A New Way to Morphological Cell Profiling Using IKOSA’s Cell Painting Assay

26 Jul, 2023 | IKOSA AI, Use case

The Cell Painting Assay provides researchers with a powerful tool for morphological cell profiling, enabling investigations into cellular behavior and the impact of compounds on cellular structures.

In this article, we explore the principles and methodology of the Cell Painting Assay, highlighting its significance in drug discovery and understanding disease mechanisms. By unlocking valuable insights into cellular morphology, this image-based assay opens new avenues for biomedical research and discovery.

Moreover, discover how to optimize your Cell Painting image analysis process by harnessing innovative AI technology. We present a novel method for automated morphological cell profiling with the help of the IKOSA Platform.

The Cell Painting Assay: A Powerful Tool for Drug Discovery 

The Cell Painting method is a high-content imaging technique used in cell biology and drug discovery. It involves systematic staining of cellular components using a combination of fluorescent dyes to visualize various subcellular structures and organelles.

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The goal of Cell Painting is to capture detailed information about cellular morphology and subcellular organization to gain insights into cellular behavior and response to external stimuli. (Bray et al., 2016)

The Cell Painting process typically involves labeling cells with a set of fluorescent dyes that selectively bind to different cellular structures, such as the nucleus, cytoplasm, and specific organelles. These dyes emit distinct fluorescent signals, enabling the visualization of multiple cellular components simultaneously. The stained cells are then imaged using fluorescence microscopy, capturing detailed images of the labeled structures. (Bray et al., 2016; Bray et al., 2017)

Once the images are acquired, sophisticated image analysis algorithms and computational tools are employed to analyze the Cell Painting data. The analysis includes image segmentation, where individual cells are identified and their boundaries delineated. Subsequently, various morphological features, such as shape, size, texture, and intensity, are extracted from the segmented cells. The collection of these characteristics forms what is known as a cellular profile, which encompasses quantitative measurements that are valuable for phenotypic screening and evaluating the impacts of perturbations, such as drug treatments or genetic modifications. (Bray et al., 2016; Bray et al., 2017)

Cell Painting offers a generalized approach for morphological cell profiling, meaning it does not rely on specific molecular markers or targets. Instead, it captures a comprehensive snapshot of cellular components through the combination of different fluorescent dyes. This enables researchers to explore multiple aspects of cellular biology simultaneously, providing a holistic view of cellular function and response.  (Bray et al., 2016; Bray et al., 2017)

The Cell Painting technique has gained popularity due to its ability to generate rich and quantitative data about cellular morphology and subcellular organization. It has been applied in various areas of research, including drug discovery, toxicology studies, and understanding disease mechanisms. By profiling cells using Cell Painting, researchers can gain valuable insights into mechanisms of action, identify potential therapeutic targets, and make informed decisions in developing new drugs and therapies. (Bray et al., 2016; Bray et al., 2017)

Use our new app to automatically extract single-cell morphological features from complex microscopy images.

What is Morphological Cell Profiling?

Morphological cell profiling refers to the systematic analysis and characterization of cellular morphology, which includes the shape, size, texture, and spatial organization of cells and their subcellular structures. This approach involves capturing detailed information about cellular features using imaging techniques, such as high-resolution microscopy, and extracting quantitative measurements to describe the morphological characteristics of single cells or cell populations. (Bray et al., 2016; Bray et al., 2017)

Morphological cell profiling plays a crucial role in understanding cellular behavior, elucidating the effects of perturbations (e.g., drug treatments, genetic modifications), and identifying phenotypic changes associated with various biological processes, diseases, or experimental conditions. By quantifying a very large set of morphological features in an unbiased manner, researchers can gain insights into the underlying mechanisms driving cellular responses and identify potential therapeutic targets.(Bray et al., 2016; Bray et al., 2017)

The emergence of high-content imaging and automated analysis methods has made it possible to conduct extensive morphological cell profiling on a large scale, allowing the analysis of numerous cells, ranging from thousands to millions, in a high-throughput fashion. By leveraging advanced image analysis algorithms, machine learning, and computational approaches, researchers can extract valuable information from complex imaging datasets, enabling comprehensive characterization and comparison of cellular phenotypes. (Bray et al., 2016)

Morphological cell profiling is widely used in various fields, including:

  • drug discovery,
  • toxicology,
  • cancer research,
  • developmental biology, and
  • regenerative medicine.

