Publikationsdatenbank

A Guided Spatial Transformer Network for Histology Cell Differentiation (2017)

Art

Elektronische Veröffentlichung

Autoren

Aubreville, Marc

Klopfleisch, Robert (WE 12)

Bertram, Christof (WE 12)

Maier, Andreas

Verweise

URL (Volltext): https://diglib.eg.org/handle/10.2312/vcbm20171233

Kontakt

Institut für Tierpathologie

Robert-von-Ostertag-Str. 15
14163 Berlin
+49 30 838 62450
pathologie@vetmed.fu-berlin.de

Abstract / Zusammenfassung

Identification and counting of cells and mitotic figures is a standard task in diagnostic histopathology. Due to the large overall
cell count on histological slides and the potential sparse prevalence of some relevant cell types or mitotic figures, retrieving
annotation data for sufficient statistics is a tedious task and prone to a significant error in assessment. Automatic classification
and segmentation is a classic task in digital pathology, yet it is not solved to a sufficient degree.
We present a novel approach for cell and mitotic figure classification, based on a deep convolutional network with an incorporated
Spatial Transformer Network. The network was trained on a novel data set with ten thousand mitotic figures, about ten
times more than previous data sets. The algorithm is able to derive the cell class (mitotic tumor cells, non-mitotic tumor cells
and granulocytes) and their position within an image. The mean accuracy of the algorithm in a five-fold cross-validation is
91.45 %.
In our view, the approach is a promising step into the direction of a more objective and accurate, semi-automatized mitosis
counting supporting the pathologist.