DilNet: A Dilation Privileged Information for Accurate Langerhans Cell Segmentation

Ankai Dong; Pan Su

doi:10.62051/ijcsit.v8n3.05

Authors

Ankai Dong
Pan Su

DOI:

https://doi.org/10.62051/ijcsit.v8n3.05

Keywords:

Medical Segmentation, Supervised learning, Privileged Information, Langerhans cell Dilation

Abstract

Accurate segmentation of Langerhans cell morphological characteristics is of great significance for the diagnosis of corneal diseases and the assessment of activation degree. However, the precise segmentation of corneal Langerhans cells remains unexplored. Manual dense annotation of Langerhans cells is a time-consuming and labor-intensive task. In order to achieve automated and accurate segmentation of Langerhans cells, this paper proposes a prior denoising framework, through boundary-aware refinement module which consists of a random mask dilation strategy to force the model to locate the target by understanding the target features rather than relying on the environment and a shrinking values generation strategy to gradually capture the target contour and improve the robustness of the refinement model. Experiments on one dataset and twelve types of neural networks have proven that our method significantly improves the accuracy of Langerhans cell segmentation and has strong versatility.

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