Medical Imaging

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Human somatic label-free bright-field cell images


This cell images dataset is collected using an ultrafast imaging system known as asymmetric-detection time-stretch optical microscopy (ATOM)  for training and evaluation. This novel imaging approach can achieve label-free and high-contrast flow imaging with good cellular resolution images at a very high speed. Each acquired image belongs to one of the four classes: THP1, MCF7, MB231 and PBMC.

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Dataset Details

Citation Author(s):
Submitted by:
Nan Meng
Last updated:
Wed, 03/28/2018 - 10:48
DOI:
10.21227/H2QW97
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[1] , "Human somatic label-free bright-field cell images", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2QW97. Accessed: Jul. 17, 2018.
@data{h2qw97-18,
doi = {10.21227/H2QW97},
url = {http://dx.doi.org/10.21227/H2QW97},
author = { },
publisher = {IEEE Dataport},
title = {Human somatic label-free bright-field cell images},
year = {2018} }
TY - DATA
T1 - Human somatic label-free bright-field cell images
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2QW97
ER -
. (2018). Human somatic label-free bright-field cell images. IEEE Dataport. http://dx.doi.org/10.21227/H2QW97
, 2018. Human somatic label-free bright-field cell images. Available at: http://dx.doi.org/10.21227/H2QW97.
. (2018). "Human somatic label-free bright-field cell images." Web.
1. . Human somatic label-free bright-field cell images [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2QW97
. "Human somatic label-free bright-field cell images." doi: 10.21227/H2QW97

Mobile EEG Recordings in an Art Museum Setting


Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies.  Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.

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Dataset Details

Citation Author(s):
Submitted by:
Justin Brantley
Last updated:
Wed, 03/28/2018 - 10:50
DOI:
10.21227/H2TM00
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[1] , "Mobile EEG Recordings in an Art Museum Setting", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2TM00. Accessed: Jul. 17, 2018.
@data{h2tm00-17,
doi = {10.21227/H2TM00},
url = {http://dx.doi.org/10.21227/H2TM00},
author = { },
publisher = {IEEE Dataport},
title = {Mobile EEG Recordings in an Art Museum Setting},
year = {2017} }
TY - DATA
T1 - Mobile EEG Recordings in an Art Museum Setting
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2TM00
ER -
. (2017). Mobile EEG Recordings in an Art Museum Setting. IEEE Dataport. http://dx.doi.org/10.21227/H2TM00
, 2017. Mobile EEG Recordings in an Art Museum Setting. Available at: http://dx.doi.org/10.21227/H2TM00.
. (2017). "Mobile EEG Recordings in an Art Museum Setting." Web.
1. . Mobile EEG Recordings in an Art Museum Setting [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2TM00
. "Mobile EEG Recordings in an Art Museum Setting." doi: 10.21227/H2TM00

Annotations for Body Organ Localization based on MICCAI LiTS Dataset


We build a set of annotations based on the MICCAI Liver Tumor Segmentation (LiTS) challenge dataset for evaluating methods on organ localization. Bounding boxes of 9 body organs are included in these annotations: heart, left lung, right lung, liver, left kidney, right kidney, bladder, left femoral head and right femoral head.

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Dataset Details

Citation Author(s):
Submitted by:
Xuanang Xu
Last updated:
Tue, 07/10/2018 - 01:32
DOI:
10.21227/df8g-pq27
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[1] , "Annotations for Body Organ Localization based on MICCAI LiTS Dataset", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/df8g-pq27. Accessed: Jul. 17, 2018.
@data{df8g-pq27-18,
doi = {10.21227/df8g-pq27},
url = {http://dx.doi.org/10.21227/df8g-pq27},
author = { },
publisher = {IEEE Dataport},
title = {Annotations for Body Organ Localization based on MICCAI LiTS Dataset},
year = {2018} }
TY - DATA
T1 - Annotations for Body Organ Localization based on MICCAI LiTS Dataset
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/df8g-pq27
ER -
. (2018). Annotations for Body Organ Localization based on MICCAI LiTS Dataset. IEEE Dataport. http://dx.doi.org/10.21227/df8g-pq27
, 2018. Annotations for Body Organ Localization based on MICCAI LiTS Dataset. Available at: http://dx.doi.org/10.21227/df8g-pq27.
. (2018). "Annotations for Body Organ Localization based on MICCAI LiTS Dataset." Web.
1. . Annotations for Body Organ Localization based on MICCAI LiTS Dataset [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/df8g-pq27
. "Annotations for Body Organ Localization based on MICCAI LiTS Dataset." doi: 10.21227/df8g-pq27

