Automated CT Based Liver Volume Assessment - Definiens

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Results: Measurements • Two measurements were used to evaluate segmentation accuracy: – Minimum overlap • A, B Sets of voxels from tracing and classification data respectively – Correlation Coefficient • tp = number of true positive voxels • fp = number of false positive voxels • tn = number of true negative voxels • fn = number of false negative voxels Medical Imaging – Princess Margaret Hospital – University Health Network – Mount Sinai Hospital – University of Toronto
Results: Sample Segmentation I Statistics • CC 0.86 • MO 86% • 9:47 min Medical Imaging – Princess Margaret Hospital – University Health Network – Mount Sinai Hospital – University of Toronto
Page 1 and 2: Automated CT Based Liver Volume Ass
Page 3 and 4: Purpose To evaluate the accuracy of
Page 5 and 6: Methods • Five representative cas
Page 7 and 8: Automatic Segmentation Algorithm Au
Page 9 and 10: Based on intensity and size the bac
Page 11 and 12: Algorithm: Preprocessing Based on s
Page 13 and 14: Algorithm: Liver Seed Liver Seed 1.
Page 15 and 16: Based on size, a single liver seed
Page 17 and 18: Algorithm: Liver refinement/Growing
Page 19 and 20: Algorithm: Liver Refinement/Growing
Page 21: Algorithm: Update Original Image Re
Page 25 and 26: Results: Summary • Correlation co
Page 27 and 28: Conclusion • Fully-automated segm

Automated CT Based Liver Volume Assessment - Definiens

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