A deep learning model for detection and tracking in high-throughput images of organoid

Understanding patient-derived tumor organoid growth through an integrated imaging and mathematical modeling framework

Patient-derived tumor organoids (PDTOs) are novel cellular models that maintain the genetic, phenotypic and structural features of patient tumor tissue and are useful for studying tumorigenesis and drug response. When integrated with advanced 3D imaging and analysis techniques, PDTOs can be used to establish physiologically relevant high-throughput and high-content drug screening platforms that support the development of patient-specific treatment strategies. However, in order to effectively leverage high-throughput PDTO observations for clinical predictions, it is critical to establish a quantitative understanding of the basic properties and variability of organoid growth dynamics. In this work, we introduced an innovative workflow for analyzing and understanding PDTO growth dynamics, by integrating a high-throughput imaging deep learning platform with mathematical modeling, incorporating flexible growth laws and variable dormancy times. We applied the workflow to colon cancer organoids and demonstrated that organoid growth is well-described by the Gompertz model of growth. Our analysis showed significant intrapatient heterogeneity in PDTO growth dynamics, with the initial exponential growth rate of an organoid following a lognormal distribution within each dataset. The level of intrapatient heterogeneity varied between patients, as did organoid growth rates and dormancy times of single seeded cells. Our work contributes to an emerging understanding of the basic growth characteristics of PDTOs, and it highlights the heterogeneity in organoid growth both within and between patients. These results pave the way for further modeling efforts aimed at predicting treatment response dynamics and drug resistance timing.

From Organ-on-a-Chip to Human-on-a-Chip: A Review of Research Progress and Latest Applications

Organ-on-a-Chip (OOC) technology, which emulates the physiological environment and functionality of human organs on a microfluidic chip, is undergoing significant technological advancements. Despite its rapid evolution, this technology is also facing notable challenges, such as the lack of vascularization, the development of multiorgan-on-a-chip systems, and the replication of the human body on a single chip. The progress of microfluidic technology has played a crucial role in steering OOC toward mimicking the human microenvironment, including vascularization, microenvironment replication, and the development of multiorgan microphysiological systems. Additionally, advancements in detection, analysis, and organoid imaging technologies have enhanced the functionality and efficiency of Organs-on-Chips (OOCs). In particular, the integration of artificial intelligence has revolutionized organoid imaging, significantly enhancing high-throughput drug screening. Consequently, this review covers the research progress of OOC toward Human-on-a-chip, the integration of sensors in OOCs, and the latest applications of organoid imaging technologies in the biomedical field.

Lung Organoids: Systematic Review of Recent Advancements and its Future Perspectives

Organoids are essentially an in vitro (lab-grown) three-dimensional tissue culture system model that meticulously replicates the structure and physiology of human organs. A few of the present applications of organoids are in the basic biological research area, molecular medicine and pharmaceutical drug testing. Organoids are crucial in connecting the gap between animal models and human clinical trials during the drug discovery process, which significantly lowers the time duration and cost associated with each stage of testing. Likewise, they can be used to understand cell-to-cell interactions, a crucial aspect of tissue biology and regeneration, and to model disease pathogenesis at various stages of the disease. Lung organoids can be utilized to explore numerous pathophysiological activities of a lung since they share similarities with its function. Researchers have been trying to recreate the complex nature of the lung by developing various "Lung organoids" models.This article is a systematic review of various developments of lung organoids and their potential progenitors. It also covers the in-depth applications of lung organoids for the advancement of translational research. The review discusses the methodologies to establish different types of lung organoids for studying the regenerative capability of the respiratory system and comprehending various respiratory diseases.Respiratory diseases are among the most common worldwide, and the growing burden must be addressed instantaneously. Lung organoids along with diverse bio-engineering tools and technologies will serve as a novel model for studying the pathophysiology of various respiratory diseases and for drug screening purposes.

Design optimization of geometrically confined cardiac organoids enabled by machine learning techniques

Stem cell organoids are powerful models for studying organ development, disease modeling, drug screening, and regenerative medicine applications. The convergence of organoid technology, tissue engineering, and artificial intelligence (AI) could potentially enhance our understanding of the design principles for organoid engineering. In this study, we utilized micropatterning techniques to create a designer library of 230 cardiac organoids with 7 geometric designs. We employed manifold learning techniques to analyze single organoid heterogeneity based on 10 physiological parameters. We clustered and refined the cardiac organoids based on their functional similarity using unsupervised machine learning approaches, thus elucidating unique functionalities associated with geometric designs. We also highlighted the critical role of calcium transient rising time in distinguishing organoids based on geometric patterns and clustering results. This integration of organoid engineering and machine learning enhances our understanding of structure-function relationships in cardiac organoids, paving the way for more controlled and optimized organoid design.

