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Boyang Zhang - One of the best experts on this subject based on the ideXlab platform.
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Correction: Deep-LUMEN assay - human lung epithelial spheroid classification from Brightfield images using deep learning.
Lab on a chip, 2020Co-Authors: Lyan Abdul, Shravanthi Rajasekar, Dawn S Y Lin, Sibi Venkatasubramania Raja, Alexander Sotra, Yuhang Feng, Amy Liu, Boyang ZhangAbstract:Correction for 'Deep-LUMEN assay - human lung epithelial spheroid classification from Brightfield images using deep learning' by Lyan Abdul et al., Lab Chip, 2020, DOI: .
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Deep-LUMEN assay – human lung epithelial spheroid classification from Brightfield images using deep learning
Lab on a chip, 2020Co-Authors: Lyan Abdul, Shravanthi Rajasekar, Dawn S Y Lin, Alexander Sotra, Yuhang Feng, Amy Liu, Sibi Venkatasubramania Raja, Boyang ZhangAbstract:Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. In contrast to 2D monolayer culture, the characterization of 3D tissue models from non-invasive Brightfield images is a significant challenge. To address this issue, here we report a deep-learning uncovered measurement of epithelial networks (Deep-LUMEN) assay. Deep-LUMEN is an object detection algorithm that has been fine-tuned to automatically uncover subtle differences in epithelial spheroid morphology from Brightfield images. This algorithm can track changes in the luminal structure of tissue spheroids and distinguish between polarized and non-polarized lung epithelial spheroids. The Deep-LUMEN assay was validated by screening for changes in spheroid epithelial architecture in response to different extracellular matrices and drug treatments. Specifically, we found the dose-dependent toxicity of cyclosporin can be underestimated if the effect of the drug on tissue morphology is not considered. Hence, Deep-LUMEN could be used to assess drug effects and capture morphological changes in 3D spheroid models in a non-invasive manner.
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correction deep lumen assay human lung epithelial spheroid classification from Brightfield images using deep learning
Lab on a Chip, 2020Co-Authors: Lyan Abdul, Shravanthi Rajasekar, Dawn S Y Lin, Alexander Sotra, Yuhang Feng, Amy Liu, Sibi Venkatasubramania Raja, Boyang ZhangAbstract:Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. In contrast to 2D monolayer culture, the characterization of 3D tissue models from non-invasive Brightfield images is a significant challenge. To address this issue, here we report a deep-learning uncovered measurement of epithelial networks (Deep-LUMEN) assay. Deep-LUMEN is an object detection algorithm that has been fine-tuned to automatically uncover subtle differences in epithelial spheroid morphology from Brightfield images. This algorithm can track changes in the luminal structure of tissue spheroids and distinguish between polarized and non-polarized lung epithelial spheroids. The Deep-LUMEN assay was validated by screening for changes in spheroid epithelial architecture in response to different extracellular matrices and drug treatments. Specifically, we found the dose-dependent toxicity of cyclosporin can be underestimated if the effect of the drug on tissue morphology is not considered. Hence, Deep-LUMEN could be used to assess drug effects and capture morphological changes in 3D spheroid models in a non-invasive manner.
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Deep-LUMEN Assay – Human lung epithelial spheroid classification from Brightfield images using deep learning
2020Co-Authors: Lyan Abdul, Shravanthi Rajasekar, Dawn S Y Lin, Alexander Sotra, Yuhang Feng, Amy Liu, Sibi Venkatasubramania Raja, Boyang ZhangAbstract:Abstract Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. However, different from 2D monolayer culture, the characterization of 3D tissue models from non-invasive Brightfield images is a significant challenge. To address this issue, here we report a Deep-Learning Uncovered Measurement of Epithelial Networks (Deep-LUMEN) assay. Deep-LUMEN is an object detection algorithm that has been fine-tuned to automatically uncover subtle differences in epithelial spheroid morphology from Brightfield images. This algorithm can track changes in the luminal structure of tissue spheroids and distinguish between polarized and non-polarized lung epithelial spheroids. The Deep-LUMEN assay was validated by screening for changes in spheroid epithelial architecture in response to different extracellular matrices and drug treatments. Specifically, we found the dose-dependent toxicity of Cyclosporin can be underestimated if the effect of the drug on tissue morphology is not considered. Hence, Deep-LUMEN could be used to assess drug effects and capture morphological changes in 3D spheroid models in a non-invasive manner. Significance of the work Deep learning has been applied for the first time to autonomously detect subtle morphological changes in 3D multi-cellular spheroids, such as spheroid polarity, from Brightfield images in a label-free manner. The technique has been validated by detecting changes in spheroid morphology in response to changes in extracellular matrices and drug treatments.
