Comput Biol Chem - Image database analysis of Hodgkin lymphoma.

Tópicos

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Resumo

Hodgkin lymphoma (HL) is a special type of B cell lymphoma, arising from germinal center B-cells. Morphological and immunohistochemical features of HL as well as the spatial distribution of malignant cells differ from other lymphoma and cancer types. Sophisticated protocols for immunostaining and the acquisition of high-resolution images become routine in pathological labs. Large and daily growing databases of high-resolution digital images are currently emerging. A systematic tissue image analysis and computer-aided exploration may provide new insights into HL pathology. The automated analysis of high resolution images, however, is a hard task in terms of required computing time and memory. Special concepts and pipelines for analyzing high-resolution images can boost the exploration of image databases. In this paper, we report an analysis of digital color images recorded in high-resolution of HL tissue slides. Applying a protocol of CD30 immunostaining to identify malignant cells, we implement a pipeline to handle and explore image data of stained HL tissue images. To the best of our knowledge, this is the first systematic application of image analysis to HL tissue slides. To illustrate the concept and methods we analyze images of two different HL types, nodular sclerosis and mixed cellularity as the most common forms and reactive lymphoid tissue for comparison. We implemented a pipeline which is adapted to the special requirements of whole slide images of HL tissue and identifies relevant regions that contain malignant cells. Using a preprocessing approach, we separate the relevant tissue region from the background. We assign pixels in the images to one of the six predefined classes: Hematoxylin(+), CD30(+), Nonspecific red, Unstained, Background, and Low intensity, applying a supervised recognition method. Local areas with pixels assigned to the class CD30(+) identify regions of interest. As expected, an increased amount of CD30(+) pixels is a characteristic feature of nodular sclerosis, and the non-lymphoma cases show a characteristically low amount of CD30(+) stain. Images of mixed cellularity samples include cases of high CD30(+) coloring as well as cases of low CD30(+) coloring.

Resumo Limpo

hodgkin lymphoma hl special type b cell lymphoma aris germin center bcell morpholog immunohistochem featur hl well spatial distribut malign cell differ lymphoma cancer type sophist protocol immunostain acquisit highresolut imag becom routin patholog lab larg daili grow databas highresolut digit imag current emerg systemat tissu imag analysi computeraid explor may provid new insight hl patholog autom analysi high resolut imag howev hard task term requir comput time memori special concept pipelin analyz highresolut imag can boost explor imag databas paper report analysi digit color imag record highresolut hl tissu slide appli protocol cd immunostain identifi malign cell implement pipelin handl explor imag data stain hl tissu imag best knowledg first systemat applic imag analysi hl tissu slide illustr concept method analyz imag two differ hl type nodular sclerosi mix cellular common form reactiv lymphoid tissu comparison implement pipelin adapt special requir whole slide imag hl tissu identifi relev region contain malign cell use preprocess approach separ relev tissu region background assign pixel imag one six predefin class hematoxylin cd nonspecif red unstain background low intens appli supervis recognit method local area pixel assign class cd identifi region interest expect increas amount cd pixel characterist featur nodular sclerosi nonlymphoma case show characterist low amount cd stain imag mix cellular sampl includ case high cd color well case low cd color

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