Segmentation and Multi-Timepoint Tracking of 3D Cancer Organoids from Optical Coherence Tomography Images Using Deep Neural Networks

Recent years have ushered in a transformative era in in vitro modeling with the advent of organoids, three-dimensional structures derived from stem cells or patient tumor cells. Still, fully harnessing the potential of organoids requires advanced imaging technologies and analytical tools to quantitatively monitor organoid growth. Optical coherence tomography (OCT) is a promising imaging modality for organoid analysis due to its high-resolution, label-free, non-destructive, and real-time 3D imaging capabilities, but accurately identifying and quantifying organoids in OCT images remain challenging due to various factors. Here, we propose an automatic deep learning-based pipeline with convolutional neural networks that synergistically includes optimized preprocessing steps, the implementation of a state-of-the-art deep learning model, and ad-hoc postprocessing methods, showcasing good generalizability and tracking capabilities over an extended period of 13 days. The proposed tracking algorithm thoroughly documents organoid evolution, utilizing reference volumes, a dual branch analysis, key attribute evaluation, and probability scoring for match identification. The proposed comprehensive approach enables the accurate tracking of organoid growth and morphological changes over time, advancing organoid analysis and serving as a solid foundation for future studies for drug screening and tumor drug sensitivity detection based on organoids.

Molecular distinctions of bronchoalveolar and alveolar organoids under differentiation conditions

The bronchoalveolar organoid (BAO) model is increasingly acknowledged as an ex-vivo platform that accurately emulates the structural and functional attributes of proximal airway tissue. The transition from bronchoalveolar progenitor cells to alveolar organoids is a common event during the generation of BAOs. However, there is a pressing need for comprehensive analysis to elucidate the molecular distinctions characterizing the pre-differentiated and post-differentiated states within BAO models. This study established a murine BAO model and subsequently triggered its differentiation. Thereafter, a suite of multidimensional analytical procedures was employed, including the morphological recognition and examination of organoids utilizing an established artificial intelligence (AI) image tracking system, quantification of cellular composition, proteomic profiling and immunoblots of selected proteins. Our investigation yielded a detailed evaluation of the morphologic, cellular, and molecular variances demarcating the pre- and post-differentiation phases of the BAO model. We also identified of a potential molecular signature reflective of the observed morphological transformations. The integration of cutting-edge AI-driven image analysis with traditional cellular and molecular investigative methods has illuminated key features of this nascent model.

A large and diverse brain organoid dataset of 1,400 cross-laboratory images of 64 trackable brain organoids

Brain organoids represent a useful tool for modeling of neurodevelopmental disorders and can recapitulate brain volume alterations such as microcephaly. To monitor organoid growth, brightfield microscopy images are frequently used and evaluated manually which is time-consuming and prone to observer-bias. Recent software applications for organoid evaluation address this issue using classical or AI-based methods. These pipelines have distinct strengths and weaknesses that are not evident to external observers. We provide a dataset of more than 1,400 images of 64 trackable brain organoids from four clones differentiated from healthy and diseased patients. This dataset is especially powerful to test and compare organoid analysis pipelines because of (1) trackable organoids (2) frequent imaging during development (3) clone diversity (4) distinct clone development (5) cross sample imaging by two different labs (6) common imaging distractors, and (6) pixel-level ground truth organoid annotations. Therefore, this dataset allows to perform differentiated analyses to delineate strengths, weaknesses, and generalizability of automated organoid analysis pipelines as well as analysis of clone diversity and similarity.

