Neuroblastoma is a cancer of the nervous system and one of the most common tumors in children. In clinical practice,
pathologists examine the haematoxylin and eosin (H&E) stained tissue slides under the microscope for the diagnosis.
According to the International Neuroblastoma Classification System, neuroblastoma tumors are categorized into
favorable and unfavorable histologies. The subsequent treatment planning is based on this classification. However, this
qualitative evaluation is time consuming, prone to error and subject to inter- and intra-reader variations and sampling
bias. To overcome these shortcomings, we are developing a computerized system for the quantitative analysis of
neuroblastoma slides. In this study, we present a novel image analysis system to determine the degree of stromal
development from digitized whole-slide neuroblastoma samples. The developed method uses a multi-resolution
approach that works similar to how pathologists examine slides. Due to their very large resolutions, the whole-slide
images are divided into non-overlapping image tiles and the proposed image analysis steps are applied to each image tile
using a parallel computation infrastructure developed earlier by our group. The computerized system classifies image
tiles as stroma-poor or stroma-rich subtypes using texture characteristics. The developed method has been independently
tested on 20 whole-slide neuroblastoma slides and it has achieved 95% classification accuracy.
Neuroblastic Tumor (NT) is one of the most commonly occurring tumors in children. Of all types of NTs, neuroblastoma
is the most malignant tumor that can be further categorized into undifferentiated (UD), poorly-differentiated (PD) and
differentiating (D) types, in terms of the grade of pathological differentiation. Currently, pathologists determine the
grade of differentiation by visual examinations of tissue samples under the microscope. However, this process is
subjective and, hence, may lead to intra- and inter-reader variability. In this paper, we propose a multi-resolution image
analysis system that helps pathologists classify tissue samples according to their grades of differentiation. The inputs to
this system are color images of haematoxylin and eosin (H&E) stained tissue samples. The complete image analysis
system has five stages: segmentation, feature construction, feature extraction, classification and confidence evaluation.
Due to the large number of input images, both parallel processing and multi-resolution analysis were carried out to
reduce the execution time of the algorithm. Our training dataset consists of 387 images tiles of size 512x512 in pixels
from three whole-slide images. We tested the developed system with an independent set of 24 whole-slide images, eight
from each grade. The developed system has an accuracy of 83.3% in correctly identifying the grade of differentiation,
and it takes about two hours, on average, to process each whole slide image.
Follicular Lymphoma (FL) is a cancer arising from the lymphatic system. Originating from follicle center B cells, FL is
mainly comprised of centrocytes (usually middle-to-small sized cells) and centroblasts (relatively large malignant cells).
According to the World Health Organization's recommendations, there are three histological grades of FL characterized
by the number of centroblasts per high-power field (hpf) of area 0.159 mm<sup>2</sup>. In current practice, these cells are manually
counted from ten representative fields of follicles after visual examination of hematoxylin and eosin (H&E) stained
slides by pathologists. Several studies clearly demonstrate the poor reproducibility of this grading system with very low
inter-reader agreement. In this study, we are developing a computerized system to assist pathologists with this process. A
hybrid approach that combines information from several slides with different stains has been developed. Thus, follicles
are first detected from digitized microscopy images with immunohistochemistry (IHC) stains, (i.e., CD10 and CD20).
The average sensitivity and specificity of the follicle detection tested on 30 images at 2×, 4× and 8× magnifications are
85.5±9.8% and 92.5±4.0%, respectively. Since the centroblasts detection is carried out in the H&E-stained slides, the
follicles in the IHC-stained images are mapped to H&E-stained counterparts. To evaluate the centroblast differentiation
capabilities of the system, 11 hpf images have been marked by an experienced pathologist who identified 41 centroblast
cells and 53 non-centroblast cells. A non-supervised clustering process differentiates the centroblast cells from noncentroblast
cells, resulting in 92.68% sensitivity and 90.57% specificity.
Conference Committee Involvement (1)
10 February 2013 | Lake Buena Vista (Orlando Area), Florida, United States