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playground:doku_tex [2012/05/15 11:27] – created kotaplayground:doku_tex [2016/05/24 12:46] (current) – external edit 127.0.0.1
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 +======Drosophila invagination: cell dynamics======
 +
 +Kota Miura
 +
 +September 15, 2011
 +
 +
 \maketitle\tableofcontents \maketitle\tableofcontents
  
-===Aim=== +====Aim==== 
  
 Analysis of cell shape dynamics before/during the invagination of Drosophila embryo.\\ Analysis of cell shape dynamics before/during the invagination of Drosophila embryo.\\
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 last update: 20110920  last update: 20110920 
  
-===Methods===+====Methods====
  
 To study the details of cell shape changes during the invagination in Drosophila, each cell should be segmented. Image sequences were preprocessed and segmented by following procedure. Unless some notice is made, native function and modules of ImageJ was used.  To study the details of cell shape changes during the invagination in Drosophila, each cell should be segmented. Image sequences were preprocessed and segmented by following procedure. Unless some notice is made, native function and modules of ImageJ was used. 
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 After segmentation, cells were measured for their centroid and occupying area. The particle analysis module in ImageJ was used for this measurement with constant parameter setting for all strains. To link cells between successive frame, a particle linking algorithm developed by \cite{Sbalzarini2005a} was modified. In short, following cost function was designed and used to evaluate the cost for the linking and globally optimize the linking between cells.\[Cost=((x_{t+1}-x_{t})^{2}) + (y_{t+1}-y_{t})^{2}) + \frac{2}{A_{t}}abs(A_{t} - A_{t+1})\]where \((x_{t}, y_{t})\) is the centroid coordinate and \(A_{t}\) is cell area at time point \(t\). The cost is thus the squared displacement between two frames added with the normalized change in the area in between.  During preprocessing and segmentation, some cells were oversegmented due to the limits in the capability of preprocessing. Such artifacts degrades the tracking quality. By using above cost function, wrongly segmented cells were automatically rejected from the track estimation and track linking algorithm steps over such badly segmented time point and links to the corresponding cell in successive frame. Only cells successfully tracked for more than 100 frames were used for the analysis. Note that though with such rejection algorithm, rejection is not 100\ After segmentation, cells were measured for their centroid and occupying area. The particle analysis module in ImageJ was used for this measurement with constant parameter setting for all strains. To link cells between successive frame, a particle linking algorithm developed by \cite{Sbalzarini2005a} was modified. In short, following cost function was designed and used to evaluate the cost for the linking and globally optimize the linking between cells.\[Cost=((x_{t+1}-x_{t})^{2}) + (y_{t+1}-y_{t})^{2}) + \frac{2}{A_{t}}abs(A_{t} - A_{t+1})\]where \((x_{t}, y_{t})\) is the centroid coordinate and \(A_{t}\) is cell area at time point \(t\). The cost is thus the squared displacement between two frames added with the normalized change in the area in between.  During preprocessing and segmentation, some cells were oversegmented due to the limits in the capability of preprocessing. Such artifacts degrades the tracking quality. By using above cost function, wrongly segmented cells were automatically rejected from the track estimation and track linking algorithm steps over such badly segmented time point and links to the corresponding cell in successive frame. Only cells successfully tracked for more than 100 frames were used for the analysis. Note that though with such rejection algorithm, rejection is not 100\
  
-  * Bleach Correction by Histogram matching. To attenuate the decreasing contrast of images due to fluorescence bleaching, histogram matching to the initial frame was applied to successive frames. Histogram matching algorithm and source code is from Burger and Burge\cite{Burger2007}.      * Running average. To reduce the noise, three frames were averaged for each time point using a frame a frame after that time point. ImageJ plugin "Running Z Projector" developed by Nico Stuurman was used \cite{NicoStuurman2002}.     * Background subtraction. To flatten the background intensity gradient, each image in each frame was subtracted with Gaussian-blurred image of that frame.   * BandPass filtering to remove noise. To further reduce noise, each image was band passed by eliminating patterns below 3 pixels and above 40 pixels.   * Grayscale-erosion to refine the segmented cell boundary.  * Trainable segmentation using common training data. "Trainable Segmentation" is a part of the ImageJ bundle "Fiji" .   * Watershed processing to separate some cells undersegmented and overlapping.  * Skeltonize and dilate the binary image to minimize the thickness of cell boundary.  
  
