Wang Xinzheng, Ou Cuisi, Hu Zhigang, Ge Aoru, Wang Yipei, Cao Kaiwen
School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang City, Henan Province, People's Republic of China.
J Multidiscip Healthc. 2024 Dec 2;17:5675-5693. doi: 10.2147/JMDH.S492655. eCollection 2024.
The diagnosis and treatment of B-Lineage Acute Lymphoblastic Leukemia (B-ALL) typically rely on cytomorphologic analysis of bone marrow smears. However, traditional morphological analysis methods require manual operation, leading to challenges such as high subjectivity and low efficiency. Accurate segmentation of individual cell nuclei is crucial for obtaining detailed morphological characterization data, thereby improving the objectivity and consistency of diagnoses.
To enhance the accuracy of nucleus segmentation of lymphoblastoid cells in B-ALL bone marrow smear images, the Multi-scale Feature Fusion-SegNeXt (MSFF-SegNeXt) model is hereby proposed, building upon the SegNeXt framework. This model introduces a novel multi-scale feature fusion technique that effectively integrates edge feature maps with feature representations across different scales. Integrating the Edge-Guided Attention (EGA) module in the decoder further enhances the segmentation process by focusing on intricate edge details. Additionally, Hamburger structures are strategically incorporated at various stages of the network to enhance feature expression.
These combined innovations enable MSFF-SegNeXt to achieve superior segmentation performance on the SN-AM dataset, as evidenced by an accuracy of 0.9659 and a Dice coefficient of 0.9422.
The results show that MSFF-SegNeXt outperforms existing models in managing the complexities of cell nucleus segmentation, particularly in capturing detailed edge structures. This advancement offers a robust and reliable solution for subsequent morphological analysis of B-ALL single cells.
B系急性淋巴细胞白血病（B-ALL）的诊断和治疗通常依赖于骨髓涂片的细胞形态学分析。然而，传统的形态学分析方法需要人工操作，导致主观性强、效率低等问题。准确分割单个细胞核对于获取详细的形态学特征数据至关重要，从而提高诊断的客观性和一致性。
为提高B-ALL骨髓涂片图像中淋巴母细胞的细胞核分割准确性，本文在SegNeXt框架的基础上，提出了多尺度特征融合-SegNeXt（MSFF-SegNeXt）模型。该模型引入了一种新颖的多尺度特征融合技术，有效地将边缘特征图与不同尺度的特征表示进行整合。在解码器中集成边缘引导注意力（EGA）模块，通过聚焦复杂的边缘细节进一步增强分割过程。此外，在网络的各个阶段策略性地融入汉堡结构以增强特征表达。
这些创新相结合，使MSFF-SegNeXt在SN-AM数据集上实现了卓越的分割性能，准确率达到0.9659，Dice系数达到0.9422。
结果表明，MSFF-SegNeXt在处理细胞核分割的复杂性方面优于现有模型，特别是在捕捉详细边缘结构方面。这一进展为后续B-ALL单细胞的形态学分析提供了一个强大而可靠的解决方案。