Image-based profiling and deep learning reveal morphological heterogeneity of colorectal cancer organoids

Patient-derived organoids have proven to be a highly relevant model for evaluating of disease mechanisms and drug efficacies, as they closely recapitulate in vivo physiology. Colorectal cancer organoids, specifically, exhibit a diverse range of morphologies, which have been analyzed with image-based profiling. However, the relationship between morphological subtypes and functional parameters of the organoids remains underexplored. Here, we identified two distinct morphological subtypes ("cystic" and "solid") across 31360 bright field images using image-based profiling, which correlated differently with viability and apoptosis level of colorectal cancer organoids. Leveraging object detection neural networks, we were able to categorize single organoids achieving higher viability scores as "cystic" than "solid" subtype. Furthermore, a deep generative model was proposed to predict apoptosis intensity based on a apoptosis-featured dataset encompassing over 17000 bright field and matched fluorescent images. Notably, a significant correlation of 0.91 between the predicted value and ground truth was achived, underscoring the feasibility of this generative model as a potential means for assessing organoid functional parameters. The underlying cellular heterogeneity of the organoids, i.e., conserved colonic cell types and rare immune components, was also verified with scRNA sequencing, implying a compromised tumor microenvironment. Additionally, the "cystic" subtype was identified as a relapse phenotype featuring intestinal stem cell signatures, suggesting that this visually discernible relapse phenotype shows potential as a novel biomarker for colorectal cancer diagnosis and prognosis. In summary, our findings demonstrate that the morphological heterogeneity of colorectal cancer organoids explicitly recapitulate the association of phenotypic features and exogenous perturbations through the image-based profiling, providing new insights into disease mechanisms.

Vascularized organoid-on-a-chip: design, imaging, and analysis

Vascularized organoid-on-a-chip (VOoC) models achieve substance exchange in deep layers of organoids and provide a more physiologically relevant system in vitro. Common designs for VOoC primarily involve two categories: self-assembly of endothelial cells (ECs) to form microvessels and pre-patterned vessel lumens, both of which include the hydrogel region for EC growth and allow for controlled fluid perfusion on the chip. Characterizing the vasculature of VOoC often relies on high-resolution microscopic imaging. However, the high scattering of turbid tissues can limit optical imaging depth. To overcome this limitation, tissue optical clearing (TOC) techniques have emerged, allowing for 3D visualization of VOoC in conjunction with optical imaging techniques. The acquisition of large-scale imaging data, coupled with high-resolution imaging in whole-mount preparations, necessitates the development of highly efficient analysis methods. In this review, we provide an overview of the chip designs and culturing strategies employed for VOoC, as well as the applicable optical imaging and TOC methods. Furthermore, we summarize the vascular analysis techniques employed in VOoC, including deep learning. Finally, we discuss the existing challenges in VOoC and vascular analysis methods and provide an outlook for future development.

Revealing the clinical potential of high-resolution organoids

The symbiotic interplay of organoid technology and advanced imaging strategies yields innovative breakthroughs in research and clinical applications. Organoids, intricate three-dimensional cell cultures derived from pluripotent or adult stem/progenitor cells, have emerged as potent tools for in vitro modeling, reflecting in vivo organs and advancing our grasp of tissue physiology and disease. Concurrently, advanced imaging technologies such as confocal, light-sheet, and two-photon microscopy ignite fresh explorations, uncovering rich organoid information. Combined with advanced imaging technologies and the power of artificial intelligence, organoids provide new insights that bridge experimental models and real-world clinical scenarios. This review explores exemplary research that embodies this technological synergy and how organoids reshape personalized medicine and therapeutics.

Organoid intelligence: Integration of organoid technology and artificial intelligence in the new era of in vitro models

Organoid Intelligence ushers in a new era by seamlessly integrating cutting-edge organoid technology with the power of artificial intelligence. Organoids, three-dimensional miniature organ-like structures cultivated from stem cells, offer an unparalleled opportunity to simulate complex human organ systems in vitro. Through the convergence of organoid technology and AI, researchers gain the means to accelerate discoveries and insights across various disciplines. Artificial intelligence algorithms enable the comprehensive analysis of intricate organoid behaviors, intricate cellular interactions, and dynamic responses to stimuli. This synergy empowers the development of predictive models, precise disease simulations, and personalized medicine approaches, revolutionizing our understanding of human development, disease mechanisms, and therapeutic interventions. Organoid Intelligence holds the promise of reshaping how we perceive in vitro modeling, propelling us toward a future where these advanced systems play a pivotal role in biomedical research and drug development.

Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation

Ascertaining the collective viability of cells in different cell culture conditions has typically relied on averaging colorimetric indicators and is often reported out in simple binary readouts. Recent research has combined viability assessment techniques with image-based deep-learning models to automate the characterization of cellular properties. However, further development of viability measurements to assess the continuity of possible cellular states and responses to perturbation across cell culture conditions is needed. In this work, we demonstrate an image processing algorithm for quantifying features associated with cellular viability in 3D cultures without the need for assay-based indicators. We show that our algorithm performs similarly to a pair of human experts in whole-well images over a range of days and culture matrix compositions. To demonstrate potential utility, we perform a longitudinal study investigating the impact of a known therapeutic on pancreatic cancer spheroids. Using images taken with a high content imaging system, the algorithm successfully tracks viability at the individual spheroid and whole-well level. The method we propose reduces analysis time by 97% in comparison with the experts. Because the method is independent of the microscope or imaging system used, this approach lays the foundation for accelerating progress in and for improving the robustness and reproducibility of 3D culture analysis across biological and clinical research.

Angio-Net: deep learning-based label-free detection and morphometric analysis of in vitro angiogenesis

Despite significant advancements in three-dimensional (3D) cell culture technology and the acquisition of extensive data, there is an ongoing need for more effective and dependable data analysis methods. These concerns arise from the continued reliance on manual quantification techniques. In this study, we introduce a microphysiological system (MPS) that seamlessly integrates 3D cell culture to acquire large-scale imaging data and employs deep learning-based virtual staining for quantitative angiogenesis analysis. We utilize a standardized microfluidic device to obtain comprehensive angiogenesis data. Introducing Angio-Net, a novel solution that replaces conventional immunocytochemistry, we convert brightfield images into label-free virtual fluorescence images through the fusion of SegNet and cGAN. Moreover, we develop a tool capable of extracting morphological blood vessel features and automating their measurement, facilitating precise quantitative analysis. This integrated system proves to be invaluable for evaluating drug efficacy, including the assessment of anticancer drugs on targets such as the tumor microenvironment. Additionally, its unique ability to enable live cell imaging without the need for cell fixation promises to broaden the horizons of pharmaceutical and biological research. Our study pioneers a powerful approach to high-throughput angiogenesis analysis, marking a significant advancement in MPS.

Deep-Orga: An improved deep learning-based lightweight model for intestinal organoid detection

PROBLEM

Organoids are 3D cultures that are commonly used for biological and medical research in vitro due to their functional and structural similarity to source organs. The development of organoids can be assessed by morphological tests. However, manual analysis of organoid morphology requires intensive labor from professionals and is prone to observer discrepancies.

AIM

Computer-assisted methods alleviate the pressure of manual labor, especially with the development of deep learning, the performance of morphological detection has been further improved. The aim of this paper is to automate the assessment of organoid morphology using deep learning techniques to reduce the labor pressure of professionals.

METHODS

Based on the lightweight model YOLOX, a lightweight intestinal organoid detection model named Deep-Orga is proposed. First, the performance of the Deep-Orga model is compared with other classical models on the intestinal organoids dataset. Then, ablation experiments are used to validate the improvement of the model detection performance by the improved module. Finally, Deep-Orga is compared with other methods.

RESULTS

Deep-Orga achieves optimal organoid detection with a partial increase in computational effort. Using Deep-Orga to replace the manual analysis process provides a new automated method for organoid morphology evaluation.

CONCLUSION

Deep-Orga proposed in this paper is able to accurately assess organoid development, effectively relieving the labor pressure of professionals and avoiding the subjectivity of assessment. This paper demonstrates the potential application of deep learning in the field of organoid morphology analysis.

Analysis of organoid and immune cell co-cultures by machine learning-empowered image cytometry

Organoids are three-dimensional (3D) structures that can be derived from stem cells or adult tissue progenitor cells and exhibit an extraordinary ability to autonomously organize and resemble the cellular composition and architectural integrity of specific tissue segments. This feature makes them a useful tool for analyzing therapeutical relevant aspects, including organ development, wound healing, immune disorders and drug discovery. Most organoid models do not contain cells that mimic the neighboring tissue’s microenvironment, which could potentially hinder deeper mechanistic studies. However, to use organoid models in mechanistic studies, which would enable us to better understand pathophysiological processes, it is necessary to emulate the in situ microenvironment. This can be accomplished by incorporating selected cells of interest from neighboring tissues into the organoid culture. Nevertheless, the detection and quantification of organoids in such co-cultures remains a major technical challenge. These imaging analysis approaches would require an accurate separation of organoids from the other cell types in the co-culture. To efficiently detect and analyze 3D organoids in co-cultures, we developed a high-throughput imaging analysis platform. This method integrates automated imaging techniques and advanced image processing tools such as grayscale conversion, contrast enhancement, membrane detection and structure separation. Based on machine learning algorithms, we were able to identify and classify 3D organoids within dense co-cultures of immune cells. This procedure allows a high-throughput analysis of organoid-associated parameters such as quantity, size, and shape. Therefore, the technology has significant potential to advance contextualized research using organoid co-cultures and their potential applications in translational medicine.

