Radiomics and Gaze-guided Medical Image Generation using Diffusion Models

Core Concepts

Integrating radiologists' eye gaze patterns and radiomic feature maps as controls to text-to-image diffusion models can generate high-fidelity and clinically accurate medical images.

Abstract

The paper presents RadGazeGen, a novel framework for integrating experts' eye gaze patterns and radiomic feature maps as controls to text-to-image diffusion models for high-fidelity medical image generation.
Key highlights:

Text descriptions are often inadequate to convey detailed disease-specific information for generating clinically accurate medical images. Anatomy, disease texture patterns, and location of the disease are crucial for realistic image generation.
Eye gaze patterns of radiologists provide important visuo-cognitive information indicative of subtle disease patterns and spatial location. Radiomic features provide important subvisual cues regarding disease phenotype.
RadGazeGen uses these gaze patterns in combination with radiomics descriptors as controls to generate anatomically correct and disease-aware medical images.
RadGazeGen is evaluated for image generation quality and diversity, and also demonstrates improved classification performance on the generated images compared to text-to-image diffusion baselines.
The proposed method also shows benefits in long-tailed disease classification tasks by generating additional images for underrepresented classes.
Qualitative results validate that RadGazeGen generates clinically accurate disease patterns that are consistent with radiologist annotations.

Stats

Text descriptions are often inadequate to convey detailed disease-specific information for generating clinically accurate medical images.
Eye gaze patterns of radiologists provide important visuo-cognitive information indicative of subtle disease patterns and spatial location.
Radiomic features provide important subvisual cues regarding disease phenotype.

Quotes

"Text descriptions are often found to be inadequate and fail to convey detailed disease-specific information to these models to generate clinically accurate images."
"Eye gaze patterns of radiologists are important visuo-cognitive information, indicative of subtle disease patterns and spatial location."
"Radiomic features further provide important subvisual cues regarding disease phenotype."

Key Insights Distilled From

RadGazeGen: Radiomics and Gaze-guided Medical Image Generation using Diffusion Models

by Moinak Bhatt... at arxiv.org 10-02-2024

https://arxiv.org/pdf/2410.00307.pdf

Deeper Inquiries

How can the RadGazeGen framework be extended to other medical imaging modalities beyond chest X-rays?

The RadGazeGen framework can be extended to other medical imaging modalities, such as MRI, CT scans, and ultrasound, by adapting its architecture to accommodate the unique characteristics and requirements of these imaging techniques. This involves several key steps:

Modality-Specific Radiomics Features: Each imaging modality has distinct features and patterns. For instance, MRI images may require different radiomic filters that capture texture and intensity variations specific to soft tissues. Developing a set of modality-specific radiomics features will enhance the model's ability to generate clinically relevant images.

3D/4D Data Handling: Unlike chest X-rays, which are typically 2D, modalities like MRI and CT often involve 3D or even 4D data (including time as a variable). The RadGazeGen framework would need to incorporate 3D convolutional neural networks (CNNs) or volumetric data processing techniques to effectively handle and generate 3D images.

Eye Gaze Data Collection: For modalities where eye gaze patterns are relevant, such as MRI or CT, it is essential to collect eye gaze data from radiologists during the interpretation of these images. This data can then be used to compute Human Visual Attention (HVA) maps specific to the modality, which can be integrated into the RadGazeGen framework.

Training on Diverse Datasets: To ensure the model generalizes well across different modalities, it should be trained on diverse datasets that include a wide range of pathologies and imaging conditions. This will help the model learn to generate images that are not only high-quality but also representative of various clinical scenarios.

Integration of Additional Controls: The framework can incorporate additional controls relevant to other modalities, such as anatomical segmentation maps or functional imaging data, to further enhance the fidelity of generated images.

By implementing these strategies, RadGazeGen can effectively adapt to various medical imaging modalities, thereby broadening its applicability and impact in clinical settings.

What other clinically relevant control signals, beyond radiomics and eye gaze, could be integrated to further improve the fidelity of generated medical images?

In addition to radiomics and eye gaze, several other clinically relevant control signals can be integrated into the RadGazeGen framework to enhance the fidelity of generated medical images:

Patient Demographics and Clinical History: Incorporating patient-specific information, such as age, gender, medical history, and genetic predispositions, can help tailor the generated images to reflect individual patient characteristics. This contextual information can improve the relevance and accuracy of the generated images in clinical scenarios.

Anatomical Segmentation Maps: Detailed anatomical segmentation maps can provide precise information about the location and boundaries of organs and structures within the images. This can guide the generation process to ensure that anatomical features are accurately represented, leading to more realistic and clinically useful images.

Pathology-Specific Features: Integrating pathology-specific features, such as biomarkers or histopathological data, can enhance the model's ability to generate images that reflect specific disease characteristics. This could involve using data from pathology reports or laboratory results to inform the image generation process.

Temporal Data: For modalities that capture dynamic processes, such as functional MRI or cardiac imaging, incorporating temporal data can help generate images that reflect changes over time. This can be particularly useful for assessing disease progression or treatment response.

Expert Annotations and Feedback: Utilizing annotations and feedback from radiologists and other medical professionals can provide valuable insights into the features that are critical for accurate diagnosis. This information can be used to refine the generation process and improve the clinical relevance of the images.

By integrating these additional control signals, the RadGazeGen framework can produce even more accurate and clinically relevant medical images, ultimately enhancing its utility in various medical applications.

How can the generated medical images from RadGazeGen be leveraged to enhance downstream tasks such as disease diagnosis, treatment planning, and clinical decision support?

The generated medical images from the RadGazeGen framework can significantly enhance downstream tasks in several ways:

Disease Diagnosis: The high-quality, clinically relevant images produced by RadGazeGen can be used to train and validate machine learning models for disease classification. By providing a diverse set of synthetic images that accurately reflect various pathologies, these models can improve their diagnostic accuracy and robustness, particularly in cases where real data is scarce or imbalanced.

Treatment Planning: Generated images can assist clinicians in visualizing potential treatment outcomes or disease progression. For instance, in oncology, synthetic images can be used to simulate the effects of different treatment modalities on tumor size and location, aiding in personalized treatment planning and decision-making.

Clinical Decision Support: The integration of generated images into clinical decision support systems can provide healthcare professionals with additional visual information to complement their assessments. This can enhance the interpretability of diagnostic tools and improve the overall quality of care by providing a more comprehensive view of the patient's condition.

Medical Education and Training: Generated images can serve as valuable resources for medical education, allowing students and trainees to familiarize themselves with a wide range of pathologies and imaging findings. This can enhance their learning experience and prepare them for real-world clinical scenarios.

Research and Development: In research settings, generated images can be used to explore new diagnostic techniques, evaluate the performance of imaging modalities, or study disease mechanisms. This can facilitate innovation in medical imaging and contribute to advancements in the field.

By leveraging the capabilities of RadGazeGen, healthcare providers can enhance their diagnostic processes, improve treatment planning, and support clinical decision-making, ultimately leading to better patient outcomes.