Highly Efficient Follicular Segmentation in Thyroid Cytopathological Whole Slide Image
This work addresses a domain-specific problem for medical imaging by improving efficiency and accuracy in thyroid cancer diagnosis, though it is incremental as it builds on existing methods.
The paper tackles efficient follicular segmentation in thyroid cytopathological whole slide images by proposing a hybrid architecture that integrates a classifier into Deeplab V3, achieving 80.9% segmentation accuracy and reducing segmentation time by 93%.
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%.