A size-invariant convolutional network with dense connectivity applied to retinal vessel segmentation measured by a unique index. (November 2020)

• Record Type: Journal Article
• Authors: Zhuo, Zhongshuo; Huang, Jianping; Lu, Ke; Pan, Daru; Feng, Shouting
• Is Part Of: Computer methods and programs in biomedicine. Volume 196(2020)
• Publication Date: 2020-11
• Subjects: Retinal vessel -- Convolutional network -- Dense connectivity -- Evaluation metrics; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105508
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We propose a ingenious metric that simplifies the optimal threshold's selection and various methods' comparison. We simultaneously utilize size-invariant feature maps and dense connectivity to improve the CNN's learning ability. The proposed method outperforms those state-of-the-art methods on DRIVE and STARE. Abstract: Background and objectives: Retinal vessel segmentation (RVS) helps in diagnosing diseases such as hypertension, cardiovascular diseases, and others. Convolutional neural networks are widely used in RVS tasks. However, how to comprehensively evaluate the segmentation results and how to improve the networks' learning ability are two great challenges. Methods: In this paper, we proposed an ingenious index: fusion score (FS), which provides an overall measure for those binary images. The FS converts multiple metrics into a single target, and therefore facilitates the optimal threshold's selection and models' comparison. In addition, We simultaneously combined size-invariant feature maps and dense connectivity together to improve the traditional CNN's learning ability. Therefore, a size-invariant convolutional network with dense connectivity is designed for RVS. The size-invariant skill helps the deep layers create feature maps with high resolution. The dense connectivity technique is utilized to integrate those hierarchical features and reuse characteristic maps to enhance the network's learning ability. Finally, an optimized threshold is used on the output image to obtain a binary image. Results: The results of experiments conducted on two shared retinal image databases, DRIVE and STARE, demonstrate that our approach outperforms other techniques when evaluated in terms of F1-score, Matthews correlation coefficient (MCC), G-mean and FS. In addition, the cross training reveals that our method has stronger robustness with respect to training sets. Segmenting a 565 × 584 image only takes 39 ms with a single GPU (graphics processing unit). Conclusions: Compared with those traditional metrics, the FS is a better indicator to measure the results of RVS tasks. The experimental results revealed that the proposed method is more suitable for real-world applications.