2014 Fiscal Year Research-status Report
Simultaneous high-frame-rate recognition of cells fast-flowing in microchannels toward ultra-fast cell sorting
| Project/Area Number |
26820158
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| Research Institution | Hiroshima University |
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Principal Investigator |
顧 慶毅 広島大学, 工学(系)研究科(研究院), 特任助教 (30713979)
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| Project Period (FY) |
2014-04-01 – 2016-03-31
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| Keywords | frame-straddling camera / lab-on-a-chip / cell-based labeling / high speed vision |
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| Outline of Annual Research Achievements |
In the first year, we have constructed a high-frame-rate cell recognition system by designing multi-object feature extraction circuits with FPGA and a PC-based, multi-object tracking algorithm.
(A) Prepare and confirm the real-time frame-straddling function of high-speed vision. We have improved our IDP Express system and verified that the frame-straddling function is working correctly with resolution 512×256 at 4000 fps and that the frame-straddling time can be adjusted from 0 to 0.25 ms in 9.9-ns steps. Furthermore, perform required calibrations for the two camera inputs (get affine transformation parameters). We also confirmed that the captured images contain no motion blur when we set the flow speed to 2 m/s by setting set the exposure time of the two camera heads to the minimum possible exposure time for the IDP Express, 6.25 us.
(B) Design and verify the multi-object tracking and recognition algorithm using offline videos. Firstly, we captured offline, high-frame-rate videos for sea urchin egg cells flowing rapidly in straight type microchannels (wide: 200 us, deep: 100 us). And then, we created a multi-object tracking algorithm on the basis of the offline videos and the multi-object features for two frame-straddling camera heads extracted by using software version of cell-based labeling algorithm.
(C) We designed cell-based labeling circuits for rapidly extracting multi-object features. In this hardware design, the cell size is 4×4, which can provide a more accurate segmentation result, compared to my previous study (cell size: 8×8).
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
In the first year, we have finished our target smoothly.
1. We have prepared and confirmed the real-time frame-straddling function of high-speed vision.
2. We have designed and verified the multi-object tracking and recognition algorithm using offline videos.
3. We have designed and confirmed two parallel multi-object features extraction hardware circuits.
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| Strategy for Future Research Activity |
In the second year, we plan to analysis different type of cells fast-flowing in different type microchannels, to verify the performance of our system.
1. Analysis shape and motion of cells fast-flowing in microchannels. We plan to perform several experiments using sea urchin egg cells fast-flowing in a straight microchannel. To compare the difference between normal and Bouin's fixed sea urchin egg cells, all experiments are performed using those two kinds of sea urchin egg cells. In the first experiment, sea urchin egg cells flowing at different speeds are observed to quantify how their shapes are deformed in fast microchannel flows (flow speed from 125 ul/min to 2000 ul/min). In the second experiment, sea urchin egg cells are observed to quantify their aged deterioration after spawning by inspecting their deformed shapes in fast microchannel flow (500 ul/min).
2. Perform fast recognition of fertilized cells fast-flowing in microchannels.
The shape and inner tissue of fertilized sea urchin egg cells will changed rapidly in two or three days after fertilization. We plan to get technical support from Sakamoto-sensei at Hiroshima University, who can provide us adequate fertilized sea urchin eggs. In this experiment, we perform fast cell recognition of fertilized sea urchin egg cells fast-flowing in straight microchannels hour by hour using hardware extracted bounding box of cells. This experiment shows our system can be used for high-frame-rate image-based (in ROI region of cells) shape recognition and analysis.
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| Causes of Carryover |
平成２６年度の実施内容については、消耗品費を中心に当初計画よりも少ない費用で実現できた一方で、当初計画予定よりも平成２７年度に行う構築システムを用いた実験に対する消耗品費が必要とされることがわかり、次年度使用分と合わせた形で対応する必要がでてきたため。
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| Expenditure Plan for Carryover Budget |
平成２７年度に行う構築システムを用いた実験に対する消耗品費の追加費用として使用する予定である。
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