Model-Agnostic Post-Processing Based on Recursive Feedback for Medical Image Segmentation
In medical image segmentation, post-processing can effectively improve the performance of a segmentation model. Existing post-processing methods generally require additional training of a post-processing model using training data or designing a post-processing procedure based on a high level of doma...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/52df5d1da8d14af6aeb0d7c13f3d273c |
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| Sumario: | In medical image segmentation, post-processing can effectively improve the performance of a segmentation model. Existing post-processing methods generally require additional training of a post-processing model using training data or designing a post-processing procedure based on a high level of domain knowledge. Their application is limited in many real-world situations due to the lack of prerequisites. In this study, we present a post-processing method that can be applied to any existing segmentation model without requiring the use of training data or domain knowledge. Given a segmentation model of any type, the proposed method improves its prediction based on the recursive feedback mechanism. For a query image, we first obtain its prediction mask by using the segmentation model. Based on the prediction mask, we modify the original image by selectively blurring the area in which the target object is expected to be absent. Subsequently, the modified image is fed again into the model to acquire a refined prediction mask. We repeat this process to obtain multiple prediction masks, which are then combined to yield the final prediction. We verified the effectiveness of the proposed method through experiments using real-world medical datasets. |
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