Polatli, E. | bioRxiv Science

bioRxiv2025

BrAIn: A comprehensive artificial intelligence-based morphology analysis system for brain organoids and neuroscience

Human-induced pluripotent stem cells (iPSCs) offer transformative potential for biomedical research, with iPSC-derived organoids providing more physiologically relevant models than traditional 2D cell cultures. Among these, brain organoids are particularly valuable for drug screening, disease modeling, and investigations into molecular pathways. Accurate representation of brain morphology is critical, as more complex organoid structures better mimic the human brain. Deep learning (DL) and machine learning (ML) approaches have become integral to analyzing organoid morphology, yet tools for comprehensive, time-resolved assessments are scarce. Here, we introduce BrAIn, a DL-based application for analyzing the developmental progression of brain organoids. BrAIn tracks their evolution from embryoid bodies and quantifies parameters including area, Feret diameter, perimeter, roundness, and circularity. It also classifies budding and abnormal morphologies of 3D organoids and detects monolayer neural rosette structures, key features of neuronal differentiation. Designed with accessibility in mind, BrAIn provides a no-code interface, enabling researchers of all technical backgrounds to conduct advanced morphological analyses with ease. Our study demonstrates the application of BrAIn to evaluate the effects of different growth conditions - static, orbital shaker, and microfluidic chip-based - on brain organoid development. Orbital shaker cultures resulted in the largest organoids, while chip-based systems achieved more homogeneous growth. Both conditions produced organoids with greater morphological complexity compared to static culture. BrAIn emerges as a robust, user-friendly tool to quantify brain organoid development and explore how versatile growth conditions influence their morphology and maturation.

bioengineering↗

bioRxiv2024

OrganoLabeling: Quick and accurate annotation tool for organoid images

Organoids are self-assembled 3D cellular structures that resemble organs structurally and functionally, providing in vitro platforms for molecular and therapeutic studies. Generation of organoids from human cells often require long and costly procedures with arguably low efficiency. Prediction and selection of cellular aggregates that result in healthy and functional organoids can be achieved using artificial intelligence-based tools. Transforming images of 3D cellular constructs into digitally processible datasets for training deep learning models require labeling of morphological boundaries, which often is performed manually. Here we report an application named OrganoLabeling, which can create large image-based datasets in consistent, reliable, fast, and user-friendly manner. OrganoLabeling can create segmented versions of images with combinations of contrast adjusting, K-means clustering, CLAHE, binary and Otsu thresholding methods. We created embryoid body and brain organoid datasets, of which segmented images were manually created by human researchers and compared with OrganoLabeling. Validation is performed by training U-Net models, which are deep learning models specialized in image segmentation. U-Net models, that are trained with images segmented by OrganoLabeling, achieved similar or better segmentation accuracies than the ones trained with manually labeled reference images. OrganoLabeling can replace manual labeling, providing faster and more accurate results for organoid research free of charge. Translational ImpactWe developed an image processing-based tool called OrganoLabeling generating datasets to train deep learning models and achieved its performance by comparing with experienced researchers. Here we demonstrate and validate OrganoLabeling, a tool that is as fast and successful as humans, automating the process of creating datasets for use in training deep learning models that can be used for disease analysis and translational purposes in medicine. OrganoLabeling can be broadly applied in artificial intelligence engaged life sciences focusing on stem cell based organoid research. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/589852v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@c2ba73org.highwire.dtl.DTLVardef@5f06acorg.highwire.dtl.DTLVardef@af369borg.highwire.dtl.DTLVardef@12a1315_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