-===Results===+  * Bleach Correction by Histogram matching. To attenuate the decreasing contrast of images due to fluorescence bleaching, histogram matching to the initial frame was applied to successive frames. Histogram matching algorithm and source code is from Burger and Burge\cite{Burger2007}.     
 + 
 +  * Running average. To reduce the noise, three frames were averaged for each time point using a frame a frame after that time point. ImageJ plugin "Running Z Projector" developed by Nico Stuurman was used \cite{NicoStuurman2002}.    
 + 
 +  * Background subtraction. To flatten the background intensity gradient, each image in each frame was subtracted with Gaussian-blurred image of that frame.  
 + 
 +  * BandPass filtering to remove noise. To further reduce noise, each image was band passed by eliminating patterns below 3 pixels and above 40 pixels.  
 + 
 +  * Grayscale-erosion to refine the segmented cell boundary. 
 + 
 +  * Trainable segmentation using common training data. "Trainable Segmentation" is a part of the ImageJ bundle "Fiji" .  
 + 
 +  * Watershed processing to separate some cells undersegmented and overlapping. 
 + 
 +  * Skeltonize and dilate the binary image to minimize the thickness of cell boundary.  
 + 
 +====Results====
  
-==Changes in cell area over time==+===Changes in cell area over time===
  
 Cell areas at each time point were plotted against time (frames). In wild type, gradual increase of variance of measured cell area was observed. This is due to the fact that some cells become smaller as they invaginate while the others increase cell area. This increase, from what could be seen in time-lapse sequence, seems to be due to stretching pulled by invaginating cells  (fig 1 first plot).   Cell areas at each time point were plotted against time (frames). In wild type, gradual increase of variance of measured cell area was observed. This is due to the fact that some cells become smaller as they invaginate while the others increase cell area. This increase, from what could be seen in time-lapse sequence, seems to be due to stretching pulled by invaginating cells  (fig 1 first plot).  
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 I should note that the image quality of BOB cell at the cell boundary was worse than other strains, wild type, TOM and M4. This caused more wrongly segmented cells in BOB. Accordingly area measurement/tracking has more error in this sample. This could be seen as more number of none tracked cells (painted in gray) in the BOB. Over/under segmentation becomes severe especially in the mid zone of the embryo where cells became smaller.  I should note that the image quality of BOB cell at the cell boundary was worse than other strains, wild type, TOM and M4. This caused more wrongly segmented cells in BOB. Accordingly area measurement/tracking has more error in this sample. This could be seen as more number of none tracked cells (painted in gray) in the BOB. Over/under segmentation becomes severe especially in the mid zone of the embryo where cells became smaller. 
  
-==Cell area dynamics and local differences==+===Cell area dynamics and local differences===
  
 Color coding of relative area Color coding of relative area
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 In the TOM strain, compaction of cells were concentrated at the midzone, but interestingly the compaction happened only in one side of the midzone. This could be clearly seen by one-sided blue streaks in the projection image (fig.4d). In the side where cell compaction at the midzone was not happening, cells at the very periphery seems to be showing shrinking of cell area. Although such left-right asymmetry in cell compaction activity is evident, cell tracks show only slight difference in their path (compaction active zone moves slightly more) (fgi5. d).  In the TOM strain, compaction of cells were concentrated at the midzone, but interestingly the compaction happened only in one side of the midzone. This could be clearly seen by one-sided blue streaks in the projection image (fig.4d). In the side where cell compaction at the midzone was not happening, cells at the very periphery seems to be showing shrinking of cell area. Although such left-right asymmetry in cell compaction activity is evident, cell tracks show only slight difference in their path (compaction active zone moves slightly more) (fgi5. d). 
  
-==Single cell behavior==+===Single cell behavior===
  
 To examine in more detail, individual cells were analyzed with their area dynamics, relative positioning within the tissue and tracks.  To examine in more detail, individual cells were analyzed with their area dynamics, relative positioning within the tissue and tracks. 
playground/doku_tex.1337081222.txt.gz · Last modified: 2016/05/24 12:46 (external edit)
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