A Novel Segmentation Method for Optic Disc and Optic Cup Zeeshan Mughal BEEE, -Abasyn Uniuversity Islamabad , Park Road Chak Shahzad Islamabad, Pakistan shanmughal794@gmail.com Abstract— Glaucoma is one of the major leading causes of blindness all over the world, and it is a kind of chronic eye fundus disease. Precise detection of the early stage of glaucoma is crucial for glaucoma treatment. The vertical cup-to-disc ratio (VCDR) is a key parameter for glaucoma detection. Thus, the accurate segmentation of optic disc and cup on the retina is the critical step for glaucoma detection. Most of existing segmentation methods either have unsatisfactory segmentation results or consume amounts of time costs. In this paper, we propose an efficient segmentation method based on deformable u-net for optic disc and optic cup. We combine the deformable convolution with the original u-net and design a novelar chitecture for optic disc and cup segmentation. Finally, wee valuate our method on several publicly datasets DRIONS-DB,RIM-ONE v3, DRISHTIGS. The experiments show that our method achieves satisfactory results, which also show promising performance for glaucoma detection. Keywords- Deformable convolution; glaucoma detection ;optic disc and cup; segmentation; I. INTRODUCTION Glaucoma is the second biggest ocular disease in theworld which can lead to permanent blindness [1]. It is predicted that the number of people with glaucoma worldwide will increase from 64.3 million in 2013 to 111.8 million in 2040 [2]. As a chronic fundus disease, there is no obvious symptom in the early stage, but with the degeneration of disease, the structure of optic nerve head will change gradually, optic nerve cells will die, and optic nerve fibers will also be degenerated, finally, it will lead to permanent blindness. So Glaucoma is also known as “the thief of vision”. Due to the irreversible damage caused by glaucoma, the early diagnosis of glaucoma is crucial to help patients to treat the disease and control the progression of Glaucoma. There are three main different sets of examinations for glaucoma detection: (1) assessment of intraocular pressure (IOP), (2) assessment of visual field, (3) assessment of optic nerve head. The assessment of IOP is not sufficient enough, because different types of glaucoma have different IOP conditions, glaucoma can also be present without raised intraocular pressure. Meanwhile, evaluation of abnormal visual field requires expensive medical devices which can only be found in large hospitals, thus it is not suitable for feature, and the other is based on automatic extracted feature. The former refers to the traditional image process techniques, such as the level-set based approach, thresholding based approach, active shape modeling approach, and the later mainly refers to the popular deep learning techniques. With the great development of deep learning, methods based on deep learning have made a great progress in many computer visions tasks. Especially, after the occurrence of Fully Connected Network In order to further improve the performance of segmentation of OD and OC for glaucoma detection, we propose a new method which is based on a deformable u-net for segmentation of OD and OC. Our method is inspired by deformable convolutional networks [5] and deformable u-net for Sickle Cell Disease diagnosis [6]. The main contributions of our work are as follows: (1) We apply the deformable convolutional u-net to automatic glaucoma detection, to the best of our knowledge, it is the first time to apply deformable convolution network to glaucoma detection. Besides, we modify the deformable U-net [6] and make it have a better performance on glaucoma Detection. (2) Due to the deformable u-net, our framework is more robust for glaucoma detection. The experiment result shows that our method has improved the OD segmentation result from 0.95 to 0.97 (in terms of the Dice metrics and RIM-ONE v.3), and the OC segmentation result is also improved from 0.82 to 0.84 compared to the best reported result, which shows the potential performance of deformable U-net in glaucoma detection. The rest of paper is organized as follows. Section II gives a brief introduction of our proposed method and architecture. Section IV introduces the OD & OC segmentation process in our method. Section V evaluates our proposed method. Finally, we conclude our work in section VI II. RELATED WORK For automatic feature extract methods, Maninis et al. [7] presented a deep retinal image understanding (DRIU) method which was based on CNN architecture. large scale screening of glaucoma. Optic nerve head can be evaluated in the form of digital fundus image (DFI), DFI gives us an intuitive display of the structure of the optic disc (OD) and the optic cup (OC) on the retina, glaucoma can be diagnosed by the assessment of OD and OC in a noninvasive manner. Compared to the two aforementioned methods, assessment of optic nerve head is both efficient and promising, digital fundus image is also recognized as the main modality to diagnose glaucoma in many automatic glaucoma detection methods. The optic disc (AKA. optic nerve head) is the location where ganglion cell axons leave the eye to form the optic nerve [3]. In digital fundus image, the OD appears as a bright oval area, and the OC is the brighter oval area in the center of the OD. The vertical cup-to-disc ratio (VCDR) is a key parameter for glaucoma detection which is calculated by a vertical cup to disc ratio. The illustration of OD and OC of glaucoma case and non-glaucoma case is shown in Figure 1. In order to get VCDR parameter, the area of OD and OC has to be accurately segmented. Afterwards, the VCDR is calculated based on segmented OD and OC for glaucoma screening. Therefore accurate segmentations of OD and OC are