It provides a holistic view of cellular structures and their alterations, aiding in identifying biomarkers, evaluating compound effects, classifying cell types, and understanding the complex interplay between cellular components. (Bray et al., 2016; Bray et al., 2017)

It is important to distinguish morphological profiling from conventional screening assays, as they differ in their scope and approach. While conventional assays focus on quantifying a limited set of predetermined features that are known to be associated with specific biological aspects of interest, morphological profiling takes a broader approach allowing for a more generalizable method that can be applied across different scenarios. (Bray et al., 2016)

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Figure 2: Morphological Profiling vs. Conventional Assays: A Comparative Approach.

By adopting an unbiased approach, morphological profiling offers the opportunity for discovery without being limited by existing knowledge or preconceived notions. It enables researchers to explore a multitude of biological processes or diseases of interest within a single experiment. This broad applicability holds the potential for increased efficiency and resource utilization, as one dataset can yield valuable insights into multiple areas of research. (Bray et al., 2016; Bray et al., 2017)

While various high-content screening methods exist for generating comprehensive profiles of biological samples, such as metabolomic or proteomic profiling, Bray et al. (2016) argue that gene expression profiling is presently the sole practical alternative to image-based morphological profiling in terms of throughput and efficiency. However, gene expression profiling is limited to aggregating cell populations and cannot be performed at the single-cell level, unlike morphological profiling, which offers the advantage of obtaining profiles at the individual cell level. This capability enhances the ability to detect changes in subpopulations of cells. (Bray et al., 2016)

Fundamentally, morphological profiling offers a versatile and all-encompassing approach that transcends the limitations of pre-existing knowledge. It empowers researchers to unveil novel connections, identify unforeseen correlations, and attain a comprehensive understanding of cellular attributes. By embracing this unbiased methodology, researchers can fully harness the potential of their experimental data and explore uncharted paths of scientific inquiry.

The Advantages of Using the Cell Painting Method

The Cell Painting Method offers several advantages over traditional techniques in image analysis, making it a valuable tool for unbiased information gathering in various research applications. 

First and foremost, the Cell Painting Method stands out as an inexpensive technique compared to other approaches in image analysis. Using common fluorescent dyes and stains eliminates the need for costly reagents and specialized equipment, making it accessible to a broader range of researchers and institutions. (Bray et al., 2016; Bray et al., 2017) 

Furthermore, it excels in providing rich information about cellular components and their spatial organization. Through the use of multiple fluorescent dyes, it captures comprehensive data on various subcellular structures, including nuclei, cytoplasm, and organelles. This holistic approach enables researchers to gain a more comprehensive understanding of cellular behavior and dynamics. In traditional analysis techniques, researchers often rely on specific targets to study cellular components or processes. This may require the use of specific antibodies or probes that bind to the desired targets, enabling their detection and visualization. However, this approach limits the analysis to a small number of predetermined analytes, as each marker is designed to detect a specific molecule or structure. (Bray et al., 2016)

In contrast, the Cell Painting Method relies on the inherent properties and characteristics of the cellular structures themselves, such as their morphology, texture, and spatial distribution. It provides a more holistic view of the cell, capturing a wide range of information beyond the low number of analytes targeted by traditional marker-based techniques. As a result, researchers can explore diverse cellular characteristics and uncover novel insights without being limited by predefined markers or targets. (Bray et al., 2016; Bray et al., 2017)

Another advantage of the Cell Painting Method lies in its efficient utilization of a limited number of imaging channels. Leveraging a small set of fluorescent dyes minimizes the complexity of image acquisition and analysis. This streamlined approach reduces potential technical challenges, such as spectral overlap or photobleaching, while still providing valuable information for comprehensive cellular profiling.

