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

gurcan.1@osu.eduwww.bmi.osu.edu

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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