Deep learning based domain adaptation for mitochondria segmentation on EM volumes

Franco-Barranco, Daniel; Pastor-Tronch, Julio; Gonzalez-Marfil, Aitor; Muñoz-Barrutia, Arrate; Arganda‐Carreras, Ignacio

doi:10.1016/j.cmpb.2022.106949

Cited by 12 publications

(6 citation statements)

References 29 publications

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“…Custom Matlab scripts were used to calculate the nuclei centroids and the application VolumeSegmenter from Matlab were used to curate the membrane regions of the cells. The training dataset was composed of 14 time-points from 256-cells stage with 35 cells labelled of the same embryo development and was used as an input to a Deep Neural Network (DNN) presenting an architecture based on residual connections (3D ResU-Net) (Franco-Barranco et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

Local and global changes in cell density induce reorganisation of 3D packing in a proliferating epithelium

Barone,

Tagua,

Andrés-San Román

et al. 2024

Preprint

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Tissue morphogenesis is intimately linked to the changes in shape and organisation of individual cells. In curved epithelia, cells can intercalate along their own apicobasal axes adopting a shape named 'scutoid' that allows energy minimization in the tissue. Although several geometric and biophysical factors have been associated with this 3D reorganisation, the dynamic changes underlying scutoid formation in 3D epithelial packing remain poorly understood. Here we use live-imaging of the sea star embryo coupled with deep learning-based segmentation, to dissect the relative contributions of cell density, tissue compaction, and cell proliferation on epithelial architecture. We find that tissue compaction, which naturally occurs in the embryo, is necessary for the appearance of scutoids. Physical compression experiments identify cell density as the factor promoting scutoid formation at a global level. Finally, the comparison of the developing embryo with computational models indicates that the increase in the proportion of scutoids is directly associated with cell divisions. Our results suggest that apico-basal intercalations appearing just after mitosis may help accommodate the new cells within the tissue. We propose that proliferation in a compact epithelium induces 3D cell rearrangements during development.

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Section: Methodsmentioning

confidence: 99%

Local and global changes in cell density induce reorganisation of 3D packing in a proliferating epithelium

Barone,

Tagua,

Andrés-San Román

et al. 2024

Preprint

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“…In BiaPy, we adopt two pretext tasks: reconstruction and masking. The reconstruction task involves recovering the original image from a degraded version of it [37]. In the masking pretext task, random patches of the input image are masked, and the network is trained to reconstruct the missing pixels or voxels [38].…”

Section: Biapy Workflowsmentioning

confidence: 99%

BiaPy: A unified framework for versatile bioimage analysis with deep learning

Franco-Barranco,

Andrés-San Román,

Hidalgo-Cenalmor

et al. 2024

Preprint

Self Cite

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BiaPy, a unified open-source bioimage analysis library, offers a comprehensive suite of deep learning-powered workflows. Tailored for users of all levels, BiaPy features an intuitive interface, zero-code notebooks, and Docker integration. With support for 2D and 3D image data, it addresses existing gaps by providing multi-GPU capabilities, memory optimization, and compatibility with large datasets. As a collaborative and accessible solution, BiaPy aims to empower researchers by democratizing the use of sophisticated and efficient bioimage analysis workflows.

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“…In cell biology, the attempts at fully automated organelle segmentation were mostly enabled by convolutional neural networks (CNNs) like the 3D U-net (23). In most cases, the efforts focused on obtaining a high segmentation quality for just one organelle, like mitochondria (24)(25)(26)(27), or nuclear membrane (17). More recently, DL methods were successfully applied to full organelle segmentation (28,29).…”

Section: Introductionmentioning

confidence: 99%

CebraEM: A practical workflow to segment cellular organelles in volume SEM datasets using a transferable CNN-based membrane prediction

Hennies

Lleti

Pape

et al. 2023

Preprint

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Segmentation of large-volume datasets obtained by volume SEM techniques is a challenging task that generally requires a considerable amount of human effort. Despite recent advances in deep learning leading to the successful segmentation of cellular organelles in a variety of datasets, it is still challenging and time-consuming to produce the necessary data for training a convolutional neural network as well as to set up targeted post-processing pipelines to obtain a good quality full-volume semantic instance segmentation. We present CebraEM, a software package that uses a novel workflow for the segmentation of organelles in volume EM datasets, which helps to minimize the annotation time for the generation of training data. It relies on a generic CNN-based membrane prediction, followed by a well-established machine-learning pipeline that includes over-segmentation before random forest classification and graph multi-cut grouping. The workflow was tested for the segmentation of organelles on different datasets originating from various sample preparations and imaging modalities in volume SEM, in each case resulting in state-of-the-art semantic instance segmentations without additional post-processing. Importantly, by considerably simplifying the segmentation problem, CebraEM empowers single users with the ability to efficiently segment hundreds of gigabytes of data.

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Deep learning based domain adaptation for mitochondria segmentation on EM volumes

Cited by 12 publications

References 29 publications

Local and global changes in cell density induce reorganisation of 3D packing in a proliferating epithelium

Local and global changes in cell density induce reorganisation of 3D packing in a proliferating epithelium

BiaPy: A unified framework for versatile bioimage analysis with deep learning

CebraEM: A practical workflow to segment cellular organelles in volume SEM datasets using a transferable CNN-based membrane prediction

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