# Fast Segmentation Algorithm for Cytoid Macular Edema Based on Omnidirectional Wave Operator
## 3 Results  
The data used in this paper are from an SD-OCT public data set that was collected by [PERSON] et al. from the Visual and Image Processing Laboratory of Duke University [14]. This data set is from the standard Spectralis (Heidelberg Engineering, Heidelberg, Germany) 61-line volume scan protocol. The volume scans were \(Q=61\)\(Bscans\times N=768\)\(Ascans\) with an axial resolution of 3.87 um/pixel, lateral resolutions ranging from 10.94-11.98 um/pixel, and azimuthal resolution ranging from 118-128 um/pixel. This data set contains 110 OCT images of the fundus collected from 10 patients, including normal fundus images and fundus images with CME diseases. The public data set was manually marked by two experts from Duke Eye Center, who are both fellowship-trained medical retina specialists of the faculty at the Duke Eye Center. They have over 5 years of clinical experience working with diabetic subjects and many years of experience in studying OCT images. The boundary obtained by this algorithm is the predicted value, and the cyst manually marked by two experts is the true value. Figure 6a,b show the manual segmentation curve of experts in the database, and Figure 6c is the result of manual segmentation of experts in Tianjin Eye Hospital, China. Among the existing algorithms, two algorithms with accurate segmentation results and strong robustness were selected for comparative experiments. DRLSE [11] and the Snake active contour model [25] both need to select the initial contour, and the segmentation of target areas with complex contours is not good (Figure 6d,e). Figure 6f is the segmentation result of the proposed algorithm and it shows a good segmentation result. Table 1 shows the objective evaluation of the segmentation results obtained by DRLSE, the Snake active contour model, and the proposed algorithm. The Dice index, accuracy, and recall of this algorithm are 81.1%, 88.8%, 75.0%, and 81.3%, respectively. Figure 6: Comparison of algorithm segmentation results, (**a–c**) the results of expert manual segmentation, (**d**) the segmentation result of Snake active contour model algorithm, (**e**) the segmentation result of DRLSE algorithm, (**f**) the result of the proposed algorithm’s segmentation. Among the four indexes, the Dice index measures the similarity between the predicted result and the real result and the F1-score reflects the performance of accuracy and recall rate. The result of the proposed algorithm in this paper is the best. The accuracy of the three algorithms is very high, that is, the algorithm has a strong ability to segment the real target area. The pixels in the non-target area are not judged as the target area, which indicates that the algorithm does not misjudge. Recall is a measure of the recognition rate of real results. It reflects the anti-missing detection ability of the segmentation algorithm. Among the three algorithms, the recall of this algorithm is the highest. It should also be noted that the manual annotation by experts is subjective, and the results of manual annotation by three experts were slightly different, so we used the average value to evaluate the algorithm. We compared the processing speed of the proposed algorithm with Snake and DRLSE. The average time of Snake and DRLSE is 49 s and 18.43 s, while the time of the proposed algorithm is only 1.2 s. The operating time is reduced by an order of magnitude. According to the above evaluation indexes, the algorithm in this paper is not only superior to the other two algorithms in segmentation accuracy but also has great advantages in operation speed. It is a fast and fully automatic algorithm with high segmentation accuracy. This paper proposes an automatic segmentation method with multiple operating directions. The selection of the number of directions has been verified by experiments and theoretical analysis.