DenseUNet: densely connected UNet for electron microscopy image segmentation

Cao, Yue; Liu, Shigang; Peng, Yali; Li, Jun

doi:10.1049/iet-ipr.2019.1527

“…It contains a contracting path for extracting features and a symmetric expanding path for up-sampling to form a U-shaped architecture. Based on Unet, researchers developed a large collection of variants [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] to further improve segmentation performance. For example, Milletari et al [15] presented V-net, which is the 3D version of Unet, to process 3D data.…”

Section: Related Workmentioning

confidence: 99%

“…The authors of Unet further proposed Unet++ [22] to dynamically adjust the depth of the network for different segmentation tasks. Cao et al [26] extended DenseNet to Unet to improve segmentation performance. Huang et al [31] proposed a group cross-channel attention (GCA) module on the basis of Unet to focus on the significant feature groups and channels, and introduced a detail recovering path to recover the fine details of brain tumors.…”

Section: Related Workmentioning

confidence: 99%

A Generalized Pooling for Brain Tumor Segmentation

Hao

¹

,

Lin²,

Qiao

³

et al. 2021

View full text Add to dashboard Cite

“…However, the specificity of this technique is low in noisy datasets and can lead to oversegmentation, whereby many small segments are created within a single feature (8,9) . As a result, segmentation tools that use machine learning and deep neural networks to distinguish features of interest from the rest of the ultrastructure have been developed for electron microscopy (e.g., Unet, Ilastik) (10)(11)(12)(13)(14)(15) . However, these tools require either a large representative training dataset or modified training for each micrograph.…”

Section: Introductionmentioning

confidence: 99%

Contour: A semi-automated segmentation and quantitation tool for cryo-soft-X-ray tomography

Nahas

¹

,

Fernandes

²

,

Vyas

³

et al. 2022

View full text Add to dashboard Cite

Cryo-soft-X-ray tomography is being increasingly used in biological research to study the morphology of cellular compartments and how they change in response to different stimuli, such as viral infections. Segmentation of these compartments is limited by time-consuming manual tools or machine learning algorithms that require extensive time and effort to train. Here we describe Contour, a new, easy-to-use, highly automated segmentation tool that enables accelerated segmentation of tomograms to delineate distinct cellular compartments. Using Contour, cellular structures can be segmented based on their projection intensity and geometrical width by applying a threshold range to the image and excluding noise smaller in width than the cellular compartments of interest. This method is less laborious and less prone to errors from human judgement than current tools that require features to be manually traced, and it does not require training datasets as would machine-learning driven segmentation. We show that high-contrast compartments such as mitochondria, lipid droplets, and features at the cell surface can be easily segmented with this technique in the context of investigating herpes simplex virus 1 infection. Contour can extract geometric measurements from 3D segmented volumes, providing a new method to quantitate cryo-soft-X-ray tomography data. Contour can be freely downloaded at github.com/kamallouisnahas/Contour.

show abstract

“…As a result, segmentation tools that use machine learning and deep neural networks to distinguish features of interest from the rest of the ultrastructure have been developed for electron microscopy (e.g. Unet, Ilastik) (10)(11)(12)(13)(14)(15) . However, these tools require either a large representative training dataset or modified training for each micrograph.…”

Section: Introductionmentioning

confidence: 99%

Contour, a semi-automated segmentation and quantitation tool for cryo-soft-X-ray tomography

Nahas

¹

,

Fernandes

²

,

Crump

³

et al. 2021

Preprint

View full text Add to dashboard Cite

Cryo-soft-X-ray tomography is being increasingly used in biological research to study the morphology of cellular compartments and how they change in response to different stimuli, such as viral infections. Segmentation of these compartments is limited by time-consuming manual tools or machine learning algorithms that require extensive time and effort to train. Here we describe Contour, a new, easy-to-use, highly automated segmentation tool that enables accelerated segmentation of tomograms to delineate distinct cellular compartments. Using Contour, cellular structures can be segmented based on their projection intensity and geometrical width by applying a threshold range to the image and excluding noise smaller in width than the cellular compartments of interest. This method is less laborious and less prone to errors from human judgement than current tools that require features to be manually traced, and does not require training datasets as would machine-learning driven segmentation. We show that high-contrast compartments such as mitochondria, lipid droplets, and features at the cell surface can be easily segmented with this technique in the context of investigating herpes simplex virus 1 infection. Contour can extract geometric measurements from 3D segmented volumes, providing a new method to quantitate cryo-soft-X-ray tomography data. Contour can be freely downloaded at github.com/kamallouisnahas/Contour.Impact StatementMore research groups are using cryo-soft-X-ray tomography as a correlative imaging tool to study the ultrastructure of cells and tissues but very few tomograms are segmented with existing segmentation programs. Segmentation is usually a prerequisite for measuring the geometry of features in tomograms but the time- and labour-intensive nature of current segmentation techniques means that such measurements are rarely across a large number of tomograms, as is required for robust statistical analysis. Contour has been designed to facilitate the automation of segmentation and, as a result, reduce manual effort and increase the number of tomograms that can be segmented. Because it requires minimal manual intervention, Contour is not as prone to human error as programs that require the users to trace the edges of cellular features. Geometry measurements of the segmented volumes can be calculated using this program, providing a new platform to quantitate cryoSXT data. Contour also supports quantitation of volumes imported from other segmentation programs. The generation of a large sample of segmented volumes with Contour that can be used as a representative training dataset for machine learning applications is a long-term aspiration of this technique.

show abstract

DenseUNet: densely connected UNet for electron microscopy image segmentation

Cited by 70 publications

References 50 publications

A Generalized Pooling for Brain Tumor Segmentation

A Generalized Pooling for Brain Tumor Segmentation

Contour: A semi-automated segmentation and quantitation tool for cryo-soft-X-ray tomography

Contour, a semi-automated segmentation and quantitation tool for cryo-soft-X-ray tomography

Contact Info

Product

Resources

About