Xie Qiuyin, Huang Jianuo, Sun Jingyang, Huang Chenxi, Xu Caixu
Department of Obstetrics and Gynecology, The Third Hospital of Xiamen, Xiamen, Fujian, China.
School of Informatics, Xiamen University, Xiamen, Fujian, China.
Front Oncol. 2025 Jul 21;15:1555585. doi: 10.3389/fonc.2025.1555585. eCollection 2025.
Ovarian cancer remains one of the most lethal gynecological malignancies, posing significant challenges for early detection due to its asymptomatic nature in early stages. Accurate segmentation of ovarian tumors from ultrasound images is critical for improving diagnostic accuracy and patient outcomes. In this study, we introduce SMoFFI-SegFormer, an advanced deep learning model specifically designed to enhance multi-scale feature representation and address the complexities of ovarian tumor segmentation. Building upon the SegFormer architecture, SMoFFI-SegFormer incorporates a novel Self-modulate Fusion with Feature Inhibition (SMoFFI) module that promotes cross-scale information exchange and effectively handles spatial heterogeneity within tumors. Through extensive experimentation on two public datasets-OTU_2D and OTU_CEUS-our model demonstrates superior performance with high overall accuracy, mean Intersection over Union (mIoU), and class accuracy. Specifically, SMoFFI-SegFormer achieves state-of-the-art results, significantly outperforming existing models in both segmentation precision and efficiency. This work paves the way for more reliable and automated tools in the diagnosis and management of ovarian cancer.
卵巢癌仍然是最致命的妇科恶性肿瘤之一，由于其早期无症状的特性，给早期检测带来了重大挑战。从超声图像中准确分割卵巢肿瘤对于提高诊断准确性和患者预后至关重要。在本研究中，我们引入了SMoFFI-SegFormer，这是一种先进的深度学习模型，专门设计用于增强多尺度特征表示并解决卵巢肿瘤分割的复杂性。基于SegFormer架构，SMoFFI-SegFormer整合了一个新颖的具有特征抑制的自调制融合（SMoFFI）模块，该模块促进跨尺度信息交换并有效处理肿瘤内的空间异质性。通过在两个公共数据集OTU_2D和OTU_CEUS上进行广泛实验，我们的模型在总体准确率、平均交并比（mIoU）和类别准确率方面表现出卓越性能。具体而言，SMoFFI-SegFormer取得了领先成果，在分割精度和效率方面均显著优于现有模型。这项工作为卵巢癌诊断和管理中更可靠、自动化的工具铺平了道路。
