CellNetQL Image Segmentation without Feature Definition The Briefest Presentation by N. Kharma & A. Mazhurin

Task Given an image, partially segmented, by hand Automatically create a machine which would segment the rest of the image Given a set of training images + matching ground truths 1 Automatically create a machine which would automatically segment other images (from the same database, say)

Method Step 1: Extract Instances, using Original Image and matching Ground Truth: An instance is made of a random selection of pixels & ‘hyper-pixels’ from the neighbourhood of the central pixel in the original image + the correct class of the central pixel (from the ground truth) Step 2: Use the instance set to train a classifier; in our case an SVM Step 3: Use an optimizer In our case ME2: Map, Explore & Exploit) to optimize pixel selection & other parameters of QL Step 4: When the training results are good enough, run the optimized QL on unseen images Step 5: Use ISAT1.0 to quantify results ISAT returns both pixel-based and region-based segmentation quality results on any pair of (segmented, GT) images

Results 1/3 Input Output

Results 2/3 Input < A different Original Image + Ground Truth > Output Sensitivity – 95.0% Specificity – 99.5% Accuracy – 95.0% Sensitivity – 96.5% Specificity – 99.7% Accuracy – 98.1% Sensitivity – 88.5% Specificity – 99.9% Accuracy – 94.2% Sensitivity – 94.9% Specificity – 98.8% Accuracy – 96.9%

Sensitivity – 85.5% Specificity – 88.9% Results 3/3 Input Output Sensitivity – 85.5% Specificity – 88.9% Accuracy – 87.2%

Optimization & results generation is in progress CellNetQL, embodies a method that can uses supervised learning to segment any image, as long as local information appears adequate CellNetQL does not require continuous expert update, to improve the quality of features for particular applications CellNetQL is being tested on all kinds of images and against the strongest commercial competitor, GENIEPro Optimization & results generation is in progress Conclusion Qualifications + Future