Automatic lesion segmentation and Pathological Myopia classification in fundus images

• URL: http://arxiv.org/abs/2002.06382v1
• Date: Sat, 15 Feb 2020 13:38:30 GMT
• Title: Automatic lesion segmentation and Pathological Myopia classification in fundus images
• Authors: Cefas Rodrigues Freire, Julio Cesar da Costa Moura, Daniele Montenegro da Silva Barros and Ricardo Alexsandro de Medeiros Valentim
• Abstract summary: We present algorithms to diagnosis Pathological Myopia (PM) and detection of retinal structures and lesions such asOptic Disc (OD), Fovea, Atrophy and Detachment. All these tasks were performed in fundus imaging from PM patients and they are requirements to participate in the Pathologic Myopia Challenge (PALM)
• Score: 1.4174475093445236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we present algorithms to diagnosis Pathological Myopia (PM) and detection of retinal structures and lesions such asOptic Disc (OD), Fovea, Atrophy and Detachment. All these tasks were performed in fundus imaging from PM patients and they are requirements to participate in the Pathologic Myopia Challenge (PALM). The challenge was organized as a half day Challenge, a Satellite Event of The IEEE International Symposium on Biomedical Imaging in Venice Italy.Our method applies different Deep Learning techniques for each task. Transfer learning is applied in all tasks using Xception as the baseline model. Also, some key ideas of YOLO architecture are used in the Optic Disc segmentation algorithm pipeline. We have evaluated our model's performance according the challenge rules in terms of AUC-ROC, F1-Score, Mean Dice Score and Mean Euclidean Distance. For initial activities our method has shown satisfactory results.

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