Benchmarking HEp-2 cell segmentation methods in indirect immunofluorescence images - standard models to deep...

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.