Computer-aided evaluation of neuroblastoma on whole-slide histology images: Classifying grade of neuroblastic differentiation

Abstract

Neuroblastoma (NB) is one of the most frequently occurring cancerous tumors in children. The current grading evaluations for patients with this disease require pathologists to identify certain morphological characteristics with microscopic examinations of tumor tissues. Thanks to the advent of modern digital scanners, it is now feasible to scan cross-section tissue specimens and acquire whole-slide digital images. As a result, computerized analysis of these images can generate key quantifiable parameters and assist pathologists with grading evaluations. In this study, image analysis techniques are applied to histological images of haematoxylin and eosin (H&E) stained slides for identifying image regions associated with different pathological components. Texture features derived from segmented components of tissues are extracted and processed by an automated classifier group trained with sample images with different grades of neuroblastic differentiation in a multi-resolution framework. The trained classification system is tested on 33 whole-slide tumor images. The resulting whole-slide classification accuracy produced by the computerized system is 87.88%. Therefore, the developed system is a promising tool to facilitate grading whole-slide images of NB biopsies with high throughput.

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

“…Many other methods collect features from either the original RGB image, a converted image to other non-biologically based color spaces (e.g., Lab or HSL), or from a grayscale version of that same image (Al-Kadi, 2010;Basavanhally et al, 2010;Dundar et al, 2011Dundar et al, , 2010Esgiar et al, 2002;Farjam et al, 2007;Glotsos et al, 2008;Huang and Lee, 2009;Jafari-Khouzani and SoltanianZadeh, 2003;Kong et al, 2009;Ozolek et al, 2014;Petushi et al, 2006;Qureshi et al, 2008;Ruiz et al, 2007;Schnorrenberg et al, 1997;Tabesh et al, 2005;Tabesh and Teverovskiy, 2006;Tahir and Bouridane, 2006;Thiran and Macq, 1996;Tuzel et al, 2007;Wang et al, 2010;Wetzel et al, 1999;Weyn et al, 1998;Xu et al 2014aXu et al , 2014b. Since hematoxylin binds to nucleic acids and eosin binds to protein, unmixing the stains allows the feature extraction to directly probe the state of these important biological molecules, whereas features from the mixed image may either miss this signal or be unable to probe them independently.…”

Detection of Cancer in Pap smear Cytological Images Using Bag of Texture Features

“…When identifying stages of cancer, different rules were followed and everyone followed the tissue architectural features. In [8], the author used neuro blastoma histological slides for processing and identifying pathological regions and which are identified by multi resolution framework.…”

Graph-based multi-resolution segmentation of histological whole slide images

“…Fortunately, they are stored as a pyramid of tiled images that enables to process them in a hierarchical way [4]. As a consequence, a multi-resolution segmentation method is a natural approach for segmenting whole slide images.…”