Extracting axial depth and trajectory trend using astigmatism, Gaussian fitting, and CNNs for protein tracking
Accurate analysis of vesicle trafficking in live cells is challenging for a number of reasons: varying appearance, complex protein movement patterns, and imaging conditions. To allow fast image acquisition, we study how employing an astigmatism can be utilized for obtaining additional information th...
| প্রধান লেখক: | , , , , , , , |
|---|---|
| বিন্যাস: | Conference item |
| ভাষা: | English |
| প্রকাশিত: |
IEEE
2020
|
| _version_ | 1826302853455544320 |
|---|---|
| author | Delas Penas, K Dmitrieva, M Lefebvre, J Zenner, H Allgeyer, E Booth, M St Johnston, D Rittscher, J |
| author_facet | Delas Penas, K Dmitrieva, M Lefebvre, J Zenner, H Allgeyer, E Booth, M St Johnston, D Rittscher, J |
| author_sort | Delas Penas, K |
| collection | OXFORD |
| description | Accurate analysis of vesicle trafficking in live cells is challenging for a number of reasons: varying appearance, complex protein movement patterns, and imaging conditions. To allow fast image acquisition, we study how employing an astigmatism can be utilized for obtaining additional information that could make tracking more robust. We present two approaches for measuring the <em>z</em> position of individual vesicles. Firstly, Gaussian curve fitting with CNN-based denoising is applied to infer the absolute depth around the focal plane of each localized protein. We demonstrate that adding denoising yields more accurate estimation of depth while preserving the overall structure of the localized proteins. Secondly, we investigate if we can predict using a custom CNN architecture the axial trajectory trend. We demonstrate that this method performs well on calibration beads data without the need for denoising. By incorporating the obtained depth information into a trajectory analysis, we demonstrate the potential improvement in vesicle tracking.
|
| first_indexed | 2024-03-07T05:53:44Z |
| format | Conference item |
| id | oxford-uuid:e9cb51a3-b715-4505-af18-a697dbb8d551 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T05:53:44Z |
| publishDate | 2020 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:e9cb51a3-b715-4505-af18-a697dbb8d5512022-03-27T10:56:56ZExtracting axial depth and trajectory trend using astigmatism, Gaussian fitting, and CNNs for protein trackingConference itemhttp://purl.org/coar/resource_type/c_5794uuid:e9cb51a3-b715-4505-af18-a697dbb8d551EnglishSymplectic ElementsIEEE2020Delas Penas, KDmitrieva, MLefebvre, JZenner, HAllgeyer, EBooth, MSt Johnston, DRittscher, JAccurate analysis of vesicle trafficking in live cells is challenging for a number of reasons: varying appearance, complex protein movement patterns, and imaging conditions. To allow fast image acquisition, we study how employing an astigmatism can be utilized for obtaining additional information that could make tracking more robust. We present two approaches for measuring the <em>z</em> position of individual vesicles. Firstly, Gaussian curve fitting with CNN-based denoising is applied to infer the absolute depth around the focal plane of each localized protein. We demonstrate that adding denoising yields more accurate estimation of depth while preserving the overall structure of the localized proteins. Secondly, we investigate if we can predict using a custom CNN architecture the axial trajectory trend. We demonstrate that this method performs well on calibration beads data without the need for denoising. By incorporating the obtained depth information into a trajectory analysis, we demonstrate the potential improvement in vesicle tracking. |
| spellingShingle | Delas Penas, K Dmitrieva, M Lefebvre, J Zenner, H Allgeyer, E Booth, M St Johnston, D Rittscher, J Extracting axial depth and trajectory trend using astigmatism, Gaussian fitting, and CNNs for protein tracking |
| title | Extracting axial depth and trajectory trend using astigmatism, Gaussian fitting, and CNNs for protein tracking |
| title_full | Extracting axial depth and trajectory trend using astigmatism, Gaussian fitting, and CNNs for protein tracking |
| title_fullStr | Extracting axial depth and trajectory trend using astigmatism, Gaussian fitting, and CNNs for protein tracking |
| title_full_unstemmed | Extracting axial depth and trajectory trend using astigmatism, Gaussian fitting, and CNNs for protein tracking |
| title_short | Extracting axial depth and trajectory trend using astigmatism, Gaussian fitting, and CNNs for protein tracking |
| title_sort | extracting axial depth and trajectory trend using astigmatism gaussian fitting and cnns for protein tracking |
| work_keys_str_mv | AT delaspenask extractingaxialdepthandtrajectorytrendusingastigmatismgaussianfittingandcnnsforproteintracking AT dmitrievam extractingaxialdepthandtrajectorytrendusingastigmatismgaussianfittingandcnnsforproteintracking AT lefebvrej extractingaxialdepthandtrajectorytrendusingastigmatismgaussianfittingandcnnsforproteintracking AT zennerh extractingaxialdepthandtrajectorytrendusingastigmatismgaussianfittingandcnnsforproteintracking AT allgeyere extractingaxialdepthandtrajectorytrendusingastigmatismgaussianfittingandcnnsforproteintracking AT boothm extractingaxialdepthandtrajectorytrendusingastigmatismgaussianfittingandcnnsforproteintracking AT stjohnstond extractingaxialdepthandtrajectorytrendusingastigmatismgaussianfittingandcnnsforproteintracking AT rittscherj extractingaxialdepthandtrajectorytrendusingastigmatismgaussianfittingandcnnsforproteintracking |