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deep lumen assay human lung epithelial spheroid classification from Brightfield images using deep learning
bioRxiv, 2020Co-Authors: Lyan Abdul, Shravanthi Rajasekar, Dawn S Y Lin, Alexander Sotra, Yuhang Feng, Amy Liu, Sibi Venkatasubramania Raja, Boyang ZhangAbstract:Abstract Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. However, different from 2D monolayer culture, the characterization of 3D tissue models from non-invasive Brightfield images is a significant challenge. To address this issue, here we report a Deep-Learning Uncovered Measurement of Epithelial Networks (Deep-LUMEN) assay. Deep-LUMEN is an object detection algorithm that has been fine-tuned to automatically uncover subtle differences in epithelial spheroid morphology from Brightfield images. This algorithm can track changes in the luminal structure of tissue spheroids and distinguish between polarized and non-polarized lung epithelial spheroids. The Deep-LUMEN assay was validated by screening for changes in spheroid epithelial architecture in response to different extracellular matrices and drug treatments. Specifically, we found the dose-dependent toxicity of Cyclosporin can be underestimated if the effect of the drug on tissue morphology is not considered. Hence, Deep-LUMEN could be used to assess drug effects and capture morphological changes in 3D spheroid models in a non-invasive manner. Significance of the work Deep learning has been applied for the first time to autonomously detect subtle morphological changes in 3D multi-cellular spheroids, such as spheroid polarity, from Brightfield images in a label-free manner. The technique has been validated by detecting changes in spheroid morphology in response to changes in extracellular matrices and drug treatments.
Lyan Abdul - One of the best experts on this subject based on the ideXlab platform.
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Correction: Deep-LUMEN assay - human lung epithelial spheroid classification from Brightfield images using deep learning.
Lab on a chip, 2020Co-Authors: Lyan Abdul, Shravanthi Rajasekar, Dawn S Y Lin, Sibi Venkatasubramania Raja, Alexander Sotra, Yuhang Feng, Amy Liu, Boyang ZhangAbstract:Correction for 'Deep-LUMEN assay - human lung epithelial spheroid classification from Brightfield images using deep learning' by Lyan Abdul et al., Lab Chip, 2020, DOI: .
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Deep-LUMEN assay – human lung epithelial spheroid classification from Brightfield images using deep learning
Lab on a chip, 2020Co-Authors: Lyan Abdul, Shravanthi Rajasekar, Dawn S Y Lin, Alexander Sotra, Yuhang Feng, Amy Liu, Sibi Venkatasubramania Raja, Boyang ZhangAbstract:Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. In contrast to 2D monolayer culture, the characterization of 3D tissue models from non-invasive Brightfield images is a significant challenge. To address this issue, here we report a deep-learning uncovered measurement of epithelial networks (Deep-LUMEN) assay. Deep-LUMEN is an object detection algorithm that has been fine-tuned to automatically uncover subtle differences in epithelial spheroid morphology from Brightfield images. This algorithm can track changes in the luminal structure of tissue spheroids and distinguish between polarized and non-polarized lung epithelial spheroids. The Deep-LUMEN assay was validated by screening for changes in spheroid epithelial architecture in response to different extracellular matrices and drug treatments. Specifically, we found the dose-dependent toxicity of cyclosporin can be underestimated if the effect of the drug on tissue morphology is not considered. Hence, Deep-LUMEN could be used to assess drug effects and capture morphological changes in 3D spheroid models in a non-invasive manner.