AI-enabled organoids: Construction, analysis, and application

Organoids, miniature and simplified in vitro model systems that mimic the structure and function of organs, have attracted considerable interest due to their promising applications in disease modeling, drug screening, personalized medicine, and tissue engineering. Despite the substantial success in cultivating physiologically relevant organoids, challenges remain concerning the complexities of their assembly and the difficulties associated with data analysis. The advent of AI-Enabled Organoids, which interfaces with artificial intelligence (AI), holds the potential to revolutionize the field by offering novel insights and methodologies that can expedite the development and clinical application of organoids. This review succinctly delineates the fundamental concepts and mechanisms underlying AI-Enabled Organoids, summarizing the prospective applications on rapid screening of construction strategies, cost-effective extraction of multiscale image features, streamlined analysis of multi-omics data, and precise preclinical evaluation and application. We also explore the challenges and limitations of interfacing organoids with AI, and discuss the future direction of the field. Taken together, the AI-Enabled Organoids hold significant promise for advancing our understanding of organ development and disease progression, ultimately laying the groundwork for clinical application.

Organalysis: Multifunctional Image Preprocessing and Analysis Software for Cardiac Organoid Studies

Due to a growing need in visualizing human pluripotent stem cell-derived organoids from recent advancements in the field, an efficient bulk-processing application is necessary to provide preprocessing and image analysis services. In this study, we developed Organalysis, a high-accuracy, multifunctional, and accessible application that meets these needs by providing the functionality of image manipulation and enhancement, organoid area and intensity calculation, fractal analysis, noise removal, and feature importance computation. The image manipulation feature includes brightness and contrast adjustment. The area and intensity calculation computes six values for each image: organoid area, total image area, percentage of the image covered by organoid, the total intensity of organoid, the total intensity of organoid-by-organoid area, and total intensity of organoid by total image area. The fractal analysis function computes the fractal dimension value for each image. The noise removal function removes superfluous marks from the input images, such as bubbles and other unwanted noise. The feature importance function trains a lasso-regularized linear regression machine learning algorithm to identify cardiac growth factors that are the strongest determinants for cell differentiation. The batch processing of this application further builds on existing services like ImageJ to provide a more convenient way to process multiple images. Collectively, the versatility and preciseness of Organalysis demonstrate novelty, since no other current imaging software combines the capability of batch processing and the breadth of feature analysis. Therefore, Organalysis provides unique functions in cardiac organoid research and proves to be invaluable in regenerative medicine.

Deep Learning Model for Predicting Airway Organoid Differentiation

BACKGROUND

Organoids are self-organized three-dimensional culture systems and have the advantages of both in vitro and in vivo experiments. However, each organoid has a different degree of self-organization, and methods such as immunofluorescence staining are required for confirmation. Therefore, we established a system to select organoids with high tissue-specific similarity using deep learning without relying on staining by acquiring bright-field images in a non-destructive manner.

METHODS

We identified four biomarkers in RNA extracted from airway organoids. We also predicted biomarker expression by image-based analysis of organoids by convolution neural network, a deep learning method.

RESULTS

We predicted airway organoid-specific marker expression from bright-field images of organoids. Organoid differentiation was verified by immunofluorescence staining of the same organoid after predicting biomarker expression in bright-field images.

CONCLUSION

Our study demonstrates the potential of imaging and deep learning to distinguish organoids with high human tissue similarity in disease research and drug screening.

