Mathematical Modeling of Retinal Mosaic Formation by Mechanical Interactions and Dendritic Overlap

A size-invariant convolutional network with dense connectivity applied to retinal vessel segmentation measured by a unique index

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.

Semi-Supervised Learning Method of U-Net Deep Learning Network for Blood Vessel Segmentation in Retinal Images

Blood vessel segmentation methods based on deep neural networks have achieved satisfactory results. However, these methods are usually supervised learning methods, which require large numbers of retinal images with high quality pixel-level ground-truth labels. In practice, the task of labeling these retinal images is very costly, financially and in human effort. To deal with these problems, we propose a semi-supervised learning method which can be used in blood vessel segmentation with limited labeled data. In this method, we use the improved U-Net deep learning network to segment the blood vessel tree. On this basis, we implement the U-Net network-based training dataset updating strategy. A large number of experiments are presented to analyze the segmentation performance of the proposed semi-supervised learning method. The experiment results demonstrate that the proposed methodology is able to avoid the problems of insufficient hand-labels, and achieve satisfactory performance.

A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images

This paper presents a new supervised method for vessel segmentation in retinal images. This method remolds the task of segmentation as a problem of cross-modality data transformation from retinal image to vessel map. A wide and deep neural network with strong induction ability is proposed to model the transformation, and an efficient training strategy is presented. Instead of a single label of the center pixel, the network can output the label map of all pixels for a given image patch. Our approach outperforms reported state-of-the-art methods in terms of sensitivity, specificity and accuracy. The result of cross-training evaluation indicates its robustness to the training set. The approach needs no artificially designed feature and no preprocessing step, reducing the impact of subjective factors. The proposed method has the potential for application in image diagnosis of ophthalmologic diseases, and it may provide a new, general, high-performance computing framework for image segmentation.