Segmentation and Tracking of Mammary Epithelial Organoids in Brightfield Microscopy
| Authors | |
|---|---|
| Year of publication | 2023 |
| Type | Article in Periodical |
| Magazine / Source | IEEE Transactions on Medical Imaging |
| MU Faculty or unit | |
| Citation | |
| web | https://doi.org/10.1109/TMI.2022.3210714 |
| Doi | http://dx.doi.org/10.1109/TMI.2022.3210714 |
| Keywords | organoid segmentation; organoid tracking; brightfield microscopy; deep learning; image synthesis |
| Description | We present an automated and deep-learningbased workflow to quantitatively analyze the spatiotemporal development of mammary epithelial organoids in twodimensional 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. |
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