Image Analysis for Neuroblastoma Classification: Hysteresis Thresholding for Nuclei Segmentation Metin Gurcan 1, PhD Tony Pan 1, MS Hiro Shimada 2, MD, PhD Joel Saltz 1, MD, PhD 1 Department of Biomedical Informatics, The Ohio State University, Columbus, OH 2 Children’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 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 , 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 , 2002.

Nodule Segmentation 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 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 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 poorStroma richStroma dominant Composite: Stroma- Poor Rich Dominant

Identify differentiation UndifferentiatedPoorly differentiated Differentiating

MKI Calculation Low MKIIntermediate 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( / 5000 ) –High ( > 200 / 5000 )

Flowchart

Original Region of Interest

Complement of the R plane

Output of the Reconstruction Filter

Top-hat by Reconstruction

Hysteresis Thresholding Th Tl

Hysteresis Thresholding Th Tl

Segmented Nuclei

Watershed Segmentation

Output of Final Segmentation

Segmentation Example

Segmentation Evaluation M A

Experimental Results Without Hysteresis Thresholding With Hysteresis Thresholding OS %±14.05%90.24%±5.14% OS %± %±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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