2 research outputs found
Comparison of Different Methods for Tissue Segmentation in Histopathological Whole-Slide Images
Tissue segmentation is an important pre-requisite for efficient and accurate
diagnostics in digital pathology. However, it is well known that whole-slide
scanners can fail in detecting all tissue regions, for example due to the
tissue type, or due to weak staining because their tissue detection algorithms
are not robust enough. In this paper, we introduce two different convolutional
neural network architectures for whole slide image segmentation to accurately
identify the tissue sections. We also compare the algorithms to a published
traditional method. We collected 54 whole slide images with differing stains
and tissue types from three laboratories to validate our algorithms. We show
that while the two methods do not differ significantly they outperform their
traditional counterpart (Jaccard index of 0.937 and 0.929 vs. 0.870, p < 0.01).Comment: Accepted for poster presentation at the IEEE International Symposium
on Biomedical Imaging (ISBI) 201
Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics
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