Leveraging Self-Supervised Vision Transformers for Segmentation-based Transfer Function Design

Abstract: In volume rendering, transfer functions are used to classify structures of interest, and to assign optical properties such as color and opacity. They are commonly defined as 1D or 2D functions that map simple features to these optical properties. As the process of designing a transfer function is typically tedious and unintuitive, several approaches have been proposed for their interactive specification. In this paper, we present a novel method to define transfer functions for volume rendering by leveraging the feature extraction capabilities of self-supervised pre-trained vision transformers. To design a transfer function, users simply select the structures of interest in a slice viewer, and our method automatically selects similar structures based on the high-level features extracted by the neural network. Contrary to previous learning-based transfer function approaches, our method does not require training of models and allows for quick inference, enabling an interactive exploration of the volume data. Our approach reduces the amount of necessary annotations by interactively informing the user about the current classification, so they can focus on annotating the structures of interest that still require annotation. In practice, this allows users to design transfer functions within seconds, instead of minutes. We compare our method to existing learning-based approaches in terms of annotation and compute time, as well as with respect to segmentation accuracy. Our accompanying video showcases the interactivity and effectiveness of our method.

• The paper introduces an innovative interactive approach that leverages pre-trained Vision Transformers to extract semantic features for transfer function design in volume rendering.
• It achieves high segmentation accuracy with a mean IoU of 0.981 on CT-ORG datasets, significantly reducing annotation and training time compared to conventional methods.
• The method enhances user experience through real-time feedback and demonstrates versatility across various medical imaging modalities, including CT and MRI.

Analysis of "Leveraging Self-Supervised Vision Transformers for Segmentation-based Transfer Function Design"

The paper under discussion introduces an innovative method for transfer function design in volume rendering by utilizing the feature extraction capabilities of self-supervised pre-trained Vision Transformers (ViTs). The primary goal is to address the tedious and often unintuitive process of transfer function creation by offering an interactive, annotation-driven approach that capitalizes on learned high-level features from pre-existing ViTs.

Technical Overview

Volume rendering requires effective mapping of data features to optical properties like color and opacity. Traditional methods rely on 1D or 2D transfer functions, which are often limited by their locality and inability to capture semantically coherent regions comprehensively. This paper leverages a pre-trained DINO ViT to extract feature representations from volumetric data. The 2D network is adapted for 3D data by processing slices along principal axes, followed by a merging process to form a 3D feature volume. The resulting features, rich in semantic information, enable immediate similarity-based voxels matching, allowing users to interactively annotate and refine transfer functions without the need for time-consuming model training.

Significant Results and Claims

1. Efficiency: The method reduces the need for extensive annotations and enables the design of transfer functions within seconds to minutes. This contrasts sharply with other learning-based approaches that often require extensive datasets and prolonged model training, as demonstrated in their comparisons, which show their method's superior performance in both time and annotation efficiency.
2. Quality and Accuracy: Through quantitative evaluations on CT-ORG datasets, the approach achieves high segmentation accuracy across different organ types with a fraction of the annotations required by conventional methods such as SVM and RF. Results indicate a mean Intersection over Union (IoU) of 0.981 using only a few annotations per class.
3. Versatility: The application of ViTs for feature extraction is not domain-specific, demonstrating applicability across different data types, including CT and MRI scans, and varied anatomical structures.
4. Interactivity and User Experience: The method offers real-time feedback after user annotations, substantially enhancing the user experience and speeding up the exploration process. The immediate visual feedback allows users to make informed decisions about further annotations required to achieve accurate segmentation.

Potential and Future Work

The introduction of self-supervised ViTs to transfer function design paves the way for more sophisticated algorithms that leverage pre-trained models' powerful generalization capabilities. The paper's approach makes a compelling case for future work to explore larger transformer models and potentially integrate cross-modal models like CLIP, which could introduce semantic annotations through natural language.

Moreover, addressing the issue of overlapping structures in segmented volumetric data through enhanced feature refinement and the development of negative annotation capabilities present opportunities for further refinement. The reduction in memory demands during feature extraction and a deeper exploration of feature space are additional promising directions.

Conclusion

This study delivers a significant contribution to the domain of volume rendering and computer graphics by using self-supervised ViTs for transfer function design. Through an interactive and efficient paradigm, this method admirably tackles the complexities of conventional approaches, suggesting a pivotal shift towards using pre-trained models in visualization tasks. As deep learning models and hardware capabilities continue to evolve, this work sets a foundation for developing more adaptive and accessible visualization tools in scientific computing.