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45 results for “segmentation methods”
The role of injection method on residual trapping at the pore-scale in continuum-scale samples: segmented data
<p>The experiments in this work explore the role of a variable injection rate on gas saturation and residual trapping. There are 2 experiments in this work H2L (high to low injection rate) and L2H (low to high injection rate). The workflow for processing the micro-CT images to get the segmented images is described in [1]. </p><p>The following scans are included in this repository NB. all data for this repository is segmented micro-CT data.: </p><ol><li>Dry scan prior to experiment = merged_binning_2_38_1927</li><li>H2L during high flow = merged_segmented_flow_09_h2lh_merged</li><li>H2L during low flow = merged_segmented_flow_11_h2ll_2_merged</li><li>H2L at the end of drainage (no flow) =merged_segmented_flow_16_dra1_pd5_merged</li><li>H2L at the end of imbibition (no flow) =merged_segmented_flow_21_imb1_pi1_merged</li><li>L2H during low flow = merged_segmented_flow_29_2_l2hl_merged</li><li>L2H during high flow = merged_segmented_flow_30_l2hh_merged</li><li>L2H at the end of drainage (no flow) =merged_segmented_flow_31_dra2_pd1_merged</li><li>L2H at the end of imbibition (no flow) =merged_segmented_flow_33_imb2_pi1_merged</li></ol>
Dataset Comparison of MRI-based automated segmentation methods and functional neurosurgery targeting with direct visualization of the Ventro-intermediate thalamic nucleus at 7T
<p>Scientific Reports - Nature - DOI : 10.1038/s41598-018-37825-8</p> <p>##################################<br> "Comparison of MRI-based automated segmentation methods and functional neurosurgery targeting with direct visualization of the Ventro-intermediate thalamic nucleus at 7T"<br> ##################################</p> <p>E. Najdenovska*, C. Tuleasca*, J. Jorge, P. Maeder, J.P. Marques, T. Roine, D. Gallichan, J.-P. Thiran, M. Levivier, and M. Bach Cuadra</p> <p>*Equally contributed authors</p> <p><br> Copyright (c) - All rights reserved. University of Lausanne. 2018.</p> <p><br> To reproduce the analyses presented in the referred study, in this repository you could find the MR images acquired from nine young healthy subjects (YS1-YS5), four elderly healthy subject (ES1-ES4) and two drug-resistant tremor patients treated treated with Vim radiosurgery by Gamma Knife (P1 and P2).</p> <p>The provided dataset includes the following NifTI files:</p> <p>- MPRRAGE @3T<br> - DWI @3T (together with the corresponding bvals and bvecs)<br> - MP2RAGE @7T<br> - SWI @7T<br> - binary masks of the manual delineation of both left and right Vim respectively that were done on the SWI (as NifTI files as well).</p> <p>Additionally, for the young cohort (YS1-YS5) we include as well the images used for building the quadrilateral of Guiot:<br> - T2-w @3T<br> - T2 CISS @3T</p> <p>For the patients (P1 and P2), a follow-up MPRAGE (acquired at 3T) with Gadolinium enhancement is also provided.</p> <p>——————————————<br> Notes:<br> 1. For YS3 MP2RAGE at 7T is missing, instead MPRAGE at 3T was used</p> <p>2. The code performing the thalamic nuclei clustering could be found in Zenodo (DOI: 10.5281/zenodo.123768)</p>
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019) in Variability of the gene cyt b in the Korean field mouse Apodemus peninsulae Thomas, 1906 - a reservoir host of AMRV in the Khasansky District of Primorsky Krai
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019)
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 4.Performance based on no. of tumor pixel & execution time
<p>In this paper we segmented the brain tumors in axial view of MR images with the help of<br> unsupervised clustering method i.e. K-means clustering. The unsupervised clustering methods gave<br> the better results than traditional method.<br> The performance analysis and comparison is done f on the basis of no. of tumor pixels in<br> segmented brain tumor and the execution time for the same. Regarding the no. of tumor pixels, Kmeans<br> clustering gave a better result than the other methods. The clustering algorithms were tested<br> with a data base of 20 MRI brain images. K-means clustering achieved almost 90%result</p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 3:(a) Input MR Image (b) Enhanced Image (c) Segmented Tumor (d) Located brain tumor
<p>Figure 3 shows three different original brain MR images, contrast enhancement of the<br> images, segmented images using K-means algorithm and finally located tumor. Fig 1.4 shows the<br> performance of the unsupervised clustering methods with the no. of tumor pixels and execution<br> time to locate the brain tumor.</p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 2. Stages of software implementation
<p>The algorithm has two stages, first is pre-processing of given MRI image and after that<br> segmentation and then perform morphological operations.</p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 1. Diagnosis Rate in different Countrie
<p>In MRI images, the amount of data is too much for manual segmentation. The procedure is<br> tedious, time, labor consuming, subjective and requires expertise. This gave way to methods that are<br> computer-aided with user interaction at varying levels. These methods are automatic and objective<br> and the results are highly reproducible. We designed software tool for locating brain tumor, based<br> on unsupervised clustering methods and analyzed its performance</p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 3. (3.a) – The flowchart of Graph cuts method; (3.b)- the result of Graph cuts image segmentation.