Ensure unbiased measurement of cellular behavior on our codeless IKOSA platform.

Benefits of AI Technology in the Analysis of Cell Painting Image Data

Leveraging the power of artificial intelligence and automated analysis algorithms enables researchers to extract valuable information from large-scale image data. Among these techniques, Cell Painting image analysis solutions have emerged as a powerful tool for comprehensive cell profiling. By automating the extraction of single-cell morphological features from complex microscopy images, this approach facilitates an unbiased and effective way for the measurement of cellular dynamics. (Bray et al., 2016)

Automated Analysis with Image Segmentation

One key aspect of image-based cell profiling is image segmentation, the process of partitioning an image into meaningful regions. Cell Painting analysis solutions utilize advanced image segmentation algorithms to accurately identify individual cells within a heterogeneous population. This automated process ensures precise delineation of cellular boundaries and enables subsequent analysis at the single-cell level.

Comprehensive Morphological Profiling

The strength of the Cell Painting approach lies in its ability to perform automated feature extraction and quantify a wide range of morphological characteristics. By analyzing multiple parameters, including shape, size, intensity, texture, and measurements of adjacency between cellular structures, the software generates morphological profiling data that provides a detailed description of cellular properties.

Transfer learning for improved generalizability

Integrating transfer learning techniques can enhance the generalizability of image-based cell profiling. By using pre-trained models and knowledge gained from one dataset, these approaches enable the efficient adaptation of the learned features to new datasets and experimental conditions. This transfer learning capability allows researchers to leverage existing knowledge and models, reducing the need for extensive data collection and accelerating the analysis process and robustness of the trained model.

Efficiency and Scalability

AI-driven software offers an efficient and scalable solution for analyzing large-scale microscopy datasets. By automating the extraction of morphological features from thousands or even millions of cells, it significantly reduces the time and effort required for data analysis. This enables researchers to uncover patterns and make discoveries at an unprecedented scale, accelerating scientific progress in fields such as drug discovery and personalized medicine.

Learn more about the efficient AI-guided workflow for analyzing Cell Painting image data in IKOSA.

Effective Strategies for the Evaluation of Cell Profiling Data

Evaluation plays a crucial role in the success of cell profiling techniques. To ensure the accuracy and reliability of the obtained results, effective strategies for data evaluation are essential. Here, we discuss some key approaches that can be employed for the evaluation of cell profiling data:

Comparison with Ground Truth

One of the fundamental strategies is comparing the cell profiling results with a ground truth dataset. This involves validating the identified cells and their morphological features against manually annotated or expert-verified data. By quantitatively measuring the agreement between the automated analysis and the ground truth, researchers can assess the accuracy and performance of the cell profiling algorithms.

Quality Control Metrics

Incorporating quality control metrics is essential to ensure the reliability of cell profiling data. These metrics assess various aspects of the image acquisition and analysis process, such as image quality, segmentation accuracy, and feature consistency. By monitoring and evaluating these metrics, researchers can identify and address potential sources of variability or bias, leading to more robust and reproducible results.

Benchmarking and Comparative Analysis

Benchmarking cell profiling methods against established standards or competing approaches is a valuable strategy to evaluate their performance. By comparing the performance metrics, computational efficiency, and accuracy of different algorithms or software tools, researchers can make informed decisions about the most suitable approach for their specific research objectives. Comparative analysis enables the identification of strengths, weaknesses, and areas for improvement in cell profiling methodologies.

Validation with Independent Datasets

Validating cell profiling results using independent datasets provides an additional layer of confidence in the findings. By applying the developed models or algorithms to new datasets, researchers can assess the generalizability and robustness of their approaches across different experimental conditions, imaging platforms, or biological systems. This validation step ensures that the cell profiling techniques perform consistently and reliably beyond the training dataset.