A dataset for nuclei segmentation based on Tripple Negative Breast Cancer patients


We make our dataset publicly avaiable. It consists of 50 H&E stained histopathology annotated images at the nuclei level. This dataset is ideal for those who want an exhaustive annotation of H&E breast cancer patient from a Tripple Negative Breast Cancer cohort.

License: Creative Commons Attribution

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Dataset Details

Citation Author(s):
Submitted by:
Peter Naylor
Last updated:
Mon, 06/18/2018 - 06:04
DOI:
10.21227/H26X0H
Data Format:
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[1] , "A dataset for nuclei segmentation based on Tripple Negative Breast Cancer patients", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H26X0H. Accessed: Jul. 17, 2018.
@data{h26x0h-18,
doi = {10.21227/H26X0H},
url = {http://dx.doi.org/10.21227/H26X0H},
author = { },
publisher = {IEEE Dataport},
title = {A dataset for nuclei segmentation based on Tripple Negative Breast Cancer patients},
year = {2018} }
TY - DATA
T1 - A dataset for nuclei segmentation based on Tripple Negative Breast Cancer patients
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H26X0H
ER -
. (2018). A dataset for nuclei segmentation based on Tripple Negative Breast Cancer patients. IEEE Dataport. http://dx.doi.org/10.21227/H26X0H
, 2018. A dataset for nuclei segmentation based on Tripple Negative Breast Cancer patients. Available at: http://dx.doi.org/10.21227/H26X0H.
. (2018). "A dataset for nuclei segmentation based on Tripple Negative Breast Cancer patients." Web.
1. . A dataset for nuclei segmentation based on Tripple Negative Breast Cancer patients [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H26X0H
. "A dataset for nuclei segmentation based on Tripple Negative Breast Cancer patients." doi: 10.21227/H26X0H

Indian Diabetic Retinopathy Image Dataset (IDRiD)


Diabetic Retinopathy is the most prevalent cause of avoidable vision impairment, mainly affecting working age population in the world. Recent research has given a better understanding of requirement in clinical eye care practice to identify better and cheaper ways of identification, management, diagnosis and treatment of retinal disease. The importance of diabetic retinopathy screening programs and difficulty in achieving reliable early diagnosis of diabetic retinopathy at a reasonable cost needs attention to develop computer-aided diagnosis tool.

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Citation Author(s):
Submitted by:
Prasanna Porwal
Last updated:
Fri, 07/13/2018 - 07:32
DOI:
10.21227/H25W98
Data Format:
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[1] , "Indian Diabetic Retinopathy Image Dataset (IDRiD)", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H25W98. Accessed: Jul. 17, 2018.
@data{h25w98-18,
doi = {10.21227/H25W98},
url = {http://dx.doi.org/10.21227/H25W98},
author = { },
publisher = {IEEE Dataport},
title = {Indian Diabetic Retinopathy Image Dataset (IDRiD)},
year = {2018} }
TY - DATA
T1 - Indian Diabetic Retinopathy Image Dataset (IDRiD)
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H25W98
ER -
. (2018). Indian Diabetic Retinopathy Image Dataset (IDRiD). IEEE Dataport. http://dx.doi.org/10.21227/H25W98
, 2018. Indian Diabetic Retinopathy Image Dataset (IDRiD). Available at: http://dx.doi.org/10.21227/H25W98.
. (2018). "Indian Diabetic Retinopathy Image Dataset (IDRiD)." Web.
1. . Indian Diabetic Retinopathy Image Dataset (IDRiD) [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H25W98
. "Indian Diabetic Retinopathy Image Dataset (IDRiD)." doi: 10.21227/H25W98

Photoacoustic Source Detection and Reflection Artifact Deep Learning Dataset


Interventional applications of photoacoustic imaging typically require visualization of point-like targets, such as the small, circular, cross-sectional tips of needles, catheters, or brachytherapy seeds. When these point-like targets are imaged in the presence of highly echogenic structures, the resulting photoacoustic wave creates a reflection artifact that may appear as a true signal. We propose to use deep learning techniques to identify these type of noise artifacts for removal in experimental photoacoustic data.

