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Segmented water phase for image: NPI-012-Filtered Segmentation performed using Watershed segmentation in Avizo. Image Dimension: 1024×1024×845 Image Type: 8 bit
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2019-01-24
相关数据集
Computing a Discrete Morse Gradient From a Watershed Decomposition
Running code produced for the paper Computing a Discrete Morse Gradient From a Watershed Decomposition Lidija Comic, Leila De Floriani, Federico Iuricich, Paola Magillo Tested Operating Sytem MacOSX 10.11.4
Mendeley Data20
IEEEXtreme 17: Watershed
This dataset contains a total of 3313 submissions, with 23% correct solutions and 41% partial solutions. Link to the original challenge: https://csacademy.com/ieeextreme-practice/task/watershed/ See the included PDF document for details about the original challenge.
DataCite Commons2024-02-26 更新20
WI-Segmented-Oil
Segmented oil phase for image: WI-Filtered Segmentation performed using Watershed segmentation in Avizo. Image Dimension: 1024×1024×845 Image Type: 8 bit
Figshare2019-01-24 更新10
Supplementary: all individual label fields
Supplementary data, not required to view model in 3D (in the Poject window of Avizo) or 2D (in the Segmentation window of Avizo). 24 individual label fields. For a condensed version of all 24 labels in a single file, please refer to 'All label fields'.
Figshare2019-09-11 更新20
Binary segmentation images for 3D segmentation model training
Binary segmentation images for 3D segmentation model training
NIAID Data Ecosystem10
Synthetic 3D Filamentous Network Dataset for Segmentation and Geometric Reconstruction Evaluation
Profesor e investigador en ciencias computacionales, con líneas de investigación enfocadas en inteligencia artificial, visión por computadora, procesamiento de imágenes tridimensionales y desarrollo de sistemas inteligentes aplicados a problemas científicos y educativos.
Zenodo2026-06-01 更新00
NPI-012-Segmented-Water
Segmented water phase for image: NPI-012-Filtered Segmentation performed using Watershed segmentation in Avizo. Image Dimension: 1024×1024×845 Image Type: 8 bit
NIAID Data Ecosystem60
Code and Data from: Segmenting Root Systems in X-Ray Computed Tomography Images Using Level Sets
This record contains code and data for segmentation using a three-dimensional level-set method, written by Amy Tabb in C++. The record also contains two datasets of root systems in media imaged with X-Ray CT, and the results of running the code on those datasets. The code will also perform a pre-processing task in three-dimensional image sets, and a dataset for that purpose is included as well. This work is a companion to the paper : "Segmenting root systems in X-ray computed tomography images using level sets" (WACV 2018) by the authors or this record, and and open-access version of the paper is here -- https://arxiv.org/abs/1809.06398 . The code is also available from Github: https://github.com/amy-tabb/tabb-level-set-segmentation , using a DOI and stable releases https://doi.org/10.5281/zenodo.3344906. Format of the data: Three input datasets are provided; two for the segmentation functionality of the code, and one to test the pre-processing functionality. The two segmentation sets are the same as were used in the paper, and are CassavaDataset, and SoybeanDataset. The pre-processing set is CassavaSlices. The output set for Soybean is SoybeanResultsJul11. The Cassava result set is large, so I broke it into three compressed folders, CassavaResultsJul12_A, _B, _C. _B is the largest, and only contains the results overwritten on the original X-Ray images. Unless your connection to Zenodo is extremely fast, it will be faster to compute the result than to download it.
Zenodo2020-07-29 更新00
Prediction of Diblock Copolymer Lamellar Morphologies in 3D and Complex Geometry via Machine Learning
3d morphology evolution of diblock copolymer via 3D UNet. Manuscript: Prediction of Diblock Copolymer Lamellar Morphologies in 3D and Complex Geometry via Machine Learning
Zenodo2023-06-02 更新00
VoroCrack3d: An annotated data set of 3d CT concrete images with synthetic crack structures
VoroCrack3d is an annotated data set of 3d CT images of concrete with synthetic crack structures. Its main purpose is the training and testing of machine learning models for 3d crack segmentation. The data set comprises 1344 images together with their corresponding ground truths. The concrete backgrounds are cropped out sections of size 400x400x400 voxels of CT images of concrete. To this end, several different concrete samples were scanned (normal concrete (NC), high-performance concrete (HPC), ultra-high-performance concrete (UHPC), air pore concrete; without and with reinforcements (steel fibers, steel wire, steel hooks, polypropylene fibers, fibers from glass fiber-reinforced polymer). The original concrete images have a resolution between 2.8 and 106 micrometers. The crack structures are modeled via minimum-weight surfaces in Voronoi diagrams according to the paper [1] C. Jung, C. Redenbach, Crack Modeling via Minimum-Weight Surfaces in 3d Voronoi Diagrams, Journal of Mathematics in Industry, 13, 10 (2023). https://doi.org/10.1186/s13362-023-00138-1. The surfaces are discretized, dilated and superimposed on the concrete backgrounds. The data set offers a high variety regarding concrete types, noise levels and crack widths, shapes, regularity and branching. This makes it suitable for studying the generalizability and robustness of 3d crack segmentation methods. ______________________________________________________________________________________________ The folder 'data' contains seven subfolders, each containing the data generated from a specific concrete type (NC, HPC, air pore concrete, polypropylene fiber-reinforced concrete, steel fiber-reinforced concrete, concrete with steel hooks and concrete with steel wires). Each subfolder again contains four subfolders according to the point process model that was used for generating the 3d Voronoi diagrams. The point processes and Voronoi diagrams are restricted to windows of size 400x150x400. - 'hc': Hard core point process with 60% volume density and intensity 0.000025 obtained from force-biased sphere packing.