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correction deep lumen assay human lung epithelial spheroid classification from Brightfield images using deep learning
Lab on a Chip, 2020Co-Authors: Lyan Abdul, Shravanthi Rajasekar, Dawn S Y Lin, Alexander Sotra, Yuhang Feng, Amy Liu, Sibi Venkatasubramania Raja, Boyang ZhangAbstract:Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. In contrast to 2D monolayer culture, the characterization of 3D tissue models from non-invasive Brightfield images is a significant challenge. To address this issue, here we report a deep-learning uncovered measurement of epithelial networks (Deep-LUMEN) assay. Deep-LUMEN is an object detection algorithm that has been fine-tuned to automatically uncover subtle differences in epithelial spheroid morphology from Brightfield images. This algorithm can track changes in the luminal structure of tissue spheroids and distinguish between polarized and non-polarized lung epithelial spheroids. The Deep-LUMEN assay was validated by screening for changes in spheroid epithelial architecture in response to different extracellular matrices and drug treatments. Specifically, we found the dose-dependent toxicity of cyclosporin can be underestimated if the effect of the drug on tissue morphology is not considered. Hence, Deep-LUMEN could be used to assess drug effects and capture morphological changes in 3D spheroid models in a non-invasive manner.
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Deep-LUMEN Assay – Human lung epithelial spheroid classification from Brightfield images using deep learning
2020Co-Authors: Lyan Abdul, Shravanthi Rajasekar, Dawn S Y Lin, Alexander Sotra, Yuhang Feng, Amy Liu, Sibi Venkatasubramania Raja, Boyang ZhangAbstract:Abstract Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. However, different from 2D monolayer culture, the characterization of 3D tissue models from non-invasive Brightfield images is a significant challenge. To address this issue, here we report a Deep-Learning Uncovered Measurement of Epithelial Networks (Deep-LUMEN) assay. Deep-LUMEN is an object detection algorithm that has been fine-tuned to automatically uncover subtle differences in epithelial spheroid morphology from Brightfield images. This algorithm can track changes in the luminal structure of tissue spheroids and distinguish between polarized and non-polarized lung epithelial spheroids. The Deep-LUMEN assay was validated by screening for changes in spheroid epithelial architecture in response to different extracellular matrices and drug treatments. Specifically, we found the dose-dependent toxicity of Cyclosporin can be underestimated if the effect of the drug on tissue morphology is not considered. Hence, Deep-LUMEN could be used to assess drug effects and capture morphological changes in 3D spheroid models in a non-invasive manner. Significance of the work Deep learning has been applied for the first time to autonomously detect subtle morphological changes in 3D multi-cellular spheroids, such as spheroid polarity, from Brightfield images in a label-free manner. The technique has been validated by detecting changes in spheroid morphology in response to changes in extracellular matrices and drug treatments.
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deep lumen assay human lung epithelial spheroid classification from Brightfield images using deep learning
bioRxiv, 2020Co-Authors: Lyan Abdul, Shravanthi Rajasekar, Dawn S Y Lin, Alexander Sotra, Yuhang Feng, Amy Liu, Sibi Venkatasubramania Raja, Boyang ZhangAbstract:Abstract Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. However, different from 2D monolayer culture, the characterization of 3D tissue models from non-invasive Brightfield images is a significant challenge. To address this issue, here we report a Deep-Learning Uncovered Measurement of Epithelial Networks (Deep-LUMEN) assay. Deep-LUMEN is an object detection algorithm that has been fine-tuned to automatically uncover subtle differences in epithelial spheroid morphology from Brightfield images. This algorithm can track changes in the luminal structure of tissue spheroids and distinguish between polarized and non-polarized lung epithelial spheroids. The Deep-LUMEN assay was validated by screening for changes in spheroid epithelial architecture in response to different extracellular matrices and drug treatments. Specifically, we found the dose-dependent toxicity of Cyclosporin can be underestimated if the effect of the drug on tissue morphology is not considered. Hence, Deep-LUMEN could be used to assess drug effects and capture morphological changes in 3D spheroid models in a non-invasive manner. Significance of the work Deep learning has been applied for the first time to autonomously detect subtle morphological changes in 3D multi-cellular spheroids, such as spheroid polarity, from Brightfield images in a label-free manner. The technique has been validated by detecting changes in spheroid morphology in response to changes in extracellular matrices and drug treatments.
N J O'connor - One of the best experts on this subject based on the ideXlab platform.
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Blind deconvolution of 3D transmitted light Brightfield micrographs.