Statistical Analysis

Statistical methods are commonly employed to analyze and interpret cell profiling data. These techniques enable researchers to identify significant differences between experimental groups, evaluate the variability within samples, and determine the statistical significance of observed changes in morphological features. Caicedo et al. provide a very comprehensive overview of Data-analysis strategies for image-based cell profiling.

The development of modern devices, equipped with multiprocessing capabilities, automation, and robotics, has enabled researchers to rapidly capture a substantial number of samples. This has significantly improved efficiency and productivity in screening experiments. Unlike earlier technologies that sacrificed resolution or content during accelerated processing, contemporary devices empower researchers to capture a vast amount of data with high quality. This revolution in high-content screening devices has created a pressing need to efficiently detect relevant images from massive datasets, presenting challenges in accelerating and automating the analysis processes.

Ensure the accuracy and validity of your research by relying on high quality Cell Paining Assay results.

Presenting our Approach to Automated Morphological Cell Profiling

To tackle the present challenges, we propose an efficient AI-driven workflow to analyze Cell Painting image data with the help of the IKOSA software. The App for morphological cell profiling is a product of our collaborative efforts with the Dutch company Core Life Analytics, a provider of downstream analytics tools for high-content data. As of the publication of this article, the app is in its final stages of development.

📢 Attention all! 📢 We’re excited to share additional details about this exciting project with you. For a deeper dive into the information, head over to our scientific poster that we presented at SLAS Belgium in May 2023.

Congratulations to Bendegúz H. Zováthi, an international Master’s student in Image Processing and Computer Vision, from Pázámány Péter Catholic University (PPCU), University Autónoma de Madrid (UAM), and University of Bordeaux (UBx) for the successful completion of his Thesis titled “Morphological cell profiling by segmentation-based feature extraction.” During his internship at our company, KML Vision GmbH, Bendegúz significantly contributed to the continuous development of our IKOSA Cell Painting App. His dedication and expertise have been invaluable, and we are thrilled to have been part of his academic journey.

Materials and Methods

We offer a comprehensive overview of the established methods utilized to prepare the Cell Painting image data and construct a resilient and highly efficient image analysis solution based on image segmentation and feature extraction. The dataset employed in the ongoing development of the IKOSA Cell Painting App has been rigorously validated by experts and made available by the JUMP-Cell Painting Consortium. This dataset, which is publicly accessible, provides immense value for a diverse array of applications.


For our ongoing App development, we utilized a carefully selected subset of the CPG0000-jump-pilot dataset from the Cell Painting Gallery. This subset served as both training data and a benchmark for our project. This publicly available image database involves profiling A549 and U2OS cells at various time points. The A549 cell line originates from lung carcinoma epithelial cells obtained in 1972. The U2OS cell line, on the other hand, is an epithelial morphology cell line established in 1964. In 2022, Chandrasekaran et al. introduced the CPG0000-jump-pilot dataset, which serves as a valuable reference for evaluating methods, predicting compound similarities, and assessing perturbation effects. The authors emphasize in their publication that this carefully compiled and well-annotated dataset is intended to accelerate the advancement of novel medicines and therapies.

Figure 3: Representative fluorescence image of the CPG0000 dataset.

The CPG0000-jump-pilot database consists of all the necessary files and information for image analysis including the CellProfiler pipeline utilized for processing. It encompasses fluorescence microscopy images as presented in Figure 3 and corresponding outputs from the Cell Painting Assay. These outputs include segmentation outlines (in PNG format), extracted features (in CSV format), and associated metadata (in both CSV and TXT formats). Apart from the fluorescent channels, this database incorporates brightfield images that offer insights into cell morphology and texture.

The dataset consists of uncompressed 16-bit TIFF files, offering a resolution of 1,080×1,080 pixels. To label different cellular compartments or structures, five distinct fluorescent dyes were used, as outlined in Table.

