Triple U-net: Hematoxylin-aware nuclei segmentation with progressive dense feature aggregation.
Med Image Anal
; 65: 101786, 2020 10.
Article
en En
| MEDLINE
| ID: mdl-32712523
ABSTRACT
Nuclei segmentation is a vital step for pathological cancer research. It is still an open problem due to some difficulties, such as color inconsistency introduced by non-uniform manual operations, blurry tumor nucleus boundaries and overlapping tumor cells. In this paper, we aim to leverage the unique optical characteristic of H&E staining images that hematoxylin always stains cell nuclei blue, and eosin always stains the extracellular matrix and cytoplasm pink. Therefore, we extract the Hematoxylin component from RGB images by Beer-Lambert's Law. According to the optical attribute, the extracted Hematoxylin component is robust to color inconsistency. With the Hematoxylin component, we propose a Hematoxylin-aware CNN model for nuclei segmentation without the necessity of color normalization. Our proposed network is formulated as a Triple U-net structure which includes an RGB branch, a Hematoxylin branch and a Segmentation branch. Then we propose a novel feature aggregation strategy to allow the network to fuse features progressively and to learn better feature representations from different branches. Extensive experiments are performed to qualitatively and quantitatively evaluate the effectiveness of our proposed method. In the meanwhile, it outperforms state-of-the-art methods on three different nuclei segmentation datasets.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Núcleo Celular
/
Redes Neurales de la Computación
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Med Image Anal
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2020
Tipo del documento:
Article
País de afiliación:
China