- 'matclust': Matérn cluster process with parent intensity 0.0002/50, offspring intensity 50 and cluster radius 20.- 'ppp': Poisson point process with intensity 0.0002.- 'ppp-scaled': Poisson point process with intensity 0.0002 (but inside 200x150x200 window). The resulting Voronoi diagram is stretched in x- and z- direction by a factor of 2. Each of these contains five subfolders: one for the 3d input images, two for the corresponding labels (ground truths; one with and one without pores/fibers), one for the input and label previews (slice z=200 for each of the images) and a misc folder containing the concrete background without crack and, if applicable, the pore/fiber segmentation image. The data itself then contains 48 images:1a-1d: crack with up to seven branches; fixed crack width (~1 voxel).2a-2d: crack with up to four branches; fixed crack width (~1 voxel).3a-3d: crack with up to one branch; fixed crack width (~1 voxel).4a-4d: crack with no branches; fixed crack width (~1 voxel).5a-5d: crack with no branches; fixed crack width (~3 voxels).6a-6d: crack with no branches; fixed crack width (~5 voxels).7a-7d: crack with no branches; fixed crack width (~7 voxels).8a-8d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.01);9a-9d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.02);10a-10d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.05);11a-11d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.1);12a-12d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.2); The names 'a'-'d' indicate level of added noise added to the image:a: None.b: Uniformly on [-sigma,sigma] c: Uniformly on [-2*sigma,2*sigma] d: Uniformly on [-4*sigma,4*sigma] Negative values are mapped to 0. For inputs of type int, noise values are rounded to the nearest integer.(sigma = standard deviation of voxel greyvalues in image) Note that the labels in the ground truths correspond to the local crack width. For more details, we refer to [1].
Zenodo2024-05-15 更新00
Comparison of the marker-controlled watersheds that use the smart markers and those identified by the comparison algorithms.
The results are obtained on the test set.
Figshare2015-12-02 更新10
Accuracy of the watershed cell segmentation.
Accuracy of the watershed cell segmentation.
Figshare2015-12-02 更新00
Supplementary Material for: On the variability in cell and nucleus shape
Cell morphology is an important regulator of cell function. Many abnormalities in cellular behavior can be discerned from changes in the shape of the cell and its organelles, typically the nucleus. Two major challenges for developing such phenotypic assays are reconstructing 3D surfaces of individual cells and nuclei from confocal images and developing characterizations of these surfaces for comparisons. We demonstrate two algorithms - 3D Active Contours and 3D Condensed-Attention UNet to segment cells and nuclei from confocal images. The cell and nuclear surfaces are then converted into vectors using a reversible, spherical transform - i.e. shapes can be recovered from the vectors. Typical methods for characterizing shapes using size, shape, and image parameters such as area, volume, shape factor, solidity, and pixel intensities are not amenable to such reverse transformation. Our vector representation's Principal Component Analysis (PCA) shows that the significant modes of variability among cell and nucleus shapes are scaling and flattening. We benchmark these modes using a known mechanical model for nucleus morphology. Subsequent modes alter the eccentricity of the nucleus and translate and rotate it with respect to the cell. Our vector-space representation of cell and nucleus shape helps physically interpret the variability sources. It may further help to guide mechanical models and identify molecular mechanisms driving cell and nuclear shape changes. The source code and the data used in this article are available at: https://github.com/iitgoa-ml/3d-cells-nuclei-segmentation
Figshare2023-01-10 更新00
Label surfaces
Individual label surfaces for 3D model viewing in Avizo Project window.
Figshare2019-09-11 更新20
t2_segmented.tar.gz
Segmented images of "t2_reconstructed.tar.gz" into foam skeleton and void space in 8 bit *.tif file format. 751x751x463 voxels with a uniform voxel size of 74.8 µm.
DataCite Commons2024-11-11 更新30
3D Convolutional Neural Networks for Dendrite Segmentation Using Fine-Tuning and Hyperparameter Optimization
X-ray computed tomography reconstructions showing the dendritic growth of an Al-Zn alloy across time steps. Folders beginning with "c-" contain image volumes, while other folders contain manually segmented images used for training 3D convolutional neural networks. These networks are used to segment dendrites across all published image volumes.
DataCite Commons2024-02-26 更新80
WI-Segmented-Water
Segmented water phase for image: WI-Filtered Segmentation performed using Watershed segmentation in Avizo. Image Dimension: 1024×1024×845 Image Type: 8 bit
NIAID Data Ecosystem60
LiS_Cathode_Subvolume1_3D_Segmented_Image_272x256x131.tif
The tiff image is the subvolume 1 which is extracted from the full segmented image of the Lithium-Sulfur battery cathode in the referenced paper. The volume of the image is 272...
B2FIND50
Synthetic 3D Filamentous Network Dataset for Segmentation and Geometric Reconstruction Evaluation
Profesor e investigador en ciencias computacionales, con líneas de investigación enfocadas en inteligencia artificial, visión por computadora, procesamiento de imágenes tridimensionales y desarrollo de sistemas inteligentes aplicados a problemas científicos y educativos.
Zenodo2026-06-01 更新00