DyeOrganelle or cellular component
Hoechst 33342Nucleus (DNA)
Concanavalin A/Alexa Fluor488 conjugateEndoplasmic reticulum (ER)
SYTO 14 green fluorescent nucleic acid stainNucleoli, cytoplasmic RNA (RNA)
Phalloidin/Alexa Fluor 568 conjugate, wheat germ agglutinin (WGA)/Alexa Fluor 555 conjugateF-actin cytoskeleton, Golgi, plasma membrane (AGP)
MitoTracker Deep RedMitochondria (Mito)
Table: Fluorescent dyes used in the cell painting protocol.
Figure 4: Presentation of multichannel settings in the Cell Painting project.

Quality Control Metadata

The careful selection of training data plays a crucial role in the development of deep learning applications, as it directly impacts the performance and accuracy of the model. High-quality data is essential to ensure the reliability of the resulting model, whereas noisy, incomplete, or damaged data can compromise its effectiveness. The Cell Painting Gallery is a huge dataset carefully generated by the corporation of leading pharma companies and experts.

In collaboration with our partner Core Life Analytics, we aimed to capture the maximum phenotypic diversity within the available data for our Cell Painting model development. Therefore, we carefully selected a diverse and representative subset consisting of 2780 images, totaling approximately 90 GB in size. This subset was chosen to ensure comprehensive coverage of different cellular phenotypes for optimal model training.

App Development and Image Analysis

The development phase of the App can be conceptualized as a two-step process, involving image segmentation and feature extraction. During segmentation, the App identifies and delineates individual cells and their nuclei. The subsequent feature extraction measures morphological features for each cellular compartment. The IKOSA deep neural network is trained to perform accurate nuclei and cell segmentation using the segmentation outlines generated by CellProfiler as the ground-truth annotations (Figure 5).

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All data used underwent completeness and consistency checks to ensure clean and high-quality data inputs.

CellProfiler uses image processing algorithms, where the segmentation is executed on low-level features (for example thresholding based on intensity level). One notable contrast is that our approach utilizes deep learning, where the model is specifically designed to autonomously learn the relevant features from the input data. This deep learning-based segmentation relies on more complex and generalized features, resulting in a more robust and resilient model. Deep learning enables the model to adapt and capture intricate patterns and variations present in the data. This approach offers greater flexibility and adaptability, ultimately enhancing the accuracy and performance of the segmentation process.

To validate the results obtained from the trained model, a comparison was conducted with the widely acknowledged CellPofiler pipeline, which is considered state-of-the-art. This comparison aimed to assess the performance and effectiveness in accurately delineating cells and capturing cellular features.

Figure 5: Representative example of object annotation in IKOSA to train the cell painting application.

Get in touch to explore the future of morphological cell analysis with IKOSA.


To assess the performance of the trained model, a comprehensive evaluation of the segmentation output is conducted. This evaluation utilizes both qualitative and quantitative measures to analyze the results. Furthermore, a thorough comparison of the extracted features with the state-of-the-art JUMP-CP pilot dataset is performed, providing a comprehensive analysis of the algorithm’s performance.

Segmentation Evaluation

The instance segmentation model is trained using 8 imaging channels (5 fluorescence, 3 brightfield). The training data comprises a total of 2,208 images with 215,732 nucleus labels and 231,501 cell labels. The validation data consists of 572 images with 58,290 nucleus annotations and 62,560 cell annotations.

The model demonstrates efficient performance by producing predictions for a 1,080×1,080 pixel-sized image in just 2.2 seconds. The quantitative evaluation, as presented in Figure 6, indicates that the model achieves a high level of accuracy in precisely identifying instances. It also exhibits only a low rate of False Positives, indicating its effectiveness in distinguishing instances.

Figure 6: Quantitative evaluation of the trained model.

