  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.