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Citation Author(s):
Submitted by:
Derek Allman
Last updated:
Mon, 06/25/2018 - 12:54
DOI:
10.21227/H2ZD39
 
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[1] , "Photoacoustic Source Detection and Reflection Artifact Deep Learning Dataset", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2ZD39. Accessed: Jul. 17, 2018.
@data{h2zd39-18,
doi = {10.21227/H2ZD39},
url = {http://dx.doi.org/10.21227/H2ZD39},
author = { },
publisher = {IEEE Dataport},
title = {Photoacoustic Source Detection and Reflection Artifact Deep Learning Dataset},
year = {2018} }
TY - DATA
T1 - Photoacoustic Source Detection and Reflection Artifact Deep Learning Dataset
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2ZD39
ER -
. (2018). Photoacoustic Source Detection and Reflection Artifact Deep Learning Dataset. IEEE Dataport. http://dx.doi.org/10.21227/H2ZD39
, 2018. Photoacoustic Source Detection and Reflection Artifact Deep Learning Dataset. Available at: http://dx.doi.org/10.21227/H2ZD39.
. (2018). "Photoacoustic Source Detection and Reflection Artifact Deep Learning Dataset." Web.
1. . Photoacoustic Source Detection and Reflection Artifact Deep Learning Dataset [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2ZD39
. "Photoacoustic Source Detection and Reflection Artifact Deep Learning Dataset." doi: 10.21227/H2ZD39

Datasets TMI paper


3D+t image real and synthetic  sequences of cells of the A549 lung adenocarcinoma cancer cell line, displaying three different phenotypes of CRMP-2, a protein involved in the assembly and disassembly of actin filaments. All real videos were acquired on aUltraviewERS (Perkin Elmer, Inc., Waltham, MA, USA), spinning disk confocal microscope, using the 488 nm line of an Ar/Kr laser to image the sample through a Plan-Apochromatic 63x 1.20 NA water immersion objective lens (Carl Zeiss, AG., Wetzlar, Germany). The videos contained one cell, imaged every two minutes duringone hour.

License: Creative Commons Attribution

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Dataset Details

Citation Author(s):
Submitted by:
Carlos Ortiz De...
Last updated:
Sun, 04/22/2018 - 06:32
DOI:
10.21227/H26W9K
 
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[1] , "Datasets TMI paper", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H26W9K. Accessed: Jul. 17, 2018.
@data{h26w9k-18,
doi = {10.21227/H26W9K},
url = {http://dx.doi.org/10.21227/H26W9K},
author = { },
publisher = {IEEE Dataport},
title = {Datasets TMI paper},
year = {2018} }
TY - DATA
T1 - Datasets TMI paper
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H26W9K
ER -
. (2018). Datasets TMI paper. IEEE Dataport. http://dx.doi.org/10.21227/H26W9K
, 2018. Datasets TMI paper. Available at: http://dx.doi.org/10.21227/H26W9K.
. (2018). "Datasets TMI paper." Web.
1. . Datasets TMI paper [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H26W9K
. "Datasets TMI paper." doi: 10.21227/H26W9K

Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data


The investigation of the performance of different Positron Emission Tomography (PET) reconstruction and motion compensation methods requires an accurate and realistic representation of the anatomy and motion trajectories as observed in real subjects during acquisitions. The generation of well- controlled clinical datasets is difficult due to the many different clinical protocols, scanner specifications, patient sizes and physiological variations. Alternatively, computational phantoms can be used to generate large datasets for different disease states, providing a ground truth.

