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. 2007 Oct 11:2007:304-8.

Computerized pathological image analysis for neuroblastoma prognosis

Metin N Gurcan  1 , Jun Kong, Olcay Sertel, B Barla Cambazoglu, Joel Saltz, Umit Catalyurek
Affiliations

Computerized pathological image analysis for neuroblastoma prognosis

Metin N Gurcan et al. AMIA Annu Symp Proc. .

Abstract

We present a pathological image analysis system for the computer-aided prognosis of neuroblastoma, a childhood cancer. The image analysis system automatically classifies Schwannian stromal development of pathological tissues and determines the grade of differentiation. Due to the demanding computational cost of processing large digitized slides, the system was implemented on a cluster of computers with automated load balancing within a multi-resolution framework. In our experiments, the overall accuracies for stromal classification and the grade of differentiation were 96.6% and 95.3%, respectively. Additionally, the multi-resolution framework reduced the run time of the single resolution approach by 53% and 34% on average for stromal classification and grade of differentiation, respectively. For these two cases, parallelization on a 16-node cluster reduced the sequential run time by 92% and 88% on average. Accuracy and efficiency of these techniques are promising for the development a computer-assisted neuroblastoma prognosis system.

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Figures

Figure 1
Figure 1
Example images of (a) stroma-rich and (b) stroma-poor tissue.
Figure 2
Figure 2
The flowchart for the developed multi-resolution neuroblastoma image analysis system.
Figure 3
Figure 3
The components segmented by EMLDA in a typical image from the undifferentiated class are shown. (a) Original image, (b) Partitioned image shown in color, (c) Nuclei, (d) Cytoplasm, (e) Neuropil, (f) Background.
Figure 4
Figure 4
(a) H&E stained image containing both stroma-rich and stroma-poor tissue (b) computerized labels for stroma-rich (red) and stroma-poor (blue) regions.
See this image and copyright information in PMC

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