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Communication Dans Un Congrès Année : 2015

Robust supervised segmentation of neuropathology whole-slide microscopy images

Résumé

Alzheimer's disease is characterized by brain pathological aggregates such as Aβ plaques and neurofibrillary tangles which trigger neuroinflammation and participate to neuronal loss. Quantification of these pathological markers on histological sections is widely performed to study the disease and to evaluate new therapies. However, segmentation of neuropathology images presents difficulties inherent to histology (presence of debris, tissue folding, non-specific staining) as well as specific challenges (sparse staining, irregular shape of the lesions). Here, we present a supervised classification approach for the robust pixel-level classification of large neuropathology whole slide images. We propose a weighted form of Random Forest in order to fit nonlinear decision boundaries that take into account class imbalance. Both color and texture descriptors were used as predictors and model selection was performed via a leave-one-image-out cross-validation scheme. Our method showed superior results compared to the current state of the art method when applied to the segmentation of Aβ plaques and neurofibrillary tangles in a human brain sample. Furthermore, using parallel computing, our approach easily scales-up to large gigabyte-sized images. To show this, we segmented a whole brain histology dataset of a mouse model of Alzheimer's disease. This demonstrates our method relevance as a routine tool for whole slide microscopy images analysis in clinical and preclinical research settings.
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Dates et versions

hal-02155732 , version 1 (13-06-2019)

Identifiants

  • HAL Id : hal-02155732 , version 1

Citer

Michel Vandenberghe, Yael Balbastre, Nicolas Souedet, Anne-Sophie Herard, Marc Dhenain, et al.. Robust supervised segmentation of neuropathology whole-slide microscopy images. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Aug 2015, Milan, France. pp.3851-3854. ⟨hal-02155732⟩
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