crucial to diagnosing glaucoma. The former refers to the traditional image process techniques, such as the level-set based approach, thresholding based approach, active shape modeling approach, and the later mainly refers to the popular deep learning techniques. With the great development of deep learning, methods based on deep learning have made a great progress in many computer visions tasks. Especially, after the occurrence of Fully Connected Network Fig ure 1. Left is glaucoma case, right is non-glaucoma case, the blue dotted line refers to optic cup, and the green dotted line denotes optic disc. The sample images are derived from DRISHTI-GS. They modified the CNN and combined a volume of fine-tocoarse feature maps into a regressed result for the optic disc segmentation. However, this method is designed for OD segmentation and it is not evaluated on OC segmentation. Zilly et al. [8] utilized entropy sampling and ensemble learning for OC and OD segmentation. They designed a multi-scale CNN for OD and OC segmentation, during the training process, they used B. Deformable U-net 1) U-net: In our paper, we implement a typical U-net as the baseline model, which is based on fully convolutional networks [12]. We modify the U-shape convolutional network (U-net) in Ref. [4] as the main body of our deep architecture. U-net is a kind of efficient fully convolutional neural network which was first proposed in 2015 ISBI cell tracking challenge for the cell image segmentation. Unlike the other segmentation networks, Unet can achieve great performance without large training data on many tasks, especially on medical image segmentation tasks. The U-net architecture consists of two path: the encoder path (left side) and decoder path (right side). Similar to the original U-net architecture, our architecture is shown in Figure 2. We add the deformable convolution operation at different layers, and inspired by the proposed architecture in Ref. [9], we also reduce the number of convolution kernel in all the convolution layers, and keep the fixed kernel number during some certain layers. The results in Ref. [9] show that properly reducing filters does not affect the segmentation performance. Meanwhile, and less filters will lead to less computational costs. 2) Deformable convolution: As previously mentioned, U-net is based on fully convolutional networks. Further, as standard convolution is limited to dealing with object shape transformations inherently due to its regular square receptive field and fixed geometric structures in their building modules [5]. The traditional convolution units usually sample at a fixed position. In general, there are two normal ways to adapt the geometric variations. One is to augment the dataset, another is feature invariance algorithms such as SIFT algorithm [13]. However, large data augmentation will lead to huge time costs during the model training process and still be unable to avoid the defect in dealing with some special unknown geometric transformation; Manually designed features are not feasible for complicated model variation. Deformable convolution can sample the input feature map in a local and dense way and adapt to locating different shape objects, which is exactly what we need. As Figure 5 shows, the receptive field of standard convolution is fixed, while the deformable convolution is self-adaptive. In this paper, we replace the standard convolution kernel with deformable convolution in the certain layers of the U-net Fig ure 4. Illustration of deformable convolution Deformable convolution can sample the input feature map in a local and dense way and adapt to locating different shape objects, which is exactly what we need. As Figure 5 shows, the receptive field of standard convolution is fixed, while the deformable convolution is self-adaptive. In this paper, we replace the standard convolution kernel with deformable convolution in the certain layers of the U-net. peat and needs much time to train the model. Sevastopolsky [9] applied a modified u-net to segment OD and OC. The modified unet has less convolution filters leading to less parameters and it is more efficient to train the model. Thus, it performs well on OD segmentation, however it is not ideal on OC segmentation. Fu et al. [10] proposed a method based on M-net (a modified u-net) for joint segmentation of OD and OC. This method combines the segmentation of OD with the segmentation of OC and utilizes the polar transformation to pre-process the input images, which deals well with the imbalanced data problem of pixelwise segmentation for fundus image. However, the center coordinate of OD must be acquired for polar transformation. Locating the center of OD is also another challenge for glaucoma detection, and it will consume more time costs. III. METHODOLOGY In this paper, we propose a deep learning architecture based on deformable U-net for OD and OC segmentation, the illustration of our method is shown in Figure 2, our architecture can be divided into two main parts: Data Preprocess, Deformable U-net. A. Data Preprocess In our method, we downsample our input image to a small fixed scale such as 256 X 256 px, then we apply the ContrastLimited Adaptive Histogram Equalization (CLAHE) [11] to preprocess the rescaled image. CLAHE is an enhancement operation that is widely applied in retinal image analysis due to its capability to improve the contrast and illumination of the fundus. Additionally, it is also known to characterize the retinal nerve fiber. Many existing glaucoma detection methods have both indicated the advantages of CLAHE. Medical image analysis is different from the natural image analysis. Despite of the difference between their morphological interpretability, the biggest difference is the sample size of dataset. Unlike the