Deep learning-based instance segmentation for the precise automated
  quantification of digital breast cancer immunohistochemistry images
Blanca Maria Priego-Torresa Barbara Lobato-Delgado Lidia Atienza-Cuevas Daniel Sanchez-Morillo
The quantification of biomarkers on immunohistochemistry breast cancer images
is essential for defining appropriate therapy for breast cancer patients, as
well as for extracting relevant information on disease prognosis. This is an
arduous and time-consuming task that may introduce a bias in the results due to
intra- and inter-observer variability which could be alleviated by making use
of automatic quantification tools. However, this is not a simple processing
task given the heterogeneity of breast tumors that results in non-uniformly
distributed tumor cells exhibiting different staining colors and intensity,
size, shape, and texture, of the nucleus, cytoplasm and membrane. In this
research work, we demonstrate the feasibility of using a deep learning-based
instance segmentation architecture for the automatic quantification of both
nuclear and membrane biomarkers applied to IHC-stained slides. We have solved
the cumbersome task of training set generation with the design and
implementation of a web platform, which has served as a hub for communication
and feedback between researchers and pathologists as well as a system for the
validation of the automatic image processing models. Through this tool, we have
collected annotations over samples of HE, ER and Ki-67 (nuclear biomarkers) and
HER2 (membrane biomarker) IHC-stained images. Using the same deep learning
network architecture, we have trained two models, so-called nuclei- and
membrane-aware segmentation models, which, once successfully validated, have
revealed to be a promising method to segment nuclei instances in IHC-stained
images. The quantification method proposed in this work has been integrated
into the developed web platform and is currently being used as a
decision-support tool by pathologists.