Weighted average ensemble-based semantic segmentation in biological electron microscopy images.
Histochem Cell Biol
; 158(5): 447-462, 2022 Nov.
Article
en En
| MEDLINE
| ID: mdl-35988009
ABSTRACT
Semantic segmentation of electron microscopy images using deep learning methods is a valuable tool for the detailed analysis of organelles and cell structures. However, these methods require a large amount of labeled ground truth data that is often unavailable. To address this limitation, we present a weighted average ensemble model that can automatically segment biological structures in electron microscopy images when trained with only a small dataset. Thus, we exploit the fact that a combination of diverse base-learners is able to outperform one single segmentation model. Our experiments with seven different biological electron microscopy datasets demonstrate quantitative and qualitative improvements. We show that the Grad-CAM method can be used to interpret and verify the prediction of our model. Compared with a standard U-Net, the performance of our method is superior for all tested datasets. Furthermore, our model leverages a limited number of labeled training data to segment the electron microscopy images and therefore has a high potential for automated biological applications.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Semántica
/
Procesamiento de Imagen Asistido por Computador
Tipo de estudio:
Prognostic_studies
/
Qualitative_research
Idioma:
En
Revista:
Histochem Cell Biol
Asunto de la revista:
CITOLOGIA
/
HISTOCITOQUIMICA
Año:
2022
Tipo del documento:
Article
País de afiliación:
Alemania