<p>Figure 3 describes the steps implemented Graph cuts algorithm for the segmentation of human body parts. The results obtained are 5 main sections that include the hands, the legs, the center of the body (chest, waist, hips), and the head. The result of the display image is taken from the human image database, which was collected by us (Нгуен, 2016). </p>
Dataset: A scalable method to improve gray matter segmentation at ultra high field MRI.
<p><strong>Dataset description: </strong>Accompanying data for manuscript “<a href="https://www.biorxiv.org/content/early/2018/01/10/245738">A scalable method to improve gray matter segmentation at ultra high field MRI</a>” written by Omer Faruk Gulban, Marian Schneider, Ingo Marquardt, Roy Haast, Federico De Martino.</p> <p><a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0198335">Published in PLOS One, June 6, 2018</a>.</p> <p>The dataset consist of 7 Tesla MRI anatomical images of living human brains (whole brain; 0.7mm isotropic resolution; T1 weighted, T2* weighted, proton density weighted MPRAGE images; inversion 1, inversion 2, T1, uni, MP2RAGE images; Multi-echo 3D GRE) and hand labeled cortical gray matter images (for further details see <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0198335#sec012">section 4.1 of our manuscript</a>).</p> <p>Folder structure is organized according to Brain Imaging Data Structure (BIDS). Further details can be found the README files.</p> <p><strong>Citation</strong></p> <p>Please cite the following paper together with this dataset doi:</p> <ul> <li>Gulban, O. F., Schneider, M., Marquardt, I., Haast, R. A. M., & De Martino, F. (2018). A scalable method to improve gray matter segmentation at ultra high field MRI. <em>PLOS ONE</em>, <em>13</em>(6), e0198335. http://doi.org/10.1371/journal.pone.0198335</li> </ul> <p><br> Bibtex format:</p> <p>```<br> @article{Gulban2018,<br> author = {Gulban, Omer Faruk and Schneider, Marian and Marquardt, Ingo and Haast, Roy A. M. and {De Martino}, Federico},<br> doi = {10.1371/journal.pone.0198335},<br> editor = {Pham, Dzung},<br> issn = {1932-6203},<br> journal = {PLOS ONE},<br> month = {jun},<br> number = {6},<br> pages = {e0198335},<br> title = {{A scalable method to improve gray matter segmentation at ultra high field MRI}},<br> url = {http://dx.plos.org/10.1371/journal.pone.0198335},<br> volume = {13},<br> year = {2018}<br> }<br> <br> ```</p>
72 white matter bundles segmented by 7 different methods in one HCP subject
<p>This dataset contains segmentations of 72 white matter tracts from 7 different automatic segmentation methods for one subject (623844) from the Human Connectome Project (HCP) young adult dataset (https://www.humanconnectome.org/study/hcp-young-adult). Segmentations are given for the original HCP data ("HCP Quality") and for a downsampled version of the HCP data with reduced resolution (2.5mm isotrop) and less gradients (32x b=1000mm/s^2) ("Clinical Quality").</p> <p>The data is part of the following project: https://github.com/MIC-DKFZ/TractSeg/<br> If you use the data please cite the paper mentioned on the project page.</p> <p>Each file is 4D nifti image. The 4th dimension determines the bundle:</p> <p>1: AF_left (Arcuate fascicle)<br> 2: AF_right<br> 3: ATR_left (Anterior Thalamic Radiation)<br> 4: ATR_right<br> 5: CA (Commissure Anterior)<br> 6: CC_1 (Rostrum)<br> 7: CC_2 (Genu)<br> 8: CC_3 (Rostral body (Premotor))<br> 9: CC_4 (Anterior midbody (Primary Motor))<br> 10: CC_5 (Posterior midbody (Primary Somatosensory))<br> 11: CC_6 (Isthmus)<br> 12: CC_7 (Splenium)<br> 13: CG_left (Cingulum left)<br> 14: CG_right <br> 15: CST_left (Corticospinal tract<br> 16: CST_right <br> 17: MLF_left (Middle longitudinal fascicle)<br> 18: MLF_right<br> 19: FPT_left (Fronto-pontine tract)<br> 20: FPT_right <br> 21: FX_left (Fornix)<br> 22: FX_right<br> 23: ICP_left (Inferior cerebellar peduncle)<br> 24: ICP_right <br> 25: IFO_left (Inferior occipito-frontal fascicle) <br> 26: IFO_right<br> 27: ILF_left (Inferior longitudinal fascicle) <br> 28: ILF_right <br> 29: MCP (Middle cerebellar peduncle)<br> 30: OR_left (Optic radiation) <br> 31: OR_right<br> 32: POPT_left (Parieto‐occipital pontine)<br> 33: POPT_right <br> 34: SCP_left (Superior cerebellar peduncle)<br> 35: SCP_right <br> 36: SLF_I_left (Superior longitudinal fascicle I)<br> 37: SLF_I_right <br> 38: SLF_II_left (Superior longitudinal fascicle II)<br> 39: SLF_II_right<br> 40: SLF_III_left (Superior longitudinal fascicle III)<br> 41: SLF_III_right <br> 42: STR_left (Superior Thalamic Radiation)<br> 43: STR_right <br> 44: UF_left (Uncinate fascicle) <br> 45: UF_right <br> 46: CC (Corpus Callosum - all)<br> 47: T_PREF_left (Thalamo-prefrontal)<br> 48: T_PREF_right <br> 49: T_PREM_left (Thalamo-premotor)<br> 50: T_PREM_right <br> 51: T_PREC_left (Thalamo-precentral)<br> 52: T_PREC_right <br> 53: T_POSTC_left (Thalamo-postcentral)<br> 54: T_POSTC_right <br> 55: T_PAR_left (Thalamo-parietal)<br> 56: T_PAR_right <br> 57: T_OCC_left (Thalamo-occipital)<br> 58: T_OCC_right <br> 59: ST_FO_left (Striato-fronto-orbital)<br> 60: ST_FO_right <br> 61: ST_PREF_left (Striato-prefrontal)<br> 62: ST_PREF_right <br> 63: ST_PREM_left (Striato-premotor)<br> 64: ST_PREM_right <br> 65: ST_PREC_left (Striato-precentral)<br> 66: ST_PREC_right <br> 67: ST_POSTC_left (Striato-postcentral)<br> 68: ST_POSTC_right<br> 69: ST_PAR_left (Striato-parietal)<br> 70: ST_PAR_right <br> 71: ST_OCC_left (Striato-occipital)<br> 72: ST_OCC_right</p>
Outputs from new methods for 3D+time cell image segmentation and tracking
<p>Segmentation and tracking of 3D+time microscopy images of cell nuclei within the zebrafish pectoral fin.</p> <p>The file named 7_cells_moving_in_70_frames_orig.avi is a 70-frame video of a group of cells moving in time, the file named 7_cells_moving_in_70_frames.avi has the result of 4D segmentation, using our new segmentation methods, for seven cells (colored black) moving in time, and the file _tracking_of_7_cell_in_70_frames.mp4 has the tracking of these seven cells. </p> <p>Additionally, the file named group_of_cells_moving_in_70_frames_orig.avi is a 70-frame video of a group of cells moving in time, the file named group_of_cells_moving_in_70_frames.avi has the result of 4D segmentation, using our new segmentation methods, for the group of cells (colored black) moving in time and the file _tracking_of_group_of_cell_in_70_frames.gif has the tracking of this group of cells. </p>
A Comparison between Background Modelling Methods for Vehicle Segmentation in Highway Traffic Videos
<p>This dataset was used on the paper "A Comparison between Background Modelling Methods for Vehicle Segmentation in Highway Traffic Videos" for the comparison of three of the most common background modelling methods. The objective was to determine which of the models would be a better fit for the videos we had available at the time.</p> <p>Images are separated into folders, each corresponding to one of the videos used. To understand the naming convention, you can check <a href="https://arxiv.org/abs/1810.02835">the paper</a>, available at arXiv.</p>
Data_Evaluation of Deep Learning Based Segmentation Methods for Industrial Burner Flames
<p>The publicated files contain data related to the research article with the title Evaluation of Deep Learning Based Segmentation Methods for Industrial Burner Flames. It contains raw image data, segmentation results and a matlab script for visualization and its derived figures.</p>
An Automated Method for Measuring Tree Rings Based on Super Resolution and Image Segmentation
Open the record for dataset details and reuse information.
