Iyer Balaji, Deoghare Smruti, Ranjan Krish, Aronow Bruce J, Prasath V B Surya
Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Department of Electrical Engineering and Computer Science, University of Cincinnati, OH 45221, USA.
Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH 45267, USA.
Comput Biol Med. 2025 Jun;192(Pt A):110150. doi: 10.1016/j.compbiomed.2025.110150. Epub 2025 Apr 26.
Indirect Immunofluorescence (IIF) stained Human Epithelial (HEp-2) cells are considered the gold standard for detecting autoimmune diseases. Accurate cell segmentation, though often viewed as an intermediary step to downstream tasks like classification, significantly enhances overall performance when executed with precision. In this study, we conduct a systematic literature review of HEp-2 cell segmentation techniques, identifying 28 key papers utilizing traditional image processing, machine learning classifiers, deep convolutional neural networks (CNNs), and generative adversarial network (GAN) frameworks. Building on these insights, we benchmark 17 CNN models without pretraining and 8 CNN models pretrained on ImageNet using both Frozen Encoder and Tunable Encoder strategies on the I3A dataset. Cross-validation (CV) and Benjamini-Hochberg (BH) significance correction were employed to ensure statistical rigor in model comparisons. Domain-Specific Pretraining (DSPT) experiments demonstrated performance improvements, particularly for underrepresented classes, while Data Augmentation strategies (DA-1 and DA-2) revealed distinct impacts across model categories. GAN-based segmentation experiments using the top-performing CNN architectures as generators within a Pix2Pix framework revealed performance degradation due to data limitations and adversarial training instabilities. Nonetheless, GANs displayed class-specific improvements in visual alignment of segmentation masks. Results were evaluated comprehensively across eight performance metrics, including Dice, IOU, Accuracy, Precision, Sensitivity, Specificity, AU-ROC and AU-PR. This work offers a robust benchmarking of state-of-the-art CNN, GAN, and Transformer-based models for HEp-2 cell segmentation, providing valuable insights for future research directions, including ensemble approaches, dynamic patch sampling, and diffusion models.
间接免疫荧光（IIF）染色的人上皮（HEp-2）细胞被认为是检测自身免疫性疾病的金标准。准确的细胞分割虽然通常被视为分类等下游任务的中间步骤，但精确执行时能显著提高整体性能。在本研究中，我们对HEp-2细胞分割技术进行了系统的文献综述，识别出28篇利用传统图像处理、机器学习分类器、深度卷积神经网络（CNN）和生成对抗网络（GAN）框架的关键论文。基于这些见解，我们在I3A数据集上使用冻结编码器和可调编码器策略，对17个未预训练的CNN模型和8个在ImageNet上预训练的CNN模型进行了基准测试。采用交叉验证（CV）和Benjamini-Hochberg（BH）显著性校正来确保模型比较中的统计严谨性。特定领域预训练（DSPT）实验证明了性能提升，特别是对于代表性不足的类别，而数据增强策略（DA-1和DA-2）在不同模型类别中显示出不同的影响。在Pix2Pix框架内使用表现最佳的CNN架构作为生成器进行的基于GAN的分割实验，由于数据限制和对抗训练不稳定性，显示出性能下降。尽管如此，GAN在分割掩码的视觉对齐方面显示出特定类别的改进。结果通过包括骰子系数、交并比、准确率、精确率、灵敏度、特异性、曲线下面积（AU-ROC）和平均精度（AU-PR）在内的八个性能指标进行了全面评估。这项工作为用于HEp-2细胞分割的基于CNN、GAN和Transformer的先进模型提供了强大的基准测试，为未来的研究方向提供了有价值的见解，包括集成方法、动态补丁采样和扩散模型。
