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Liver Segmentation Using Active Learning Ankur Bakshi Allison Petrosino Advisor: Dr. Jacob Furst August 21, 2008
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Agenda Introduction Problem Statement Related Work Liver Segmentation Methods Results Conclusion Questions
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Agenda Introduction Problem Statement Related Work Liver Segmentation Methods Results Conclusion Questions
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Introduction Liver has many important functions Liver cancer is 4 th most common malignancy in the world Computed Tomography (CT) scans are a common tool for diagnosis
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Agenda Introduction Problem Statement Related Work Liver Segmentation Methods Results Conclusion Questions
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Problem Statement Liver Segmentation is an important first step for Computer-Aided Diagnosis (CAD) Difficulties associated with liver segmentation Time consuming Similarities to other organs Source: Comparison and Evaluation of Methods for Liver Segmentation from CT datasets, Heimann et al., 2008
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Agenda Introduction Problem Statement Related Work Liver Segmentation Methods Results Conclusion Questions
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Related Work Heimann et al.- statistical shape based segmentation Susomboon et al.- hybrid liver segmentation Tur et al.- natural language application Tong et al.- text classification Turtinen et al.- texture application Prasad et al.- emphysema classification
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Agenda Introduction Problem Statement Related Work Liver Segmentation Methods Results Conclusion Questions
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Liver Segmentation Algorithm
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Agenda Introduction Problem Statement Related Work Liver Segmentation Methods Results Conclusion Questions
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Methods Explored Passive Learning Active Learning 1000 vs 100 initial examples 100 vs 10 examples added Negatives taken from evaluated non-liver vs. all non-liver Most informative vs Hierarchical Gabor
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Hierarchical Method
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Post-Processing
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Agenda Introduction Problem Statement Related Work Liver Segmentation Methods Results Conclusion Questions
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Results, Patient 1 MethodScore Passive Learning55 Confidence Interval36 Active Learning, 1000 initial examples 81 Active Learning, 100 initial examples 79 Active Learning, 100 examples added 79 Active Learning, 10 examples added 55
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Results, Patient 1 MethodScores 10 added non-evaluated 55 10 added non-liver evaluated 82 Most Informative78 Hierarchical77 Average Human, non- radiologist 75
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Results, Patient 1 Slice 134Slice 135Slice 136 Slice 137Slice 138Slice 139
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Results, Patient 3 ApproachScores Passive0 Confidence Interval0 Active Learning22
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Results, Patient 20 ApproachScores Passive- Confidence Interval59 Active Learning50
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Agenda Introduction Problem Statement Related Work Liver Segmentation Methods Results Conclusion Questions
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Conclusion Classifier based approach outperforms confidence interval based approach Active learning outperforms passive learning Different active learning methods have similar results 10 examples, evaluated non-liver is most promising Interesting structures highlighted for application in CADx systems
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Agenda Introduction Problem Statement Related Work Liver Segmentation Methods Results Conclusion Questions
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