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Benchmarking HEp-2 cell segmentation methods in indirect immunofluorescence images - standard models to deep learning
Balaji Iyer1, Smruti Deoghare2, Krish Ranjan3
1Division 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.
Insights
This study benchmarks deep learning models for segmenting Human Epithelial (HEp-2) cells in autoimmune disease diagnostics. Convolutional Neural Networks (CNNs) show promise, with domain-specific pretraining improving performance on challenging cell types.
Area of Science:
- Medical Imaging Analysis
- Computational Biology
- Artificial Intelligence in Healthcare
Background:
- Indirect Immunofluorescence (IIF) staining of Human Epithelial (HEp-2) cells is crucial for autoimmune disease diagnosis.
- Precise HEp-2 cell segmentation is vital for accurate downstream classification tasks.
- Existing segmentation methods vary, necessitating a comprehensive performance evaluation.
Purpose of the Study:
- To systematically review and benchmark various HEp-2 cell segmentation techniques.
- To evaluate the performance of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) for HEp-2 cell segmentation.
- To identify optimal strategies including pretraining and data augmentation for improved segmentation accuracy.
Main Methods:
- Conducted a systematic literature review identifying 28 relevant papers.
- Benchmarked 17 non-pretrained and 8 pretrained CNN models on the I3A dataset using Frozen and Tunable Encoder strategies.
- Performed Domain-Specific Pretraining (DSPT) and Data Augmentation (DA-1, DA-2) experiments; evaluated GANs (Pix2Pix) with top CNN generators.
Main Results:
- CNN models, especially with Domain-Specific Pretraining (DSPT), showed significant performance improvements, particularly for underrepresented classes.
- Data Augmentation strategies had varied impacts across different model architectures.
- GAN-based segmentation showed potential for visual alignment but suffered from data limitations and training instability, leading to overall performance degradation compared to CNNs.
Conclusions:
- This work provides a robust benchmark of CNN, GAN, and Transformer models for HEp-2 cell segmentation.
- Domain-Specific Pretraining (DSPT) is a key strategy for enhancing segmentation performance, especially for rare cell types.
- Future research should explore ensemble methods, dynamic patch sampling, and diffusion models for further advancements in HEp-2 cell segmentation.
Abstract:
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.
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