Journal of microscopy, 2000Co-Authors: T J Holmes, N J O'connorAbstract:The blind deconvolution algorithm for 3D transmitted light Brightfield (TLB) microscopy, published previously [Holmes et al. Handbook of Biological Confocal Microscopy (1995)], is summarized with example images. The main emphasis of this paper is to discuss more thoroughly the importance and usefulness of this method and to provide more detailed evidence, some being quantitative, of its necessity. Samples of horseradish peroxidase (HRP)-stained pyramidal neurones were prepared and evaluated for the ability to see fine structures clearly, including the dendrites and spines. It is demonstrated that the appearance of fine spine structure, and means of identifying spine categories, is made possible by using blind deconvolution. A comparison of images of the same sample from reflected light confocal microscopy, which is the conventional light microscopic way of viewing the 3D structure of these HRP-stained samples, shows that the blind deconvolution method is far superior for clearly showing the structure with less distortion and better resolution of the spines. The main significance of this research is that it is now possible to obtain clear images of 3D structure by light microscopy of absorbing stains. This is important because the TLB microscope is probably the most widely used modality in the life-science laboratory, yet, until now, there has been no reliable means for it to provide visualization of 3D structure clearly. The main importance of the blind deconvolution approach is that it obviates the need to measure the point spread function of the optical system, so that it now becomes realistic to provide a 3D light microscopic deconvolution method that can be pervasively used by microscopists.
Badrinath Roysam - One of the best experts on this subject based on the ideXlab platform.
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A Broadly Applicable 3-D Neuron Tracing Method Based on Open-Curve Snake
Neuroinformatics, 2011Co-Authors: Yu Wang, Arunachalam Narayanaswamy, Chia-ling Tsai, Badrinath RoysamAbstract:This paper presents a broadly applicable algorithm and a comprehensive open-source software implementation for automated tracing of neuronal structures in 3-D microscopy images. The core 3-D neuron tracing algorithm is based on three-dimensional (3-D) open-curve active Contour (Snake). It is initiated from a set of automatically detected seed points. Its evolution is driven by a combination of deforming forces based on the Gradient Vector Flow (GVF), stretching forces based on estimation of the fiber orientations, and a set of control rules. In this tracing model, bifurcation points are detected implicitly as points where multiple snakes collide. A boundariness measure is employed to allow local radius estimation. A suite of pre-processing algorithms enable the system to accommodate diverse neuronal image datasets by reducing them to a common image format. The above algorithms form the basis for a comprehensive, scalable, and efficient software system developed for confocal or Brightfield images. It provides multiple automated tracing modes. The user can optionally interact with the tracing system using multiple view visualization, and exercise full control to ensure a high quality reconstruction. We illustrate the utility of this tracing system by presenting results from a synthetic dataset, a Brightfield dataset and two confocal datasets from the DIADEM challenge.
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Automated three-dimensional tracing of neurons in confocal and Brightfield images.
Microscopy and microanalysis : the official journal of Microscopy Society of America Microbeam Analysis Society Microscopical Society of Canada, 2003Co-Authors: Thomas A. Hamilton, Andrew R. Cohen, Timothy J. Holmes, Christopher Pace, Donald H. Szarowski, James N. Turner, Badrinath RoysamAbstract:Automated three-dimensional (3-D) image analysis methods are presented for tracing of dye-injected neurons imaged by fluorescence confocal microscopy and HRP-stained neurons imaged by transmitted-light Brightfield microscopy. An improved algorithm for adaptive 3-D skeletonization of noisy images enables the tracing. This algorithm operates by performing connectivity testing over large N × N × N voxel neighborhoods exploiting the sparseness of the structures of interest, robust surface detection that improves upon classical vacant neighbor schemes, improved handling of process ends or tips based on shape collapse prevention, and thickness-adaptive thinning. The confocal image stacks were skeletonized directly. The Brightfield stacks required 3-D deconvolution. The results of skeletonization were analyzed to extract a graph representation. Topological and metric analyses can be carried out using this representation. A semiautomatic method was developed for reconnection of dendritic fragments that are disconnected due to insufficient dye penetration, an imaging deficiency, or skeletonization errors.
