Learning to segment microscopy images with lazy labels
Idioma
en
Communication dans un congrès
Este ítem está publicado en
ECCV Workshop on BioImage Computing (BIC'20), 2020-08-23, Glasgow. 2020-08-23p. 411-428
Resumen en inglés
The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for segmenting bioimages that can contain numerous object instances with thin separations. In this paper, ...Leer más >
The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for segmenting bioimages that can contain numerous object instances with thin separations. In this paper, we introduce a deep convolutional neural network for microscopy image segmentation. Annotation issues are circumvented by letting the network being trainable on coarse labels combined with only a very small number of images with pixel-wise annotations. We call this new labelling strategy `lazy' labels. Image segmentation is stratified into three connected tasks: rough inner region detection, object separation and pixel-wise segmentation. These tasks are learned in an end-to-end multi-task learning framework. The method is demonstrated on two microscopy datasets, where we show that the model gives accurate segmentation results even if exact boundary labels are missing for a majority of annotated data. It brings more flexibility and efficiency for training deep neural networks that are data hungry and is applicable to biomedical images with poor contrast at the object boundaries or with diverse textures and repeated patterns.< Leer menos
Palabras clave en inglés
Multi-task learning
Convolutional neural networks
Image segmentation
Proyecto europeo
Nonlocal Methods for Arbitrary Data Sources
Orígen
Importado de HalCentros de investigación