For a comprehensive understanding of the performance metrics employed in this context, we recommend checking our Knowledge Base, where you can find detailed explanations and interpretations of these metrics.

In the qualitative evaluation, Figure 7 showcases precise nucleus segmentation outputs, surpassing the ground truth data obtained from CellProfiler. The model demonstrates the ability to accurately segment instances, even in challenging scenarios like cell accumulation. The reported False Positive detections on the right side of the image are actually True Positive predictions, whereas the ground truth annotations, in this case, were incorrect (cluster of four cells). The model’s cell segmentation results were improved by utilizing nucleus segmentation to separate cell instances, as depicted in Figure 8.

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The model demonstrates the ability to accurately segment instances, even in challenging scenarios like cell accumulation.

However, it should be noted that while this approach is not able to achieve perfect detection of all objects (which is barely the case in bioimage analysis), it only results in a small number of False Negative detections and shows a really good overall outcome.

Further, it is important to emphasize that the ground-truth labels used in the training data are not perfect or entirely error-free. However, despite the imperfections in the ground-truth data, the trained model is able to surpass the performance of the segmentation results obtained from CellProfiler. This outcome highlights the superiority of the deep learning approach in terms of accuracy and robustness. The model’s ability to outperform the imperfect ground-truth data indicates its capability to learn and generalize features effectively, leading to improved segmentation results.

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However, despite the imperfections in the ground-truth data, the trained model is able to surpass the performance of the segmentation results obtained from CellProfiler. This outcome highlights the superiority of the deep learning approach in terms of accuracy and robustness. The model’s ability to outperform the imperfect ground-truth data indicates its capability to learn and generalize features effectively, leading to improved segmentation results.

Additionally, it is worth mentioning that we also trained a segmentation model using only 5 channels, which yielded comparable accuracy to the model trained with 8 channels. Surprisingly, the inclusion of brightfield images in the 8-channel model did not have a significant impact on the overall performance of the model. This observation suggests that the additional information provided by the brightfield images may not be crucial for achieving high segmentation accuracy in our specific case.

Figure 7: IKOSA nucleus segmentation compared to ground truth data.
Figure 8: IKOSA cell segmentation compared to ground truth data.

Comparison of extracted features

To ensure an information-rich morphological profile of the cell, it is recommended to utilize as many channels as possible. This approach provides comprehensive outputs of object feature groups, including area and shape, correlation, granularity, intensity, location, neighbor, radial distribution, and texture.

In order to compare the extracted features from our model with CellProfiler, the normalized mean squared error (MSE) and mean absolute error (MAE) were calculated across each object.

Did you know?

Mean Squared Error (MSE): MSE measures the amount of error in statistical models. It assesses the average squared difference between the observed and predicted values. When a model has no error, the MSE equals zero. As model error increases, its value increases.

Mean Absolute Error (MAE): Absolute Error is the amount of error in your measurements. It is the difference between the measured value and “true” value. For example, if a scale states 90 pounds but you know your true weight is 89 pounds, then the scale has an absolute error of 90 lbs – 89 lbs = 1 lbs.

The overall results for the MSE indicate a value of 0.011, while the MAE shows a value of 0.029. These results provide a measure of the dissimilarity between the extracted features from our model and those obtained using CellProfiler, demonstrating the effectiveness of our approach.

Benefits you can expect using the IKOSA Cell Painting App* over CellProfiler

*Please note that the app is still under development and not yet publicly available. 

User-friendly: The IKOSA Cell Painting App available on the IKOSA platform enables users to execute assays without the need for programming skills or specialized hardware. Also, its browser-based approach ensures accessibility and flexibility, allowing users to conveniently conduct Cell Painting analysis from any computer with an internet connection.

Fast and automated image analysis: Configuring complex image analysis pipelines can be a time-consuming process, often taking several hours, even for experts in the field. With the IKOSA Cell Painting App, this lengthy configuration is eliminated. The App offers a novel approach to image analysis, enabling rapid feature extraction from raw image data, automating and streamlining the analysis process, and saving valuable time and resources.