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Developments in three-dimensional stereo Brightfield microscopy
Microscopy research and technique, 1993Co-Authors: Byron H. Willis, Badrinath Roysam, James N. Turner, Doris N. Collins, Timothy J. HolmesAbstract:We present recent developments of a widefield computer/microscope system and image reconstruction algorithm for producing three-dimensional (3D) increased depth of field images in the form of Brightfield stereo pairs of thick specimens. The theoretical principle of this image reconstruction technique is based on Weiner-type inverse filtering. A number of extensions and refinements to our previous work have included further testing of the system with a broader class of specimens and the implementation of several pragmatic refinements important for future 3D microscopy systems. These refinements include histogram modification routines for improving visualization, a preprocessing routine to eliminate edge artifacts due to circular convolution and other effects, stereo viewing angle optimization, a rule of thumb estimate for the axial sampling rate, and incorporation of a variation of the Fast Fourier Transform and filtering operations that significantly reduce computational time. Images of spyrogyra, neonatal rat hippocampal neurons, and cervical/vaginal cell smears are presented to show the utility of these methods for 3D visualization. The primary advantages of these methods are that they operate with an ordinary transmitted light microscope and are inexpensively implemented on a personal computer with reasonable computation time.
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Algorithms for 3-D Brightfield microscopy
Biomedical Image Processing and Three-Dimensional Microscopy, 1992Co-Authors: Byron H. Willis, Badrinath Roysam, James N. Turner, Timothy J. HolmesAbstract:We have developed image reconstruction algorithms for generating 3-D renderings of biological specimens from Brightfield micrographs. The algorithm presented here is founded on the maximum likelihood estimation theory where steepest ascent and conjugate gradient techniques are used to optimize the solution to the multidimensional equation. The estimation problem posed is that of reconstructing the optical density, or linear attenuation coefficients, similar to that of computed tomography, under the simplifying assumption of geometric optics. We assume white Gaussian noise corrupts the signal generating a Gaussian distributed signal according to the modeling of the system impulse response. One of the challenges of the algorithms presented here is in restoring the values within the missing cone region of the system optical transfer function. The algorithm and programming are straightforward and incorporate standard Fourier techniques. The theoretical development of the algorithms is outlined. Simulations of reconstructions using this technique are currently being performed.
Dawn S Y Lin - One of the best experts on this subject based on the ideXlab platform.
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Correction: Deep-LUMEN assay - human lung epithelial spheroid classification from Brightfield images using deep learning.
Lab on a chip, 2020Co-Authors: Lyan Abdul, Shravanthi Rajasekar, Dawn S Y Lin, Sibi Venkatasubramania Raja, Alexander Sotra, Yuhang Feng, Amy Liu, Boyang ZhangAbstract:Correction for 'Deep-LUMEN assay - human lung epithelial spheroid classification from Brightfield images using deep learning' by Lyan Abdul et al., Lab Chip, 2020, DOI: .
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Deep-LUMEN assay – human lung epithelial spheroid classification from Brightfield images using deep learning
Lab on a chip, 2020Co-Authors: Lyan Abdul, Shravanthi Rajasekar, Dawn S Y Lin, Alexander Sotra, Yuhang Feng, Amy Liu, Sibi Venkatasubramania Raja, Boyang ZhangAbstract:Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. In contrast to 2D monolayer culture, the characterization of 3D tissue models from non-invasive Brightfield images is a significant challenge. To address this issue, here we report a deep-learning uncovered measurement of epithelial networks (Deep-LUMEN) assay. Deep-LUMEN is an object detection algorithm that has been fine-tuned to automatically uncover subtle differences in epithelial spheroid morphology from Brightfield images. This algorithm can track changes in the luminal structure of tissue spheroids and distinguish between polarized and non-polarized lung epithelial spheroids. The Deep-LUMEN assay was validated by screening for changes in spheroid epithelial architecture in response to different extracellular matrices and drug treatments. Specifically, we found the dose-dependent toxicity of cyclosporin can be underestimated if the effect of the drug on tissue morphology is not considered. Hence, Deep-LUMEN could be used to assess drug effects and capture morphological changes in 3D spheroid models in a non-invasive manner.
