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Automated Semen Analysis Using Holographic Imaging

Technology Benefits

• Lightweight, compact and cost-effective• Automated analysis requiring little manual effort

Technology Application

Fertility testing for fertility clinics, personal fertility testing or veterinary medicine.

Detailed Technology Description

The innovation described here is a lightweight, compact and cost-effective platform for conducting automated semen analysis on a chip. The platform leverages a lens-free holographic on-chip microscope and software that is capable of accurately distinguishing and counting motile and immotile sperm.

Application No.

8842901

Others

State Of Development

The method has been prototyped and successfully demonstrated.

Background

Semen analysis is an important and extensively routine for evaluating male fertility and preparing artificial insemination. Current approaches include manually counting sperm through an optical microscope, utilizing complex algorithms to analyze images recorded from microscopes or indirectly qualifying sperm concentration through electrical or chemical means. These approaches fall short of ideal because they are labor-intensive, prohibitively expensive or not sufficiently discriminating of sperm motility. Demand therefore exists for a system that is easy to use while sufficiently fast and accurate.

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• High-Throughput And Label-Free Single Nanoparticle Sizing Based On Time-Resolved On-Chip Microscopy
• Fluorescent Imaging Of Single Nano-Particles And Viruses On A Smart-Phone
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• Revolutionizing Micro-Array Technologies: A Microscopy Method and System Incorporating Nanofeatures
• Sparsity-Based Multi-Height Phase Recovery In Holographic Microscopy
• Computational Out-Of-Focus Imaging Increases The Space-Bandwidth Product In Lens-Based Coherent Microscopy
• Computational Sensing Using Low-Cost and Mobile Plasmonic Readers Designed by Machine Learning
• Deep Learning Microscopy
• Mobile Phone Based Fluorescence Multi-Well Plate Reader
• Phase Recovery And Holographic Image Reconstruction Using Neural Networks
• Lensfree Tomographic Imaging
• Air Quality Monitoring Using Mobile Microscopy And Machine Learning

Tech ID/UC Case

24674/2011-044-0

Related Cases

2011-044-0

*Abstract

UCLA researchers in the department of electrical engineering have developed a compact and lightweight platform for conducting automated semen analysis using a lens-free on-chip microscope.

*IP Issue Date

Sep 23, 2014

*Principal Investigator

Name: Anthony Erlinger

Department:

Name: Aydogan Ozcan

Department:

Name: Ting Wei Su

Department:

Country/Region

USA