Optimized-Unet: Novel Algorithm for Parapapillary Atrophy Segmentation

Optimized-Unet: Novel Algorithm for Parapapillary Atrophy Segmentation

Blog Article

In recent years, an Blu-ray Player increasing number of people have myopia in China, especially the younger generation.Common myopia may develop into high myopia.High myopia causes visual impairment and blindness.

Parapapillary atrophy (PPA) is a typical retinal pathology related to high myopia, which is also a basic clue for diagnosing high myopia.Therefore, accurate segmentation of the PPA is essential for high myopia diagnosis and treatment.In this study, we propose an optimized Unet (OT-Unet) to solve this important task.

OT-Unet uses one of the pre-trained models: Visual Geometry Group (VGG), ResNet, and Res2Net, as a backbone and is combined with edge attention, parallel partial decoder, and reverse attention modules to improve the segmentation accuracy.In general, using the pre-trained models can improve the accuracy with fewer samples.The edge attention module extracts contour information, the parallel partial decoder module combines the multi-scale features, and the reverse attention module integrates high- and low-level features.

We also propose an augmented loss function to increase the weight of complex pixels to enable the network to segment more complex lesion areas.Based on a dataset containing 360 images (Including 26 pictures provided by PALM), the proposed OT-Unet achieves a high AUC (Area Under Curve) of 0.9235, indicating BABY BODY LOTION a significant improvement over the original Unet (0.

7917).