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19 April 2024 |
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Article overview
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Automated Image Analysis of Hodgkin lymphoma | Alexander Schmitz
; Tim Schäfer
; Hendrik Schäfer
; Claudia Döring
; Jörg Ackermann
; Norbert Dichter
; Sylvia Hartmann
; Martin-Leo Hansmann
; Ina Koch
; | Date: |
14 Sep 2012 | Abstract: | Hodgkin lymphoma is an unusual type of lymphoma, arising from malignant
B-cells. Morphological and immunohistochemical features of malignant cells and
their distribution differ from other cancer types. Based on systematic tissue
image analysis, computer-aided exploration can provide new insights into
Hodgkin lymphoma pathology. In this paper, we report results from an image
analysis of CD30 immunostained Hodgkin lymphoma tissue section images. To the
best of our knowledge, this is the first systematic application of image
analysis to a set of tissue sections of Hodgkin lymphoma. We have implemented
an automatic procedure to handle and explore image data in Aperio’s SVS format.
We use pre-processing approaches on a down-scaled image to separate the image
objects from the background. Then, we apply a supervised classification method
to assign pixels to predefined classes. Our pre-processing method is able to
separate the tissue content of images from the image background. We analyzed
three immunohistologically defined groups, non-lymphoma and the two most common
forms of Hodgkin lymphoma, nodular sclerosis and mixed cellularity type. We
found that nodular sclerosis and non-lymphoma images exhibit different amounts
of CD30 stain, whereas mixed cellularity type exhibits a large variance and
overlaps with the other groups. The results can be seen as a first step to
computationally identify tumor regions in the images. This allows us to focus
on these regions when performing computationally expensive tasks like object
detection in the high-resolution image. | Source: | arXiv, 1209.3189 | Services: | Forum | Review | PDF | Favorites |
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