Hot-spot selection and evaluation methods for whole slice images of meningiomas and oligodendrogliomas

The paper presents a combined method for an automatic hot-spot areas selection based on penalty factor in the whole slide images to support the pathomorphological diagnostic procedure. The studied slides represent the meningiomas and oligodendrogliomas tumor on the basis of the Ki-67/MIB-1 immunohistochemical reaction. It allows determining the tumor proliferation index as well as gives an indication to the medical treatment and prognosis. The combined method based on mathematical morphology, thresholding, texture analysis and classification is proposed and verified. The presented algorithm includes building a specimen map, elimination of hemorrhages from them, two methods for detection of hot-spot fields with respect to an introduced penalty factor. Furthermore, we propose localization concordance measure to evaluation localization of hot spot selection by the algorithms in respect to the expert's results. Thus, the results of the influence of the penalty factor are presented and discussed. It was found that the best results are obtained for 0.2 value of them. They confirm effectiveness of applied approach.

[1]  S. Osowski,et al.  New automated image analysis method for the assessment of Ki-67 labeling index in meningiomas. , 2010, Folia histochemica et cytobiologica.

[2]  D. Commins,et al.  Review of meningioma histopathology. , 2007, Neurosurgical focus.

[3]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[4]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[5]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[6]  M. Salama,et al.  Practical Approaches to Microvessel Analysis: Hotspots, Microvessel Density, and Vessel Proximity , 2014 .

[7]  Michael Unser,et al.  Sum and Difference Histograms for Texture Classification , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Todd H. Stokes,et al.  Pathology imaging informatics for quantitative analysis of whole-slide images , 2013, Journal of the American Medical Informatics Association : JAMIA.

[9]  Claes Lundström,et al.  Feature-enhancing zoom to facilitate Ki-67 hot spot detection , 2014, Medical Imaging.

[10]  W Feiden,et al.  Automated nuclear segmentation in the determination of the Ki-67 labeling index in meningiomas. , 2006, Clinical neuropathology.

[11]  A. Sari,et al.  MIB-1 counting methods in meningiomas and agreement among pathologists. , 2008, Analytical and quantitative cytology and histology.

[12]  Andrew P Stubbs,et al.  Automated Selection of Hotspots (ASH): enhanced automated segmentation and adaptive step finding for Ki67 hotspot detection in adrenal cortical cancer , 2014, Diagnostic Pathology.

[13]  Abderrahim Elmoataz,et al.  Multi-resolution graph-based analysis of histopathological whole slide images: Application to mitotic cell extraction and visualization , 2011, Comput. Medical Imaging Graph..

[14]  F. Soylemezoğlu,et al.  The significance of immunohistochemical expression of Ki-67, p53, p21, and p16 in meningiomas tissue arrays. , 2008, Pathology, research and practice.

[15]  Masayuki Nakajima,et al.  Significance of MIB-1 Staining Indices in Meningiomas: Comparison of Two Counting Methods , 2001, The American journal of surgical pathology.