RT journal article
T1 Deep learning-based instance segmentation for the precise automated quantification of digital breast cancer immunohistochemistry images
A1 Priego Torres, Blanca María
A1 Lobato Delgado, Bárbara
A1 Atienza Cuevas, Lidia
A1 Sánchez Morillo, Daniel
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
K1 Breast cancer
K1 IHC quantification
K1 Instance segmentation
K1 Deep learning
K1 Biomarkers
AB 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.
PB PERGAMON-ELSEVIER SCIENCE LTD
SN 0957-4174
YR 2022
FD 2022-01-18
LK http://hdl.handle.net/10498/30088
UL http://hdl.handle.net/10498/30088
LA eng
NO After the 24 months embargo, this version of the article was accepted for publication, after peer review and does not reflect post-acceptance improvements, or any corrections. The published version is available online (2022-01-14) at: https://doi.org/10.1016/j.eswa.2021.116471.
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026