| Title |
Wide-field pathology imaging using on-chip microscopy
|
|---|---|
| Published in |
Virchows Archiv, May 2015
|
| DOI | 10.1007/s00428-015-1782-z |
| Pubmed ID | |
| Authors |
Yibo Zhang, Alon Greenbaum, Wei Luo, Aydogan Ozcan |
| Abstract |
As the primary imaging tool to assist the examination of pathological samples, the conventional light microscope suffers from limited throughput, relatively high cost, bulky size, lack of portability, and requirement for focus adjustment. All of these drawbacks partially limit the use of light microscopy tools in resource-limited settings. Lens-free on-chip microscopy can help to address these drawbacks and achieve high-throughput pathology slide imaging without using lenses or objectives. Here, we review the performance of this lens-free imaging platform by showing examples of its performance with various samples including normal and sickle-cell disease blood smears and human carcinoma of the breast. This lens-free computational microscopy platform is a promising tool that can serve high-throughput pathology needs especially in resource-poor settings. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 52 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 27% |
| Student > Ph. D. Student | 6 | 12% |
| Professor | 4 | 8% |
| Student > Doctoral Student | 3 | 6% |
| Student > Bachelor | 3 | 6% |
| Other | 10 | 19% |
| Unknown | 12 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 12 | 23% |
| Medicine and Dentistry | 5 | 10% |
| Computer Science | 4 | 8% |
| Biochemistry, Genetics and Molecular Biology | 4 | 8% |
| Chemistry | 2 | 4% |
| Other | 11 | 21% |
| Unknown | 14 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 September 2015.
All research outputs
#20,291,881
of 22,828,180 outputs
Outputs from Virchows Archiv
#1,726
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Outputs of similar age
#222,941
of 266,258 outputs
Outputs of similar age from Virchows Archiv
#18
of 18 outputs
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