Spectroscopic techniques for monitoring stem cell and organoid proliferation in 3D environments for therapeutic development

Spectroscopic techniques for monitoring stem cell and organoid proliferation have gained significant attention in therapeutic development. Spectroscopic techniques such as fluorescence, Raman spectroscopy, and infrared spectroscopy offer noninvasive and real-time monitoring of biochemical and biophysical changes that occur during stem cell and organoid proliferation. These techniques provide valuable insight into the underlying mechanisms of action of potential therapeutic agents, allowing for improved drug discovery and screening. This review highlights the importance of spectroscopic monitoring of stem cell and organoid proliferation and its potential impact on therapeutic development. Furthermore, this review discusses recent advances in spectroscopic techniques and their applications in stem cell and organoid research. Overall, this review emphasizes the importance of spectroscopic techniques as valuable tools for studying stem cell and organoid proliferation and their potential to revolutionize therapeutic development in the future.

Application of colloidal photonic crystals in study of organoids

As alternative disease models, other than 2D cell lines and patient-derived xenografts, organoids have preferable in vivo physiological relevance. However, both endogenous and exogenous limitations impede the development and clinical translation of these organoids. Fortunately, colloidal photonic crystals (PCs), which benefit from favorable biocompatibility, brilliant optical manipulation, and facile chemical decoration, have been applied to the engineering of organoids and have achieved the desirable recapitulation of the ECM niche, well-defined geometrical onsets for initial culture, in situ multiphysiological parameter monitoring, single-cell biomechanical sensing, and high-throughput drug screening with versatile functional readouts. Herein, we review the latest progress in engineering organoids fabricated from colloidal PCs and provide inputs for future research.

Computer vision meets microfluidics: a label-free method for high-throughput cell analysis

In this paper, we review the integration of microfluidic chips and computer vision, which has great potential to advance research in the life sciences and biology, particularly in the analysis of cell imaging data. Microfluidic chips enable the generation of large amounts of visual data at the single-cell level, while computer vision techniques can rapidly process and analyze these data to extract valuable information about cellular health and function. One of the key advantages of this integrative approach is that it allows for noninvasive and low-damage cellular characterization, which is important for studying delicate or fragile microbial cells. The use of microfluidic chips provides a highly controlled environment for cell growth and manipulation, minimizes experimental variability and improves the accuracy of data analysis. Computer vision can be used to recognize and analyze target species within heterogeneous microbial populations, which is important for understanding the physiological status of cells in complex biological systems. As hardware and artificial intelligence algorithms continue to improve, computer vision is expected to become an increasingly powerful tool for in situ cell analysis. The use of microelectromechanical devices in combination with microfluidic chips and computer vision could enable the development of label-free, automatic, low-cost, and fast cellular information recognition and the high-throughput analysis of cellular responses to different compounds, for broad applications in fields such as drug discovery, diagnostics, and personalized medicine.

Patient-derived organoids in translational oncology and drug screening

Patient-derived organoids (PDO) are a new biomedical research model that can reconstruct phenotypic and genetic characteristics of the original tissue and are useful for research on pathogenesis and drug screening. To introduce the progression in this field, we review the key factors of constructing organoids derived from epithelial tissues and cancers, covering culture medium and matrix, morphological characteristics, genetic profiles, high-throughput drug screening, and application potential. We also discuss the co-culture system of cancer organoids with tumor microenvironment (TME) associated cells. The co-culture system is widely used in evaluating crosstalk of cancer cells with TME components, such as fibroblasts, endothelial cells, immune cells, and microorganisms. The article provides a prospective for standardized cultivation mode, automatic morphological evaluation, and drug sensitivity screening using high-throughput methods.

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence

In modern terminology, "organoids" refer to cells that grow in a specific three-dimensional (3D) environment in vitro, sharing similar structures with their source organs or tissues. Observing the morphology or growth characteristics of organoids through a microscope is a commonly used method of organoid analysis. However, it is difficult, time-consuming, and inaccurate to screen and analyze organoids only manually, a problem which cannot be easily solved with traditional technology. Artificial intelligence (AI) technology has proven to be effective in many biological and medical research fields, especially in the analysis of single-cell or hematoxylin/eosin stained tissue slices. When used to analyze organoids, AI should also provide more efficient, quantitative, accurate, and fast solutions. In this review, we will first briefly outline the application areas of organoids and then discuss the shortcomings of traditional organoid measurement and analysis methods. Secondly, we will summarize the development from machine learning to deep learning and the advantages of the latter, and then describe how to utilize a convolutional neural network to solve the challenges in organoid observation and analysis. Finally, we will discuss the limitations of current AI used in organoid research, as well as opportunities and future research directions.

