  From diagnosing neovascular diseases to detecting white matter lesions,
accurate tiny vessel segmentation in fundus images is critical. Promising
results for accurate vessel segmentation have been known. However, their
effectiveness in segmenting tiny vessels is still limited. In this paper, we
study retinal vessel segmentation by incorporating tiny vessel segmentation
into our framework for the overall accurate vessel segmentation. To achieve
this, we propose a new deep convolutional neural network (CNN) which divides
vessel segmentation into two separate objectives. Specifically, we consider the
overall accurate vessel segmentation and tiny vessel segmentation as two
individual objectives. Then, by exploiting the objective-dependent
(homoscedastic) uncertainty, we enable the network to learn both objectives
simultaneously. Further, to improve the individual objectives, we propose: (a)
a vessel weight map based auxiliary loss for enhancing tiny vessel connectivity
(i.e., improving tiny vessel segmentation), and (b) an enhanced encoder-decoder
architecture for improved localization (i.e., for accurate vessel
segmentation). Using 3 public retinal vessel segmentation datasets (CHASE_DB1,
DRIVE, and STARE), we verify the superiority of our proposed framework in
segmenting tiny vessels (8.3% average improvement in sensitivity) while
achieving better area under the receiver operating characteristic curve (AUC)
compared to state-of-the-art methods.
