Feng Rui, Chen Wei, Qi Jie
School of Communication and Information Engineering, Xi'an University of Science and Technology, Shaanxi 710054, China.
Xi'an Key Laboratory of Network Convergence Communication, Shaanxi, China.
Biomed Opt Express. 2024 Aug 6;15(9):5067-5080. doi: 10.1364/BOE.525294. eCollection 2024 Sep 1.
Leukocytes are an essential component of the human defense system, accurate segmentation of leukocyte images is a crucial step towards automating detection. Most existing methods for leukocyte images segmentation relied on fully supervised semantic segmentation (FSSS) with extensive pixel-level annotations, which are time-consuming and labor-intensive. To address this issue, this paper proposes a weakly supervised semantic segmentation (WSSS) approach for leukocyte images utilizing improved class activation maps (CAMs). Firstly, to alleviate ambiguous boundary problem between leukocytes and background, preprocessing technique is employed to enhance the image quality. Secondly, attention mechanism is added to refine the CAMs generated by improving the matching of local and global features. Random walks, dense conditional random fields and hole filling were leveraged to obtain final pseudo-segmentation labels. Finally, a fully supervised segmentation network is trained with pseudo-segmentation labels. The method is evaluated on BCCD and TMAMD datasets. Experimental results demonstrate that by employing the pseudo segmentation annotations generated through this method can be utilized to train UNet as close as possible to FSSS. This method effectively reduces manual annotation cost while achieving WSSS of leukocyte images.
白细胞是人体防御系统的重要组成部分，白细胞图像的准确分割是实现自动检测的关键一步。大多数现有的白细胞图像分割方法依赖于具有大量像素级注释的全监督语义分割（FSSS），这既耗时又费力。为了解决这个问题，本文提出了一种利用改进的类激活映射（CAM）对白细胞图像进行弱监督语义分割（WSSS）的方法。首先，为了缓解白细胞与背景之间的边界模糊问题，采用预处理技术来提高图像质量。其次，添加注意力机制以通过改善局部和全局特征的匹配来细化生成的CAM。利用随机游走、密集条件随机场和空洞填充来获得最终的伪分割标签。最后，使用伪分割标签训练全监督分割网络。该方法在BCCD和TMAMD数据集上进行了评估。实验结果表明，通过采用该方法生成的伪分割注释可用于训练尽可能接近FSSS的UNet。该方法在实现白细胞图像的WSSS的同时，有效地降低了人工注释成本。