Graphic abstract

Automated detection and growth tracking of 3D bio-printed organoid clusters using optical coherence tomography with deep convolutional neural networks

Organoids are advancing the development of accurate prediction of drug efficacy and toxicity in vitro. These advancements are attributed to the ability of organoids to recapitulate key structural and functional features of organs and parent tumor. Specifically, organoids are self-organized assembly with a multi-scale structure of 30-800 μm, which exacerbates the difficulty of non-destructive three-dimensional (3D) imaging, tracking and classification analysis for organoid clusters by traditional microscopy techniques. Here, we devise a 3D imaging, segmentation and analysis method based on Optical coherence tomography (OCT) technology and deep convolutional neural networks (CNNs) for printed organoid clusters (Organoid Printing and optical coherence tomography-based analysis, OPO). The results demonstrate that the organoid scale influences the segmentation effect of the neural network. The multi-scale information-guided optimized EGO-Net we designed achieves the best results, especially showing better recognition workout for the biologically significant organoid with diameter ≥50 μm than other neural networks. Moreover, OPO achieves to reconstruct the multiscale structure of organoid clusters within printed microbeads and calibrate the printing errors by segmenting the printed microbeads edges. Overall, the classification, tracking and quantitative analysis based on image reveal that the growth process of organoid undergoes morphological changes such as volume growth, cavity creation and fusion, and quantitative calculation of the volume demonstrates that the growth rate of organoid is associated with the initial scale. The new method we proposed enable the study of growth, structural evolution and heterogeneity for the organoid cluster, which is valuable for drug screening and tumor drug sensitivity detection based on organoids.

Segmentation and Tracking of Mammary Epithelial Organoids in Brightfield Microscopy

We present an automated and deep-learning-based workflow to quantitatively analyze the spatiotemporal development of mammary epithelial organoids in two-dimensional time-lapse (2D+t) sequences acquired using a brightfield microscope at high resolution. It involves a convolutional neural network (U-Net), purposely trained using computer-generated bioimage data created by a conditional generative adversarial network (pix2pixHD), to infer semantic segmentation, adaptive morphological filtering to identify organoid instances, and a shape-similarity-constrained, instance-segmentation-correcting tracking procedure to reliably cherry-pick the organoid instances of interest in time. By validating it using real 2D+t sequences of mouse mammary epithelial organoids of morphologically different phenotypes, we clearly demonstrate that the workflow achieves reliable segmentation and tracking performance, providing a reproducible and laborless alternative to manual analyses of the acquired bioimage data.

Deep learning predicts the differentiation of kidney organoids derived from human induced pluripotent stem cells

BACKGROUND

Kidney organoids derived from human pluripotent stem cells (hPSCs) contain multilineage nephrogenic progenitor cells and can recapitulate the development of the kidney. Kidney organoids derived from hPSCs have the potential to be applied in regenerative medicine as well as renal disease modeling, drug screening, and nephrotoxicity testing. Despite biotechnological advances, individual differences in morphological and growth characteristics among kidney organoids need to be addressed before clinical and commercial application. In this study, we hypothesized that an automated noninvasive method based on deep learning of bright-field images of kidney organoids can predict their differentiation status.

METHODS

Bright-field images of kidney organoids were collected on day 18 after differentiation. To train convolutional neural networks (CNNs), we utilized a transfer learning approach. CNNs were trained to predict the differentiation of kidney organoids on bright-field images based on the messenger RNA expression of renal tubular epithelial cells as well as podocytes.

RESULTS

The best prediction model was DenseNet121 with a total Pearson correlation coefficient score of 0.783 on a test dataset. W classified the kidney organoids into two categories: organoids with above-average gene expression (Positive) and those with below-average gene expression (Negative). Comparing the best-performing CNN with human-based classifiers, the CNN algorithm had a receiver operating characteristic-area under the curve (AUC) score of 0.85, while the experts had an AUC score of 0.48.

CONCLUSION

These results confirmed our original hypothesis and demonstrated that our artificial intelligence algorithm can successfully recognize the differentiation status of kidney organoids.

