Open AccessWeakly supervised retinal vessel segmentation algorithm without groundtruth
Author(s) -
Lu Zheng,
Chen Dali,
Xue Dingyu
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2020.1893
Subject(s) - computer science , artificial intelligence , cluster analysis , segmentation , artificial neural network , pattern recognition (psychology) , feature (linguistics) , feature extraction , field (mathematics) , domain (mathematical analysis) , image (mathematics) , mathematics , mathematical analysis , philosophy , linguistics , pure mathematics
In the current image processing field, medical image segmentation needs a lot of groundtruths, and the process of making these groundtruths is time‐consuming and laborious. Thus, a novel retinal vessel segmentation algorithm without groundtruth is proposed in this Letter. The hierarchical clustering algorithm is first used to binary classify vessel and non‐vessel pixels. Then classification results based on DRIVE databases are used as pseudo groundtruths to train the neural networks and transfer learning is considered for subsequent processing. Next the trained network is used as the feature extraction tool, by calculating and comparing the difference of image features between the target domain data (DRIVE database) and the source domain data (STARE, CHASE DB1, and HRF databases) extracted from the network. The data required for training is expanded based on semi‐supervised clustering in this image feature space, finally the deep neural network is further fine‐tuned. Experiments on the publicly available fundus image dataset DRIVE demonstrate that the proposed method outperforms many other state‐of‐the‐art weakly supervised and unsupervised methods.