Update README.md

README.md

@@ -260,7 +260,9 @@ followed by the standard error of the mean. *
 Table 1 is represented by three separate tables, describing each post-processing methods separately.
 
 ![table_1_original](/images/table_1_original.pdf)
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 ![table_1_dcan](/images/table_1_dcan.pdf)
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 ![table_1_our](/images/table_1_our.pdf)
 
 *Table 2. Comparison of the Mask R-CNN and the SA-FCN models’ performance in terms of classification (F1 Score) and segmentation (Dice Index). Scores are represented by mean
@@ -270,9 +272,11 @@ value from five training followed by the standard error of the mean.*
 Table 2 is represented by two separate tables, describing each model scores separately.
 
 ![table2_mask_rcnn](/images/table2_mask_rcnn.pdf)
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 ![table_2_sa_fcn](/images/table_2_sa_fcn.pdf)
 
 *Fig. 4. Visualisation of three post-processing methods on the example of one sample. Images headers describe postprocessing actions applied on sample.*
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 ![figure_4](/images/figure_4.pdf)
 
 *Fig. 5. Visualisation of the contour prediction of the sample, presenting the misalignment problem. The bottom right image show superposed ground truth and prediction 
@@ -282,7 +286,8 @@ black colour is used to mark properly predicted pixels.*
 ![figure_5](/images/figure_5.pdf)
 
 *Fig. 6. Visualisation of the same sample prediction before and after post-processing for the Mask R-CNN and SA-FCN models.*
-![figure_5](/images/figure_6.pdf)
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+![figure_6](/images/figure_6.pdf)
 
 
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