License: Creative Commons Attribution

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Citation Author(s):
Submitted by:
IRENE POLYCARPOU
Last updated:
Sun, 02/04/2018 - 12:37
DOI:
10.21227/H21D3X
 
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[1] , "Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H21D3X. Accessed: Jul. 17, 2018.
@data{h21d3x-18,
doi = {10.21227/H21D3X},
url = {http://dx.doi.org/10.21227/H21D3X},
author = { },
publisher = {IEEE Dataport},
title = {Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data},
year = {2018} }
TY - DATA
T1 - Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H21D3X
ER -
. (2018). Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data. IEEE Dataport. http://dx.doi.org/10.21227/H21D3X
, 2018. Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data. Available at: http://dx.doi.org/10.21227/H21D3X.
. (2018). "Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data." Web.
1. . Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H21D3X
. "Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data." doi: 10.21227/H21D3X

SUT-Lips-DB - A database of lips traces


A database of lips traces
Cheiloscopy is a forensic investigation technique that deals with identification of humans based on lips traces. Lip prints are unique and permanent for each individual, and next to the fingerprinting, dental identification, and DNA analysis can be one of the basis for criminal/forensics analysis.

License: Creative Commons Attribution

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Citation Author(s):
Submitted by:
Dariusz Mrozek
Last updated:
Sat, 06/16/2018 - 23:18
DOI:
10.21227/H2R04P
Data Format:
Links:
 
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[1] , "SUT-Lips-DB - A database of lips traces", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2R04P. Accessed: Jul. 17, 2018.
@data{h2r04p-17,
doi = {10.21227/H2R04P},
url = {http://dx.doi.org/10.21227/H2R04P},
author = { },
publisher = {IEEE Dataport},
title = {SUT-Lips-DB - A database of lips traces},
year = {2017} }
TY - DATA
T1 - SUT-Lips-DB - A database of lips traces
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2R04P
ER -
. (2017). SUT-Lips-DB - A database of lips traces. IEEE Dataport. http://dx.doi.org/10.21227/H2R04P
, 2017. SUT-Lips-DB - A database of lips traces. Available at: http://dx.doi.org/10.21227/H2R04P.
. (2017). "SUT-Lips-DB - A database of lips traces." Web.
1. . SUT-Lips-DB - A database of lips traces [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2R04P
. "SUT-Lips-DB - A database of lips traces." doi: 10.21227/H2R04P

Band-Limited Stokes LDDMM


The class of registration methods proposed in the framework of Stokes Large Deformation
Diffeomorphic Metric Mapping is a particularly interesting family of physically
meaningful diffeomorphic registration methods.
Stokes-LDDMM methods are formulated as a conditioned variational problem,
where the different physical models are imposed using the associated partial differential equations
as hard constraints.
The most significant limitation of Stokes-LDDMM framework is its huge computational complexity.

License: Creative Commons Attribution

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No Data files have been uploaded.

Dataset Details

Citation Author(s):
Submitted by:
Monica Hernandez
Last updated:
Tue, 10/10/2017 - 04:15
DOI:
10.21227/H24D0Q
Data Format:
 
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[1] , "Band-Limited Stokes LDDMM", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H24D0Q. Accessed: Jul. 17, 2018.
@data{h24d0q-17,
doi = {10.21227/H24D0Q},
url = {http://dx.doi.org/10.21227/H24D0Q},
author = { },
publisher = {IEEE Dataport},
title = {Band-Limited Stokes LDDMM},
year = {2017} }
TY - DATA
T1 - Band-Limited Stokes LDDMM
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H24D0Q
ER -
. (2017). Band-Limited Stokes LDDMM. IEEE Dataport. http://dx.doi.org/10.21227/H24D0Q
, 2017. Band-Limited Stokes LDDMM. Available at: http://dx.doi.org/10.21227/H24D0Q.
. (2017). "Band-Limited Stokes LDDMM." Web.
1. . Band-Limited Stokes LDDMM [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H24D0Q
. "Band-Limited Stokes LDDMM." doi: 10.21227/H24D0Q