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

2. 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)

3. 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

4. Results 1/3
Input
Output

5. 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%

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

7. 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