Robustness and performance: The IKOSA Cell Painting App employs a computer vision approach that ensures robust data analysis and high performance, overcoming the challenges associated with conventional threshold-based algorithms.

Transferability and Reusability: The trained model can be transferred and retrained on other datasets. This flexibility enables its capabilities to be applied to diverse experimental setups, enhancing the analysis across a wide range of projects.

Collaboration with Core Life Analytics: The App’s functionality is enhanced through compatibility with the StratoMineR software. This collaboration enables optimized feature selection without losing any relevant information, providing researchers with an interpretable and efficient analysis workflow.

Resource efficiency: The App’s high-throughput capabilities, combined with its automated analysis contribute to more efficient screening processes and data-driven decision-making. And all this without investing in additional IT infrastructure.

Dedicated support and maintenance: While open-source software relies on community support for maintenance and updates, the IKOSA Cell Painting App offers dedicated support and ongoing maintenance from our development team. This ensures prompt bug fixes, updates, and improvements, providing users with a more reliable and supported platform for their image analysis needs.

Annotation preparation for the Cell Painting App training in IKOSA
Figure 9: Annotation preparation for the Cell Painting App training in IKOSA.

Reach out to us and learn more about our upcoming IKOSA Cell Painting App.


The ongoing development process of the IKOSA Cell Painting App commences and continues with a comprehensive understanding of the requirements and challenges associated with Cell Painting image analysis. In-depth research was conducted to identify shortcomings in existing tools and explore novel approaches. Subsequently, the conceptualization and design phase took place, outlining the core features and workflow of the App.

Developing accurate and efficient algorithms by employing advanced computer vision techniques and deep learning models is crucial. Extensive testing and optimization are performed to enhance algorithm performance and minimize computational requirements.

Thorough testing and quality assurance procedures have been implemented to guarantee the stability, accuracy, and reliability of the App. Continuous maintenance, updates, and feedback collection are in place to facilitate ongoing improvements that address evolving research requirements.

It is crucial to recognize that the “ground truth” data used in the developing model is derived from the Cell Painting Gallery data, including outlines and pixel coordinates. During the data preprocessing stage, the segmentation outlines have been converted into masks, which may introduce slight variations or differences compared to the original outlines. It is vital to recognize that the term “ground truth” in this context does not imply an absolute representation of the actual ground truth. Instead, it refers to data generated by another image analysis software (CellProfiler). Although the data has undergone meticulous validation by experts, it is essential to acknowledge that absolute correctness cannot always be guaranteed due to the complexities and nuances inherent in image analysis.

The forthcoming IKOSA Cell Painting Application is expected to be the most robust and generalizable solution on the market. With this versatile approach to morphological profiling, we aim to provide users with a tool that has a substantial positive influence on drug discovery and personalized medicine.

Our authors:

KML Vision Team Benjamin Obexer Lead Content Writer

Benjamin Obexer

Lead content writer, life science professional, and simply a passionate person about technology in healthcare

KML Vision Team Elisa Opriessnig Content writer

Elisa Opriessnig

Content writer focused on the technological advancements in healthcare such as digital health literacy and telemedicine.

KML Vision Team Fanny Dobrenova Marketing Specialist

Fanny Dobrenova

Health communications and marketing expert dedicated to delivering the latest topics in life science technology to healthcare professionals.


Bray, MA., Singh, S., Han, H. et al. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat Protoc 11, 1757–1774 (2016). https://doi.org/10.1038/nprot.2016.105

Caicedo, J., Cooper, S., Heigwer, F. et al. Data-analysis strategies for image-based cell profiling. Nat Methods 14, 849–863 (2017). https://doi.org/10.1038/nmeth.4397

Mark-Anthony Bray and others, A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay, GigaScience, Volume 6, Issue 12, December 2017, giw014, https://doi.org/10.1093/gigascience/giw014


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