Perspectives: Comparison of Deep Learning Segmentation Models on Biophysical and Biomedical Data (2408.07786v1)

Abstract: Deep learning based approaches are now widely used across biophysics to help automate a variety of tasks including image segmentation, feature selection, and deconvolution. However, the presence of multiple competing deep learning architectures, each with its own unique advantages and disadvantages, makes it challenging to select an architecture best suited for a specific application. As such, we present a comprehensive comparison of common models. Here, we focus on the task of segmentation assuming the typically small training dataset sizes available from biophysics experiments and compare the following four commonly used architectures: convolutional neural networks, U-Nets, vision transformers, and vision state space models. In doing so, we establish criteria for determining optimal conditions under which each model excels, thereby offering practical guidelines for researchers and practitioners in the field.

Comparison of Deep Learning Segmentation Models on Biophysical and Biomedical Data

The paper "Perspectives: Comparison of Deep Learning Segmentation Models on Biophysical and Biomedical Data" by J. Shepard Bryan IV, Meysam Tavakoli, and Steve Press provides a meticulous comparative analysis of several prevalent deep learning segmentation models. The focus of this comparison is on applications within biophysics and biomedical data, specifically under the common constraint of small dataset sizes. The paper evaluates the performance of four commonly utilized architectures: Convolutional Neural Networks (CNNs), U-Nets, Vision Transformers (ViTs), and Vision State Space Models (VSSMs).

Introduction

Deep learning techniques have significantly impacted biophysics, enhancing various tasks such as time series analysis, image reconstruction, protein structure prediction, and most notably, segmentation. The challenge in segmentation lies in selecting an appropriate deep learning architecture, especially in scenarios where only limited training data are available. The paper attempts to bridge this gap by systematically evaluating the segmentation capabilities of the four aforementioned architectures using small datasets from three different biophysical and biomedical experiments.

Datasets

Three datasets were employed in this paper, each representing distinct experimental conditions and presenting unique segmentation challenges:

1. Phase-contrast Bdellovibrio Dataset: This dataset includes over 1000 images of Bdellovibrio bacteriovorus obtained through phase-contrast imaging. The primary challenge here is the sparsity of the target bacteria within the images, requiring models to utilize features like Airy discs for accurate segmentation.
2. Fluorescence Microscopy Neuron Dataset: Comprising 283 high-resolution images of fluorescently labelled mouse neurons, this dataset presents significant challenges due to the variability in neuron appearance and the presence of numerous artifacts and overlapping structures.
3. Retina Fundus Images: This dataset includes 800 high-resolution fundus images annotated for pixelwise vessel segmentation. The principal challenge in this dataset stems from the complex branching patterns of retinal vessels, necessitating models capable of capturing intricate structures.

Methods

The paper evaluates the performance of each model using several hard metrics, including accuracy, specificity, sensitivity, and AUC score, alongside soft metrics such as model size and training time. The models were trained using cross-validation on each dataset, and results were averaged over the folds to ensure robustness.

Model Architectures

1. Convolutional Neural Networks (CNNs): Efficient in capturing local spatial patterns with fewer parameters but limited in learning long-range dependencies.
2. U-Nets: Known for their applicability in medical imaging, U-Nets leverage skip connections and downsampling to integrate local and long-range information. They are memory-intensive but excel in tasks involving multiple length scales.
3. Vision Transformers (ViTs): Utilizes self-attention mechanisms to capture long-range dependencies but are computationally intensive, often introducing block-like artifacts due to patchification.
4. Vision State Space Models (VSSMs): Similar to ViTs but optimized to reduce computational complexity, making them potentially faster on GPUs. However, they may still suffer from gridding artifacts.

Results

The results indicate that:

• The CNN and U-Net architectures performed robustly across different datasets, with the U-Net demonstrating superior qualitative results and fewer false positives.
• The ViT and VSSM models were effective in more complex datasets like the fluorescence microscopy neuron images, where their ability to handle long-range information mitigated annotation artifacts.
• No single model dominated across all scenarios, highlighting the importance of model selection based on specific dataset attributes.

Quantitative Analysis

1. Phase-contrast Bdellovibrio Dataset: The CNN and U-Net models excelled, with U-Net achieving the highest AUC score of 0.998. ViT lagged significantly in this dataset, failing to detect targets effectively.
2. Fluorescence Microscopy Neuron Dataset: ViT and VSSM showed better qualitative performance and faster convergence, with the CNN and U-Net requiring substantially more epochs to achieve comparable results.
3. Retina Fundus Images: U-Net outperformed other models, achieving the highest metrics across the board, validating its design for medical image segmentation tasks.

Discussion

The paper underscores the versatility of the CNN and U-Net architectures for general segmentation tasks, recommending their initial use in biophysical datasets. ViTs and VSSMs, while showing promise in more complex scenarios, require more computational resources and careful optimization of parameters. Future comparisons could explore other deep learning paradigms, incorporate pre-training techniques, or evaluate Bayesian deep learning for enhanced uncertainty quantification. Extending this comparative framework to other domains within biophysics, such as time series analysis and protein dynamics, may also provide deeper insights into model performance and applicability.

The provided codebase on GitHub serves as an excellent resource for researchers to replicate and extend these comparisons, facilitating the practical implementation of optimal deep learning models in various biophysical and biomedical segmentation tasks.

Conclusion

The paper provides a comprehensive guide for selecting deep learning architectures tailored to specific biophysics applications, especially under constraints of small training datasets. By systematically evaluating CNNs, U-Nets, ViTs, and VSSMs, the paper offers valuable insights into the strengths and limitations of each model, aiding researchers in making informed decisions for their segmentation tasks.

Code Availability

All code used in this work, as well as a script to quickly download data, can be found in a repository on GitHub.

Acknowledgements

SP acknowledge support from the NIH (grant no. R01GM134426 and R01GM130745) and the NIH MIRA award.

Competing Interests

SP and JSB acknowledge a competing interest with their affiliation with Saguaro Solutions.