Device and method for motility-based label-free detection of motile objects in a fluid sample
Systems and methods for detecting motile objects (e.g., parasites) in a fluid sample by utilizing the locomotion of the parasites as a specific biomarker and endogenous contrast mechanism. The imaging platform includes one or more substantially optically transparent sample holders. The imaging platform has a moveable scanning head containing light sources and corresponding image sensor(s) associated with the light source(s). The light source(s) are directed at a respective sample holder containing a sample and the respective image sensor(s) are positioned below a respective sample holder to capture time-varying holographic speckle patterns of the sample contained in the sample holder. The image sensor(s). A computing device is configured to receive time-varying holographic speckle pattern image sequences obtained by the image sensor(s). The computing device generates a 3D contrast map of motile objects within the sample use deep learning-based classifier software to identify the motile objects.
1. An imaging platform for the label-free detection of motile objects in a sample comprising:
one or more substantially optically transparent sample holders;
a moveable scanning head containing one or more coherent light sources and corresponding image sensor(s) associated with the one or more coherent light sources, wherein no lens is present between the one or more coherent light sources and the corresponding image sensor(s);
a translation stage configured to translate the moveable scanning head along the one or more optically transparent sample holders;
a computing device configured to receive time-varying holographic speckle pattern image sequences obtained by the image sensor(s), the computing device comprising computational motion analysis software configured to generate a three-dimensional (3D) contrast map of the motile objects within the one or more optically transparent sample holders that back-propagates the time-varying holographic speckle pattern image sequences followed by differential analysis, image filtering, and thresholding to find connected regions, the computing device further comprising a deep learning-based classifier software to identify motile objects in the three-dimensional (3D) contrast map.
2. The imaging platform of claim 1 , wherein the sample comprises a biological fluid.
3. The imaging platform of claim 2 , wherein the biological fluid comprises blood.
4. The imaging platform of claim 2 , wherein the biological fluid comprises cerebrospinal fluid.
5. The imaging platform of claim 1 , wherein the one or more optically transparent sample holders comprise one or more capillary tubes.
6. The imaging platform of claim 1 , wherein the scanning head contains one or more light sources selected from the group consisting of laser diodes, light-emitting diodes, and lasers, projecting light onto the one or more optically transparent sample holders.
7. The imaging platform of claim 1 , wherein the translation stage further comprises one or more linear motion shafts holding the moveable scanning head and a stepper motor coupled to the moveable scanning head via a belt.
8. The imaging platform of claim 1 , wherein the moveable scanning head further comprises a one or more heat sinks for the image sensor(s).
9. The imaging platform of claim 1 , wherein the computational motion analysis software performs object function normalization (OFN) to suppress strongly scattering objects within the sample.
10. A method of using the imaging platform of claim 1 , comprising:
loading the sample into the one or more optically transparent sample holders;
translating the moveable scanning head to different regions of the one or more optically transparent sample holders;
obtaining time-varying holographic speckle pattern image sequences using the image sensor(s);
identifying the motile objects in the sample using the computational motion analysis software; and
classify the motile objects in the sample using the deep learning-based classifier software.
11. The method of claim 10 , wherein the sample is first exposed to a lysis buffer prior to loading.
12. The method of claim 10 , wherein the sample is allowed to settle prior to translating the moveable scanning head.
13. The method of claim 10 , wherein the deep learning-based classifier software outputs a count of motile objects in the sample.
14. The method of claim 10 , wherein the deep learning-based classifier software outputs a concentration of motile objects in the sample.
15. The method of claim 10 , wherein the deep learning-based classifier software outputs a positive or negative classification for the sample based on a count or concentration of motile objects in the sample.
16. The method of claim 10 , wherein the sample is a biological sample.
17. The method of claim 10 , wherein the sample is an environmental sample.
18. The method of claim 10 , wherein the motile objects comprise parasites.
19. A method of detecting motile objects in a sample comprising:
obtaining a plurality of time-varying holographic speckle pattern image sequences of the sample using a moveable scanning head containing one or more coherent light sources and corresponding image sensor(s) associated with the one or more coherent light sources wherein no lens is present between the one or more coherent light sources and the corresponding image sensor(s); and
processing the plurality of time-varying holographic speckle pattern image sequences with a computing device configured to receive the time-varying holographic speckle pattern image sequences obtained by the image sensor(s), the computing device comprising computational motion analysis software configured to generate a three-dimensional (3D) contrast map of the motile objects within the one or more optically transparent sample holders that back-propagates the time-varying holographic speckle pattern image sequences followed by differential analysis, image filtering, and thresholdinq to find connected regions, the computing device further comprising a deep learning-based classifier software to identify motile objects in the three-dimensional (3D) contrast map.
20. The method of claim 19 , further comprising the computing device outputting a count of the motile objects.
21. The method of claim 19 , further comprising the computing device outputting a concentration of the motile objects in the sample.