School of Information Technology, Jiangsu Open University, Nanjing, 210000, Jiangsu, China.
Sci Rep. 2024 Oct 28;14(1):15687. doi: 10.1038/s41598-024-66585-x.
Histopathological examination holds a crucial role in cancer grading and serves as a significant reference for devising individualized patient treatment plans in clinical practice. Nevertheless, the distinctive features of numerous histopathological image targets frequently contribute to suboptimal segmentation performance. In this paper, we propose a UNet-based multi-scale context fusion algorithm for medical image segmentation, which extracts rich contextual information by extracting semantic information at different encoding stages and assigns different weights to the semantic information at different scales through TBSFF module to improve the learning ability of the network for features. Through multi-scale context fusion and feature selection networks, richer semantic features and detailed information are extracted. The target can be more accurately segmented without significantly increasing the extra overhead. The results demonstrate that our algorithm achieves superior Dice and IoU scores with a relatively small parameter count. Specifically, on the GlaS dataset, the Dice score is 90.56, and IoU is 83.47. For the MoNuSeg dataset, the Dice score is 79.07, and IoU is 65.98.
组织病理学检查在癌症分级中起着至关重要的作用，并为临床实践中制定个体化患者治疗计划提供了重要参考。然而，许多组织病理学图像目标的独特特征常常导致分割性能不佳。在本文中，我们提出了一种基于 UNet 的多尺度上下文融合算法，用于医学图像分割，该算法通过在不同的编码阶段提取语义信息，提取丰富的上下文信息，并通过 TBSFF 模块为不同尺度的语义信息分配不同的权重，以提高网络对特征的学习能力。通过多尺度上下文融合和特征选择网络，可以提取更丰富的语义特征和详细信息。在不显著增加额外开销的情况下，可以更准确地分割目标。实验结果表明，我们的算法在相对较小的参数计数下实现了较高的 Dice 和 IoU 分数。具体来说，在 GlaS 数据集上，Dice 分数为 90.56，IoU 分数为 83.47。在 MoNuSeg 数据集上，Dice 分数为 79.07，IoU 分数为 65.98。
