Abstract
Lung cancer has emerged as a severe disease that threatens human life and health. The precise segmentation of lung regions is a crucial prerequisite for localizing tumors, which can provide accurate information for lung image analysis. In this work, we first propose a lung image segmentation model using the NASNet-Large as an encoder and then followed by a decoder architecture, which is one of the most commonly used architectures in deep learning for image segmentation. The proposed NASNet-Large-decoder architecture can extract high-level information and expand the feature map to recover the segmentation map. To further improve the segmentation results, we propose a post-processing layer to remove the irrelevant portion of the segmentation map. Experimental results show that an accurate segmentation model with 0.92 dice scores outperforms state-of-the-art performance.
Abstract (translated)
肺癌已成为威胁人类生命和健康的严重疾病。精确分割肺部区域是定位肿瘤的至关重要先决条件,可以为肺部图像分析提供准确的信息。在本研究中,我们首先提出了使用NASNet-Large作为编码器的图像分割模型,然后提出了解码器架构,它是深度学习中常用于图像分割的常见架构之一。 proposed NASNet-Large-decoder architecture can extract high-level information and expand the feature map to recover the segmentation map. 为进一步改善分割结果,我们提出了一个后处理层,以删除分割图中无关的部分。实验结果显示,一个具有0.92dice评分的准确分割模型比当前最先进的性能表现更好。
URL
https://arxiv.org/abs/2303.10315
