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摘要: 針對目前視網膜血管分割中存在的細小血管提取不完整、分割不準確的問題，從血管形狀拓撲關系利用的角度出發，探索多任務卷積神經網絡設計，提出骨架圖引導的級聯視網膜血管分割網絡框架。該框架包含血管骨架圖提取網絡模塊、血管分割網絡模塊和若干自適應特征融合結構體。骨架提取輔助任務用于提取血管中心線，能夠最大限度地保留血管拓撲結構特征；自適應特征融合結構體嵌入在兩個模塊的特征層間。該結構體通過學習像素級的融合權重，有效地將血管拓撲結構特征與血管局部特征相融合，加強血管特征的結構信息響應。為了獲得更完整的骨架圖，骨架圖提取網絡還引入了基于圖的正則化損失函數用于訓練。與最新的血管分割方法相比，該方法在3個公共視網膜圖像數據集上均獲得第一名，在DRIVE，STARE和CHASEDB1中其F1值分別為83.1%，85.8%和82.0%。消融實驗表明骨架圖引導的視網膜血管分割效果更好，并且，基于圖的正則化損失也能進一步提高血管分割準確性。通過將骨架提取模塊和血管分割模塊替換成不同的卷積網絡驗證了框架的普適性。Abstract: Accurate identification of retinal vessels is essential for assisting doctors in screening early fundus diseases. Diabetes, hypertension, and cardiovascular disease can cause abnormalities of the retinal vascular structure. Retinal vessel segmentation maps can be quickly obtained using the automated retinal vessel segmentation technology, which saves time and cost of manually identifying retinal vessels. Aiming at the problem of incomplete and inaccurate extraction of fine retinal vessels, this paper explored the design of a multitask convolutional neural network and the topological relationship of retinal vessels. A cascaded retinal vessel segmentation network framework guided by a skeleton map was proposed. The auxiliary task of skeleton extraction was used to extract vessel centerlines, which could maximally preserve topological structure information. SAFF cascaded the two modules by remaining embedded between their feature layers. This process could effectively fuse the structural features with the vessel local features by learning pixel-wise fusion weight and thus enhancing the structural response of features in the vessel segmentation module. To obtain a complete skeleton map, the skeleton map extraction module introduced a graph-based regularization loss function for training. Compared with the latest vessel segmentation methods, the proposed approach wins the first place among the three public retinal image datasets. F1 metrics of the proposed method achieved 83.1%, 85.8%, and 82.0% on the DRIVE, STARE, and CHASEDB1 datasets, respectively. Ablation studies have shown that skeleton map-guided vessel segmentation is more effective, and graph-based regularization loss further improves accuracy of the retinal vessel segmentation compared to the vanilla network. Moreover, the framework generality is verified by replacing the skeleton map extraction and vessel segmentation modules with various convolutional networks.
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圖 1 骨架圖引導的視網膜血管分割網絡框架
Figure 1. Skeleton map-guided retinal vessel segmentation framework
圖 4 自適應特征融合模塊和注意力門控的對比。（a）深層特征
$ {{\boldsymbol{f}}_{\rm{g}}} $ 過濾淺層特征$ {{\boldsymbol{f}}_{\rm{l}}} $ ；（b）包含結構信息的骨架特征$ {{\boldsymbol{f}}_{\rm{s}}} $ 和血管特征$ {{\boldsymbol{f}}_{{\rm{ves}}}} $ 融合Figure 4. Comparison of the self-adaptive feature fusion block and attention gating: (a) deeper features
$ {{\boldsymbol{f}}_{\rm{g}}} $ filter shallower features$ {{\boldsymbol{f}}_{\rm{l}}} $ ; (b) vessel features$ {{\boldsymbol{f}}_{{\rm{ves}}}} $ fuse skeleton features$ {{\boldsymbol{f}}_{\rm{s}}} $ containing structural information
圖 5 消融實驗中每輪訓練后在不同驗證集上的F1值。（a）DRIVE；（b）CHASEDB1
Figure 5. F1 on the validation set after each training iteration in ablation experiments: (a) DRIVE; (b) CHASEDB1
圖 6 框架在DRIVE（a）和CHASEDB1（b）數據集上的訓練損失
Figure 6. Training loss of the framework on the DRIVE (a) and CHASEDB1 (b) datasets
表 1 本文提出的方法和近期的先進方法在F1值、敏感性Se、準確率Acc、AUC的比較結果
Table 1. Comparison results between our proposed method and the recent advanced methods of the F1 score, Sensitivity, Accuracy, and AUC
Method DRIVE STARE CHASEDB1 F1/% Se/% Acc/% AUC/% F1/% Se/% Acc/% AUC/% F1/% Se/% Acc/% AUC/% Segment[37] — 76.5 95.4 97.5 — 75.8 96.1 98.0 — 76.3 96.1 97.8 DS[11] — 87.3 95.0 98.0 — 76.7 97.1 98.8 — 76.7 97.7 99.0 DUNet[47] — 78.9 97.0 98.6 — 74.3 97.3 98.7 — 82.3 97.2 98.6 Cascade[49] 80.9 76.5 95.4 — 81.3 75.2 96.4 — 78.1 77.3 96.0 — DualUNet[48] 82.7 79.4 95.7 97.7 — — — — 80.4 80.7 96.6 98.1 CE-Net[34] — 83.1 95.5 97.8 — 78.4 95.8 97.9 — — — — STD[40] — 81.5 97.0 98.6 — — — — — — — — IterNet[46] 82.2 77.9 95.7 98.1 81.5 77.2 97.0 98.8 80.7 79.7 96.6 98.5 Our method 83.1 83.7 97.1 98.8 85.8 86.4 97.1 99.1 82.0 84.5 97.7 99.1 
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表 2 第一組消融實驗結果
Table 2. Results of the first ablation experiments
Control group DRIVE CHASEDB1 F1/% Se/% Acc/% AUC/% F1/% Se/% Acc/% AUC/% Single task
network84.2 84.6 96.5 98.8 81.3 82.9 97.1 98.8 +Skeleton
extraction85.0 85.5 97.7 99.2 81.8 83.6 97.6 99.0 +Structure loss 85.1 86.3 97.7 99.3 82.0 84.5 97.7 99.1
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表 3 第二組消融實驗結果
Table 3. Results of the second ablation experiments
Network DRIVE CHASEDB1 F1/% Se/% Acc/% AUC/% F1/% Se/% Acc/% AUC/% ResNet34 83.1 83.7 97.1 98.8 82.0 84.5 97.1 99.1 ResNet18 82.9 83.5 97.0 98.7 81.7 83.8 97.6 99.0 VGG16 82.8 83.2 97.0 98.7 81.6 83.7 97.6 99.0
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