7.4%). Argentina [ 34 ], Finland [ 43 ], Germany [ 39 ], Italy [ 49 ], South Korea [ 36 ], and Thailand [ 44 ] each had one study each (3.7%), as shown in Figure 2 .…”

Section: Resultsmentioning

confidence: 99%

“…Chang et al [ 32 ] and Chang et al [ 40 ] tested CNN and human iPSCs. Chang et al [ 32 ] tested CNN’s ability to trace human iPS cell formation from CD34+ cord blood cells on 144 images.…”

Section: Discussionmentioning

confidence: 99%

“…Chang et al [ 32 ] and Chang et al [ 40 ] tested CNN and human iPSCs. Chang et al [ 32 ] tested CNN’s ability to trace human iPS cell formation from CD34+ cord blood cells on 144 images. In contrast, Chang et al [ 40 ] applied the CNN on 132 images for the automatic detection and localization of human iPSC regions in brightfield microscopy images.…”

Section: Discussionmentioning

confidence: 99%

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Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review

Issa

Chaar

Kempisty

et al. 2022

Biology

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This systematic scoping review aims to map and identify the available artificial-intelligence-based techniques for imaging analysis, the characterization of stem cell differentiation, and trans-differentiation pathways. On the ninth of March 2022, data were collected from five electronic databases (PubMed, Medline, Web of Science, Cochrane, and Scopus) and manual citation searching; all data were gathered in Zotero 5.0. A total of 4422 articles were collected after deduplication; only twenty-seven studies were included in this systematic scoping review after a two-phase screening against inclusion criteria by two independent reviewers. The amount of research in this field is significantly increasing over the years. While the current state of artificial intelligence (AI) can tackle a multitude of medical problems, the consensus amongst researchers remains that AI still falls short in multiple ways that investigators should examine, ranging from the quality of images used in training sets and appropriate sample size, as well as the unexpected events that may occur which the algorithm cannot predict.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review

Issa

Chaar

Kempisty

et al. 2022

Biology

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“…Due in part to the recent development of open software in machine learning libraries, the use of ADAM optimizer was used in 7 cases [29][30][31][32][33][34][35], Pytorch in 5 cases [29,33,[35][36][37], Numpy in 2 cases [38,39], TensorFlow in 2 cases [30,40], and OpenCV in 2 cases [41,42]. (By considering the mean square and mean of the gradient as first and second-order moments, weights can be updated on an appropriate scale for each parameter.)…”

Section: Softwarementioning

confidence: 99%

Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

2023

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Growth prediction technology is not only a practical application but also a crucial approach that strengthens the safety of image processing techniques. By supplementing the growth images obtained from the original images, especially in insufficient data sets, we can increase the robustness of machine learning. Therefore, predicting the growth of living organisms is an important technology that increases the safety of existing applications that target living organisms and can extend to areas not yet realized. This paper is a systematic literature review (SLR) investigating biological growth prediction based on the PRISMA 2020 guidelines. We systematically survey existing studies from 2017 to 2022 to provide other researchers with current trends. We searched four digital libraries—IEEE Xplore, ACM Digital Library, Science Direct, and Web of Science—and finally analyzed 47 articles. We summarize the methods used, year, features, accuracy, and dataset of each paper. In particular, we explained LSTM, GAN, and STN, the most frequently used methods among the 20 papers related to machine learning (40% of all papers).

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“…In some applications, not the whole images, but simplified pixels representing probability vectors or position vectors (classified by CNNs (convolution neural networks)) were used as LSTM inputs to predict probability of future hiPS reprogramming [16] or cell motility [17].…”

Section: Introductionmentioning

confidence: 99%

Morphology And Status Transition Monitoring For Mouse Embryonic Stem Cell Colonies In Virto By LSTM Networks With Progressive Training Using Fluorescence Microscopy Images

Chu

Abe

Yokota

et al. 2021

Preprint

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Purpose Embryonic stem (ES) cells represent as a cellular resource for basic biological studies and for their uses as medically relevant cells in in vitro studies. Fluorescence microscopy images taken during cell culture are frequently used to manually monitor time-series morphology changes and status transitions of ES cell (ESC) colonies, and to study dynamical pattern formation and heterogeneity distribution within ESC colonies, intrinsic fluctuation and cell-cell cooperativity. Therefore, tracking and furthermore predicting morphology changes and status transitions of ESC colonies is an effective method to monitor culture medium for maintaining ES cells in undifferentiated or early differentiated stage. Methods A P-LSTM (Progressive Long Short-Term Memory) structure is proposed to incorporate some new time-lapse images real-time taken from incubators for a new RNN (Recurrent Neural Networks) training. The P-LSTM can achieve adaptive long- and short- term memories to generate accurate predicted images. On the time-lapse images, entropy and bi-lateral filtering are used to extract the range of every colony to calculate colony morphology. Colony status transitions between consecutive images are calculated by mapping the calculated colony centers and ranges. Results Accuracies for the colony status transition, area and roundness for the 15 predicted (five-hour) future frames calculated from 1500-2500 colonies for respective frames show the effectiveness of the proposed method.Conclusion We proposed an efficient and automatic method to predict and monitor status transitions and morphology changes of mouse ESC colonies in culture using time-lapse fluorescence microscopy images.

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Human Induced Pluripotent Stem Cell Reprogramming Prediction in Microscopy Images using LSTM based RNN

Cited by 8 publications

References 9 publications

Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review

Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review

Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

Morphology And Status Transition Monitoring For Mouse Embryonic Stem Cell Colonies In Virto By LSTM Networks With Progressive Training Using Fluorescence Microscopy Images

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