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chromosome_segementation
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chromosome_segementation
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# chromosome_segementation ## Introduction The repo is aimed for segmentation of overlapping chromosomes, as described in the problem statement given on [AI ON website](http://ai-on.org/projects/visual-segmentation-of-chromosomal-preparations.html) The repo uses [U-Net](https://arxiv.org/abs/1505.04597), state-of-the art segmentation net for segmenting overlapping chromosomes. The repo used Lasagne, a Theano based library for segmentation. ## Methodology The data consists of 4 classes, where class 4 is the common region between 2 overlapping chromosomes. The classes 1 & 2 , are non-overlapping part of each of the chromosomes. Class 0 is the background The performance of the net was observed using mean_dice_score. It was computed as `dice_score = 2*I/(GT + PL)` where I is the sum of the number of pixels predicted correctly except background, GT is the number of pixels which belong to ground-truth except background and PL is the number of pixels in predicted image except background. There were 2 methods of training attempted - Treating all the classes independently (param combine_label = True in segmentation.py) - Treating Class 1 & Class 2 as same i.e. Class 1 and Class 3 as Class 2 (param combine_label = False in segmentation.py)Assumption being the non-overlapping parts inherently aren't different in each chromosomes. Then we can apply conventional CV methods like watershed algorithm to distinguish between the non-overlapping blobs <!-- The training log with combined_label looks like this  The training log without combined_label, i.e. as it is looks like this  --> ##TODO - Build the Watershed algorithm post-processing part to the pipleline in case of combined_label training - Optimise the parameters better for the nets in each case - Build a full pipeline for test_data generator using best validated model ## Results With combined labels, could reach a dice score as high as **0.97**. Without combined labels, could reach a dice score as high as **0.81**. #### Predictions for combined_label   #### Predictions without combining labels  
