image analysis for neuroblastoma classification: hysteresis thresholding for nuclei segmentation...
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Image Analysis for Neuroblastoma Classification: Hysteresis Thresholding for
Nuclei Segmentation
Metin Gurcan1, PhDTony Pan1, MS
Hiro Shimada2, MD, PhDJoel Saltz1, MD, PhD
1Department of Biomedical Informatics, The Ohio State University, Columbus, OH2Children’s Hospital, Los Angeles, CA
CAD
• Computer-aided diagnosis: – a diagnosis made by a
physician using the output of a computerized system
• Computerized system– Automated image (or
data) analysis
Applications
• Breast Cancer
• Lung Cancer
• Colon Cancer
Observational Lapses
• Fatigue• Distraction• Emotional stress• Satisfaction of Search• Variation in reader
CAD
CAD
Physician Decision
Breast Cancer
M. N. Gurcan, B. Sahiner, H. P. Chan, L. Hadjiiski, and N. Petrick, "Selection of an optimal neural network architecture for computer-aided detection of microcalcifications--comparison of automated optimization techniques," Med Phys, vol. 28, pp. 1937-48, 2001.
Lung Cancer
M. N. Gurcan, B. Sahiner, N. Petrick, H. P. Chan, E. A. Kazerooni, P. N. Cascade, and L. Hadjiiski, "Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system," Med Phys, vol. 29, pp. 2552-8, 2002.
Nodule Segmentation
HR 2 (7/23/01)
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M. N. Gurcan, B. H. Allen, S. K. Rogers, D. Dozer, R. Burns, and J. Hoffmeister, "Accurate nodule volume estimation from helical CT images: Comparison of slice-based and volume-based methods," 88th Scientific Assembly and Annual Meeting of Radiological Society of North
America (RSNA), 2002.
Polyp Segmentation
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M. Gurcan, R. Ernst, A. Oto, S. Worrell, J. Hoffmeister, and S. K. Rogers, "Measurement of colonic polyp size from virtual colonoscopy studies: Comparison of manual and automated methods," SPIE Medical Imaging Conference, vol. 6144, 2006.
Measurement
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M. Gurcan, R. Ernst, A. Oto, S. Worrell, J. Hoffmeister, and S. K. Rogers, "Measurement of colonic polyp size from virtual colonoscopy studies: Comparison of manual and automated methods," SPIE Medical Imaging Conference, vol. 6144, 2006.
NB Image Analysis
Image Analysis
Pathologist Decision
NB Image Analysis
Image Analysis
Pathologist Decision
Neuroblastoma Classification
• Stroma Density• Differentiation• Mitosis Karyorrhexis
Index
Identify stroma density
Stroma poor Stroma rich Stroma dominant
Composite:
Stroma-
Poor
Rich
Dominant
Identify differentiation
Undifferentiated Poorly differentiated
Differentiating
MKI Calculation
Low MKI Intermediate
MKI
High
MKI
How to determine MKI?
• The number of the tumor cells in mitosis and karyorrhexis per 5000 NB cells by averaging
• Darker nuclei with irregular, fragmented shapes– This is how they are separated from hyperchromatic
nuclei, which are more roundish uniformly dark cells (dying a silent death)
• Karyorrhexis cells usually have dark pinkish cytoplasm
• Three types– Low ( < 100 / 5000)– Intermediate( 100-200 / 5000 )– High ( > 200 / 5000 )
FlowchartH&E Stained
Image
Color Space Decomposition
Morphological Reconstruction
SegmentedNuclei
Post Processing
Hysteresis Thresholding
Original Region of Interest
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H&E Stained Image
Color Space Decomposition
Morphological Reconstruction
SegmentedNuclei
Post Processing
Hysteresis Thresholding
Complement of the R planeH&E Stained
Image
Color Space Decomposition
Morphological Reconstruction
SegmentedNuclei
Post Processing
Hysteresis Thresholding
Output of the Reconstruction Filter
H&E Stained Image
Color Space Decomposition
Morphological Reconstruction
SegmentedNuclei
Post Processing
Hysteresis Thresholding
Top-hat by ReconstructionH&E Stained
Image
Color Space Decomposition
Morphological Reconstruction
SegmentedNuclei
Post Processing
Hysteresis Thresholding
Hysteresis Thresholding
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H&E Stained Image
Color Space Decomposition
Morphological Reconstruction
SegmentedNuclei
Post Processing
Hysteresis Thresholding
Hysteresis Thresholding
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H&E Stained Image
Color Space Decomposition
Morphological Reconstruction
SegmentedNuclei
Post Processing
Hysteresis Thresholding
Segmented Nuclei
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H&E Stained Image
Color Space Decomposition
Morphological Reconstruction
SegmentedNuclei
Post Processing
Hysteresis Thresholding
Watershed SegmentationH&E Stained
Image
Color Space Decomposition
Morphological Reconstruction
SegmentedNuclei
Post Processing
Hysteresis Thresholding
Output of Final Segmentation
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H&E Stained Image
Color Space Decomposition
Morphological Reconstruction
SegmentedNuclei
Post Processing
Hysteresis Thresholding
Segmentation Example
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Segmentation Example
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Segmentation Example
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Segmentation Example
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Segmentation Evaluation
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||1 AM
AMOS
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AMOS
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A
Experimental Results
Without Hysteresis Thresholding
With
Hysteresis Thresholding
OS1 85.76%±14.05% 90.24%±5.14%
OS2 91.56%±10.39 94.79%±2.97%
Summary
• Feasible to do cell segmentation using morphological operations
• Hysteresis Thresholding improves segmentation accuracy while decreasing variability
Summary
• Application of segmentation algorithm to neuroblastoma classification– MKI calculation
Acknowledgment
• Thomas Barr, Columbus Children’s Hospital
• Dr. Hideki Sano, Los Angeles Children’s Hospital
Questions?
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