natural image datasets, medical image datasets are usually small-scale, it will bring several drawbacks, such as overfitting, imbalanced class. In order to reduce the negative effects, we apply data augmentation to the dataset, by means of cropping and zooming the image, flipping the image horizontally or vertically, the size of dataset has been enlarged several times. Besides, we apply an extra data preprocess for optic cup segmentation, due to the low contrast boundary Fig ure 5. Illustration of receptive field in standard convolution(c) and deformable convolution(d) denotes number, OD denotes whether dataset contains the optic disc segmentation ground-truth, OC denotes whether dataset contains optic cup segmentation ground-truth, DRIONS-DB contains 110 full eye fundus images with OD segmentation; RIM-ONE v.3 contains 159 images cropped by OD area, all the images contain the OD and OC segmentations; DRISHTIGS contains 50 full eye fundus images with OD and OC segmentation D. Experimental Results The comparison between our proposed method and the existing methods is shown in Table II and Table III. We have five methods to evaluate on the datasets for OD and huge. Besides, the full deformable U-net means replacing all convolution in original U-net as deformable convolution. Zhang et al. [6] proposed the full deformable U-net for Sickle Cell Disease detection. The deformable Unet just replaces a part of the convolution operations as deformable convolution, as shown in Fig. 2. From the result, we notice that deformable U-net gets the comparative performance with full deformable U-net. The proposed method has less parameters and spends less computational costs. From the Table III, we can notice the similar results with OD segmentation, while the difference is that optic cup segmentation is more challenging than OD segmentation. The visualization of OD segmentation on DRIONSDB is shown in Figure 6, and the OD and OC segmentation results for deformable U-net on RIM-ONE v.3 and DRISHTI-GS are shown in Figure 7. difference between OD and OC, in order to get a better segmentation performance, we first extract the region of interest (AKA.ROI). In fact, we can use the result of OD segmentation to acquire the ROI, then resize the image. After above operations, we will obtain the inputs for optic cup segmentation. Fig ure 3. Deformable convolution kernel The main idea of deformable convolution is adding 2 offsets on the regular convolution kernels, just as shown in Figure 3 (modified from Ref. [5]). The left is the regular kernel and the right is the result of adding offsets. The key for deformable convolution network is how to acquire the offsets, then the offsets will be utilized to change the sampling locations during the training process. The general convolution process can be denoted as Eq. 1 (from Ref. [5]). (1) where pi denotes the receptive field in the input feature map, and pi png, w denotes the weights of kernel. For the deformable convolution, an extra offset _pi will be added to get the Eq. 2 (from Ref. [5]).and offset _pi is generated by another general convolution process. In order to change the input feature map smoothly and slowly, the offset should be small. We denote the results IV. OPTIC DISC & CUP SEGMENTATION Optic disc and optic cup segmentations are the goals of this paper. In order to produce more accurate segmentation results, we choose to train two deformable U-net orderly for OD and OC segmentations. For the OD segmentation, we first preprocess the raw images, and train the deformable Unet with our input images, then output the OD segmentation results, meanwhile, ellipse fitting technique is also applied to process the raw segmentation results. After OD segmentation, the ROI of input image can be easily and precisely extracted as the input of optic cup segmentation. Due to relatively vague boundary of optic cup, precise ROI extraction is essential for eyes. VI. CONCLUSION In this paper, a novel segmentation method for optic disc and optic cup was proposed, our architecture was based on deformable U-net. The deformable convolution makes our architecture more robust for segmentation, and it can achieve satisfactory results in spite of the small dataset. We evaluated our proposed method on different publicly datasets and achieved promising results compared with conventional methods. optic cup segmentation. The raw optic cup segmentation results also need to be processed by ellipse fitting. So far, we get the segmentations of both OD and OC, then we can use the results to calculate the VCDR of digital fundus image for further glaucoma detection V. EXPERIMENT In this section, we evaluate our method on several publicly available digital fundus image datasets DRIONSDB [14], RIMONE v.3 [15], and DRISHTI-GS [16]. The comparison with other methods is also evaluated. Besides, we design several internal comparison experiments for deformable convolution Unet. The performances of different number of deformable convolution in u-net is evaluated. (1) Original U-net, (2) deformable U-net (with different deformable layers), (3) segmentation method based on other optic cup/disc segmentation algorithms. A. Evaluation Criteria For evaluation of the segmentation results and comparison with other segmentation methods, we take the real-valued dice score d (A, B) [9] as our evaluation criteria B. Datasets Our experiment is based on three publicly available datasets DRIONS-DB [14], RIM-ONE v.3 [15], and DRISHTI-GS [16], which contain ground-truth segmentation for OD, and some for optic cup as well. OC segmentation, and the disappeared number means the result is not reported. From the Table II, we can easily observe the improvement of our method in Segmentation. REFERENCES [1] S. 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