D-CryptO: deep learning-based analysis of colon organoid morphology from brightfield images

Stem cell-derived organoids are a promising tool to model native human tissues as they resemble human organs functionally and structurally compared to traditional monolayer cell-based assays. For instance, colon organoids can spontaneously develop crypt-like structures similar to those found in the native colon. While analyzing the structural development of organoids can be a valuable readout, using traditional image analysis tools makes it challenging because of the heterogeneities and the abstract nature of organoid morphologies. To address this limitation, we developed and validated a deep learning-based image analysis tool, named D-CryptO, for the classification of organoid morphology. D-CryptO can automatically assess the crypt formation and opacity of colorectal organoids from brightfield images to determine the extent of organoid structural maturity. To validate this tool, changes in organoid morphology were analyzed during organoid passaging and short-term forskolin stimulation. To further demonstrate the potential of D-CryptO for drug testing, organoid structures were analyzed following treatments with a panel of chemotherapeutic drugs. With D-CryptO, subtle variations in how colon organoids responded to the different chemotherapeutic drugs were detected, which suggest potentially distinct mechanisms of action. This tool could be expanded to other organoid types, like intestinal organoids, to facilitate 3D tissue morphological analysis.

Application of medical imaging methods and artificial intelligence in tissue engineering and organ-on-a-chip

Organ-on-a-chip (OOC) is a new type of biochip technology. Various types of OOC systems have been developed rapidly in the past decade and found important applications in drug screening and precision medicine. However, due to the complexity in the structure of both the chip-body itself and the engineered-tissue inside, the imaging and analysis of OOC have still been a big challenge for biomedical researchers. Considering that medical imaging is moving towards higher spatial and temporal resolution and has more applications in tissue engineering, this paper aims to review medical imaging methods, including CT, micro-CT, MRI, small animal MRI, and OCT, and introduces the application of 3D printing in tissue engineering and OOC in which medical imaging plays an important role. The achievements of medical imaging assisted tissue engineering are reviewed, and the potential applications of medical imaging in organoids and OOC are discussed. Moreover, artificial intelligence - especially deep learning - has demonstrated its excellence in the analysis of medical imaging; we will also present the application of artificial intelligence in the image analysis of 3D tissues, especially for organoids developed in novel OOC systems.

Present Application and Perspectives of Organoid Imaging Technology

An organoid is a miniaturized and simplified in vitro model with a similar structure and function to a real organ. In recent years, the use of organoids has increased explosively in the field of growth and development, disease simulation, drug screening, cell therapy, etc. In order to obtain necessary information, such as morphological structure, cell function and dynamic signals, it is necessary and important to directly monitor the culture process of organoids. Among different detection technologies, imaging technology is a simple and convenient choice and can realize direct observation and quantitative research. In this review, the principle, advantages and disadvantages of imaging technologies that have been applied in organoids research are introduced. We also offer an overview of prospective technologies for organoid imaging. This review aims to help biologists find appropriate imaging techniques for different areas of organoid research, and also contribute to the development of organoid imaging systems.

Imaging-Based Machine Learning Analysis of Patient-Derived Tumor Organoid Drug Response

Three-quarters of compounds that enter clinical trials fail to make it to market due to safety or efficacy concerns. This statistic strongly suggests a need for better screening methods that result in improved translatability of compounds during the preclinical testing period. Patient-derived organoids have been touted as a promising 3D preclinical model system to impact the drug discovery pipeline, particularly in oncology. However, assessing drug efficacy in such models poses its own set of challenges, and traditional cell viability readouts fail to leverage some of the advantages that the organoid systems provide. Consequently, phenotypically evaluating complex 3D cell culture models remains difficult due to intra- and inter-patient organoid size differences, cellular heterogeneities, and temporal response dynamics. Here, we present an image-based high-content assay that provides object level information on 3D patient-derived tumor organoids without the need for vital dyes. Leveraging computer vision, we segment and define organoids as independent regions of interest and obtain morphometric and textural information per organoid. By acquiring brightfield images at different timepoints in a robust, non-destructive manner, we can track the dynamic response of individual organoids to various drugs. Furthermore, to simplify the analysis of the resulting large, complex data files, we developed a web-based data visualization tool, the Organoizer, that is available for public use. Our work demonstrates the feasibility and utility of using imaging, computer vision and machine learning to determine the vital status of individual patient-derived organoids without relying upon vital dyes, thus taking advantage of the characteristics offered by this preclinical model system.