  In this paper, we propose a novel method for highly efficient follicular
segmentation of thyroid cytopathological WSIs. Firstly, we propose a hybrid
segmentation architecture, which integrates a classifier into Deeplab V3 by
adding a branch. A large amount of the WSI segmentation time is saved by
skipping the irrelevant areas using the classification branch. Secondly, we
merge the low scale fine features into the original atrous spatial pyramid
pooling (ASPP) in Deeplab V3 to accurately represent the details in
cytopathological images. Thirdly, our hybrid model is trained by a
criterion-oriented adaptive loss function, which leads the model converging
much faster. Experimental results on a collection of thyroid patches
demonstrate that the proposed model reaches 80.9% on the segmentation accuracy.
Besides, 93% time is reduced for the WSI segmentation by using our proposed
method, and the WSI-level accuracy achieves 53.4%.
