Dataset Information

In Support of: Label-free cell segmentation of diverse lymphoid tissues in 2-D and 3-D

ABSTRACT: 20221212 Code and image-data supporting the 2-D and 3-D label-free cell segmentation method demonstrated in the paper "Label-free cell segmentation of diverse lymphoid tissues in 2-D and 3-D".2022 Five zip files are available for download: 1 - Standalone software. This allows deployment of the method using precompiled Windows software to generate the label-free probability maps with no programming required. (~2.77GB including image-data) 2 - MATLAB version. This provides the data and code necessary to reproduce the methods shown in the paper using MATLAB and the Deep Learning Toolbox (~7GB including image-data). 3 - Python version. This provides the data and code necessary to reproduce the methods shown in the paper using the Python programming language using TensorFlow / Keras (~8GB including image-data). 4 - MATLAB App files to enable further development/tailoring of standalone software (i.e., not compiled) (~2.80GB including image-data) 5 - MATLAB, Python and uncompiled software files - CODE ONLY NO DATA (~60 MB) Abstract - Unlocking and quantifying fundamental biological processes through tissue microscopy requires accurate, in situ segmentation of all cells imaged. Currently, achieving this is complex and requires exogenous fluorescent labels that occupy significant spectral bandwidth, increasing the duration and complexity of imaging experiments whilst limiting the number of channels remaining to address the study’s objectives. To overcome these difficulties, here we show that the excitation light reflected during routine confocal microscopy contains sufficient information to achieve accurate, label-free cell segmentation in 2-D and 3-D. This is achieved using a simple neural network trained to output the probability that reflected light pixels belong to either nucleus, cytoskeleton or background classifications. We demonstrate the approach across diverse lymphoid tissues and provide video tutorials demonstrating deployment using Python, MATLAB or precompiled software for Windows.

ORGANISM(S): Mus musculus (mouse)

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PROVIDER: S-BSST742 | bioimages |

REPOSITORIES: bioimages

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Project description:Chromatin-sensitive Partial Wave Spectroscopic (csPWS) microscopy offers a non-invasive glimpse into the mass density distribution of cellular structures at the nanoscale, leveraging the spectroscopic information. Such capability allows us to analyze the chromatin structure and organization and the global transcriptional state of the cell nuclei for the study of its role in carcinogenesis. Accurate segmentation of the nuclei in csPWS microscopy images is an essential step in isolating them for further analysis. However, manual segmentation is error-prone, biased, time-consuming, and laborious, resulting in disrupted nuclear boundaries with partial or over-segmentation. Here, we present an innovative deep-learning-driven approach to automate the accurate nuclei segmentation of label-free live cell csPWS microscopy imaging data. Our approach, csPWS-seg, harnesses the Convolutional Neural Networks-based U-Net model with an attention mechanism to automate the accurate cell nuclei segmentation of csPWS microscopy images. We leveraged the structural, physical, and biological differences between the cytoplasm, nucleus, and nuclear periphery to construct three distinct csPWS feature images for nucleus segmentation. Using these images of HCT116 cells, csPWS-seg achieved superior performance with a median Intersection over Union (IoU) of 0.80 and a Dice Similarity Coefficient (DSC) score of 0.88. The csPWS-seg overcame the segmentation performance over the baseline U-Net model and another attention-based model, SE-U-Net, marking a significant improvement in segmentation accuracy. Further, we analyzed the performance of our proposed model with four loss functions: binary cross-entropy loss, focal loss, dice loss, and Jaccard loss. The csPWS-seg with focal loss provided the best results compared to other loss functions. The automatic and accurate nuclei segmentation offered by the csPWS-seg not only automates, accelerates, and streamlines csPWS data analysis but also enhances the reliability of subsequent chromatin analysis research, paving the way for more accurate diagnostics, treatment, and understanding of cellular mechanisms for carcinogenesis.

Dataset Information

In Support of: Label-free cell segmentation of diverse lymphoid tissues in 2-D and 3-D

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