Objectives: The optic disc is part of the retinal fundus image structure, which influences the extraction of glaucoma features. This study proposes a method that automatically segments the optic disc area in retinal fundus images using deep learning based on a convolutional neural network (CNN). Methods: This study used private and public datasets containing retinal fundus images. The private dataset consisted of 350 images, while the public dataset was the Retinal Fundus Glaucoma Challenge (REFUGE). The proposed method was based on a CNN with a single-shot multibox detector (MobileNetV2) to form images of the region-of-interest (ROI) using the original image resized into 640 × 640 input data. A pre-processing sequence was then implemented, including augmentation, resizing, and normalization. Furthermore, a U-Net model was applied for optic disc segmentation with 128 × 128 input data. Results: The proposed method was appropriately applied to the datasets used, as shown by the values of the F1-score, dice score, and intersection over union of 0.9880, 0.9852, and 0.9763 for the private dataset, respectively, and 0.9854, 0.9838 and 0.9712 for the REFUGE dataset. Conclusions The optic disc area produced by the proposed method was similar to that identified by an ophthalmologist. Therefore, this method can be considered for implementing automatic segmentation of the optic disc area.

OBJECTIVES: Glaucoma is an incurable eye disease and the second leading cause of blindness in the world. Until 2020, the number of patients of this disease is estimated to increase. This paper proposes a glaucoma detection method using statistical features and the k-nearest neighbor algorithm as the classifier. METHODS: We propose three statistical features, namely, the mean, smoothness and 3rd moment, which are extracted from images of the optic nerve head. These three features are obtained through feature extraction followed by feature selection using the correlation feature selection method. To classify those features, we apply the k-nearest neighbor algorithm as a classifier to perform glaucoma detection on fundus images. RESULTS: To evaluate the performance of the proposed method, 84 fundus images were used as experimental data consisting of 41 glaucoma image and 43 normal images. The performance of our proposed method was measured in terms of accuracy, and the overall result achieved in this work was 95.24%, respectively. CONCLUSIONS: This research showed that the proposed method using three statistics features achieves good performance for glaucoma detection.
Blindness ; Classification ; Eye Diseases ; Glaucoma ; Humans ; Methods ; Optic Disk ; Optic Nerve Diseases ; Retinal Degeneration

Ovarian Sertoli-Leydig cell tumors are rare sex cord-stromal tumors, accounting for less than 1% of ovarian tumors. Majority of these tumors are benign and unilateral, only 3-5% are bilateral. These patients present with clinical features of virilization due to excessive secretion of testosterone from the tumor, however 50% may have no endocrine symptoms. We report a case of poorly differentiated Sertoli-Leydig cell tumour in a woman diagnosed during routine investigation of infertility. She had two spontaneous successful pregnancies after tumor excision laparoscopically.
Accounting ; Female ; Humans ; Infertility ; Laparoscopy ; Ovary ; Pregnancy ; Sertoli-Leydig Cell Tumor ; Sex Cord-Gonadal Stromal Tumors ; Testosterone ; Virilism