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correction deep lumen assay human lung epithelial spheroid classification from Brightfield images using deep learning
Lab on a Chip, 2020Co-Authors: Lyan Abdul, Shravanthi Rajasekar, Dawn S Y Lin, Alexander Sotra, Yuhang Feng, Amy Liu, Sibi Venkatasubramania Raja, Boyang ZhangAbstract:Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. In contrast to 2D monolayer culture, the characterization of 3D tissue models from non-invasive Brightfield images is a significant challenge. To address this issue, here we report a deep-learning uncovered measurement of epithelial networks (Deep-LUMEN) assay. Deep-LUMEN is an object detection algorithm that has been fine-tuned to automatically uncover subtle differences in epithelial spheroid morphology from Brightfield images. This algorithm can track changes in the luminal structure of tissue spheroids and distinguish between polarized and non-polarized lung epithelial spheroids. The Deep-LUMEN assay was validated by screening for changes in spheroid epithelial architecture in response to different extracellular matrices and drug treatments. Specifically, we found the dose-dependent toxicity of cyclosporin can be underestimated if the effect of the drug on tissue morphology is not considered. Hence, Deep-LUMEN could be used to assess drug effects and capture morphological changes in 3D spheroid models in a non-invasive manner.
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Deep-LUMEN Assay – Human lung epithelial spheroid classification from Brightfield images using deep learning
2020Co-Authors: Lyan Abdul, Shravanthi Rajasekar, Dawn S Y Lin, Alexander Sotra, Yuhang Feng, Amy Liu, Sibi Venkatasubramania Raja, Boyang ZhangAbstract:Abstract Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. However, different from 2D monolayer culture, the characterization of 3D tissue models from non-invasive Brightfield images is a significant challenge. To address this issue, here we report a Deep-Learning Uncovered Measurement of Epithelial Networks (Deep-LUMEN) assay. Deep-LUMEN is an object detection algorithm that has been fine-tuned to automatically uncover subtle differences in epithelial spheroid morphology from Brightfield images. This algorithm can track changes in the luminal structure of tissue spheroids and distinguish between polarized and non-polarized lung epithelial spheroids. The Deep-LUMEN assay was validated by screening for changes in spheroid epithelial architecture in response to different extracellular matrices and drug treatments. Specifically, we found the dose-dependent toxicity of Cyclosporin can be underestimated if the effect of the drug on tissue morphology is not considered. Hence, Deep-LUMEN could be used to assess drug effects and capture morphological changes in 3D spheroid models in a non-invasive manner. Significance of the work Deep learning has been applied for the first time to autonomously detect subtle morphological changes in 3D multi-cellular spheroids, such as spheroid polarity, from Brightfield images in a label-free manner. The technique has been validated by detecting changes in spheroid morphology in response to changes in extracellular matrices and drug treatments.
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deep lumen assay human lung epithelial spheroid classification from Brightfield images using deep learning
bioRxiv, 2020Co-Authors: Lyan Abdul, Shravanthi Rajasekar, Dawn S Y Lin, Alexander Sotra, Yuhang Feng, Amy Liu, Sibi Venkatasubramania Raja, Boyang ZhangAbstract:Abstract Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. However, different from 2D monolayer culture, the characterization of 3D tissue models from non-invasive Brightfield images is a significant challenge. To address this issue, here we report a Deep-Learning Uncovered Measurement of Epithelial Networks (Deep-LUMEN) assay. Deep-LUMEN is an object detection algorithm that has been fine-tuned to automatically uncover subtle differences in epithelial spheroid morphology from Brightfield images. This algorithm can track changes in the luminal structure of tissue spheroids and distinguish between polarized and non-polarized lung epithelial spheroids. The Deep-LUMEN assay was validated by screening for changes in spheroid epithelial architecture in response to different extracellular matrices and drug treatments. Specifically, we found the dose-dependent toxicity of Cyclosporin can be underestimated if the effect of the drug on tissue morphology is not considered. Hence, Deep-LUMEN could be used to assess drug effects and capture morphological changes in 3D spheroid models in a non-invasive manner. Significance of the work Deep learning has been applied for the first time to autonomously detect subtle morphological changes in 3D multi-cellular spheroids, such as spheroid polarity, from Brightfield images in a label-free manner. The technique has been validated by detecting changes in spheroid morphology in response to changes in extracellular matrices and drug treatments.