A high-precision method of segmenting complex postures in C. elegans and deep phenotyping to analyze lifespan
<p>The data for our paper, "A high-precision method of segmenting complex postures in <em>C. elegans</em> and deep phenotyping to analyze lifespan", includes the following three components:</p> <ol> <li>Synthetic image dataset, CSB-1 dataset, and MD dataset, which originate from the paper <em>"WormSwin: Instance segmentation of C. elegans using Vision Transformer"</em>.</li> <li>BBC010 dataset, sourced from the paper <em>"Annotated high-throughput microscopy image sets for validation".</em></li> <li>Training weights for the Synthetic image dataset, CSB-1 dataset, MD dataset, and BBC010 dataset, as well as the pretrained weights used for worm tracking. The training weights from the synthetic image dataset can serve as pretrained weights for training on other datasets.</li> </ol> <p>Our experimental results are based on the average of multiple training runs; here, we have only uploaded one set of weights per dataset to facilitate reproducibility for readers. For more detailed information on the datasets, please refer to the relevant papers.</p> <p> </p>
Fig. 2. A B in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 2. A B-spline solid is a closed object whose shape can be adjusted by moving control points (dark points) that deforms the local portion of the object near the control point. The initial cylindrical shape in A is adjusted (B and C) by pulling out the points at the ends and drawing the points in the middle closer to the axis.
Fig. 1 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 1. Body segments can be created using mass objects of different density and shape. Mass objects can be collected into mass sets to calculate their combined inertial properties; the most inclusive Tyrannosaurus mass set (whole body) is outlined here, as well as the trunk segment and its embedded mass objects.
Fig. 1 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 1. Body segments can be created using mass objects of different density and shape. Mass objects can be collected into mass sets to calculate their combined inertial properties; the most inclusive Tyrannosaurus mass set (whole body) is outlined here, as well as the trunk segment and its embedded mass objects.
Data for "3DCellComposer - A Versatile Pipeline Utilizing 2D Cell Segmentation Methods for 3D Cell Segmentation"
<p>Segmentation masks and evaluation metrics generated for "3DCellComposer - A Versatile Pipeline Utilizing 2D Cell Segmentation Methods for 3D Cell Segmentation"</p>
Haptics and Targeted Box and Blocks Data for Evaluating Reach and Grasp Segmentation Methods
<p>Data associated with <em>Approaches for Segmenting the Reaching and Targeting Motion Primitives in Functional Upper Extremity Reaching Tasks. </em>Pre-print available at: https://doi.org/10.36227/techrxiv.22672312.v1</p> <p>Code associated with these data available at <a href="https://github.com/kjacks21/UE-reach-grasp-seg">kjacks21/UE-reach-grasp-seg: Segmenting reach and grasp phases of reaching motions (github.com)</a>.</p> <p>Data include kinematics from performing a task in a haptics virtual environment and optical motion capture of participants completing the targeted Box and Blocks test. Please reference the paper for additional information.</p> <p>For questions, please contact kjacks21 [at] gmu [dot] edu or zduric [at] gmu [dot] edu.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.