Context-aware Attention U-Net for the segmentation of pores in Lamina Cribrosa using partial points annotation
| Autor: | Ding, Nan, Urien, Hélène, Rossant, Florence, Sublime, Jérémie, Paques, Michel |
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| Přispěvatelé: | Institut Supérieur d'Electronique de Paris (ISEP), INSERM-Centre d'Investigation Clinique 1423, Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts, F-75012 Paris, France, Ding, Nan |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | 21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA'22) 21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA'22), Dec 2022, Bahamas, Bahamas |
| DOI: | 10.1109/icmla55696.2022.00088 |
| Popis: | International audience; Glaucoma is the second leading cause of blindness in the world. Although its physiopathology remains unclear, the lamina cribrosa, a 3D mesh-like structure consisting of pores, that allow the axons passing through to join the brain, has been identified as the primary site of damage. In this work we present an extended version of U-Net for pore segmentation in 2D enface OCT images with partial point annotations, i.e. having only a small portion of pore locations in each image labeled. Our method combines the attention gate and the context information to address the difficulties caused by small object segmentation in low signal to noise ratio images. Experimental results show that 71.8% of the annotated pores are successfully segmented. |
| Databáze: | OpenAIRE |
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