MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model (2211.00611v5)

Abstract: Diffusion probabilistic model (DPM) recently becomes one of the hottest topic in computer vision. Its image generation application such as Imagen, Latent Diffusion Models and Stable Diffusion have shown impressive generation capabilities, which aroused extensive discussion in the community. Many recent studies also found it is useful in many other vision tasks, like image deblurring, super-resolution and anomaly detection. Inspired by the success of DPM, we propose the first DPM based model toward general medical image segmentation tasks, which we named MedSegDiff. In order to enhance the step-wise regional attention in DPM for the medical image segmentation, we propose dynamic conditional encoding, which establishes the state-adaptive conditions for each sampling step. We further propose Feature Frequency Parser (FF-Parser), to eliminate the negative effect of high-frequency noise component in this process. We verify MedSegDiff on three medical segmentation tasks with different image modalities, which are optic cup segmentation over fundus images, brain tumor segmentation over MRI images and thyroid nodule segmentation over ultrasound images. The experimental results show that MedSegDiff outperforms state-of-the-art (SOTA) methods with considerable performance gap, indicating the generalization and effectiveness of the proposed model. Our code is released at https://github.com/WuJunde/MedSegDiff.

MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model

The paper presents "MedSegDiff," a novel framework leveraging Diffusion Probabilistic Models (DPMs) to enhance medical image segmentation. The authors introduce this approach to address challenges in segmenting various medical imaging modalities, such as fundus images, MRI, and ultrasound. MedSegDiff distinguishes itself as the first application of DPMs in a general medical image segmentation context, aiming to overcome the inherent ambiguities often encountered in medical images.

Key Contributions

1. Dynamic Conditional Encoding: The authors propose a dynamic conditional encoding strategy to enhance regional attention throughout the diffusion process. This approach adapts the conditional features step-wise, integrating the current segmentation prediction into the image prior, thus offering improved segmentation results.
2. Feature Frequency Parser (FF-Parser): The FF-Parser is introduced to mitigate high-frequency noise effects during the segmentation process. It employs a Fourier domain approach to filter noise and enhance feature integration accuracy.
3. Performance Evaluation: The MedSegDiff model demonstrates superior performance over state-of-the-art (SOTA) segmentation methods across three medical imaging tasks—optic cup segmentation, brain tumor segmentation, and thyroid nodule segmentation—showcasing its generalization capability across diverse modalities.

Methodology

MedSegDiff utilizes the inherent strengths of DPMs, known for high-quality image generation capabilities, to address the unique challenges of medical image segmentation. The architecture involves a modified ResUNet, where segmentation maps iteratively refine through a diffusion process guided by adaptive dynamic conditioning. In this process, the conditional encodings at each step dynamically adjust according to prior segmentation and image conditions, enabling precise localization and calibration.

The FF-Parser complements this by processing features in the Fourier space, effectively reducing noise by modulating spectral components. This component is vital for ensuring clarity and precision in regions prone to ambiguities, a common occurrence in medical imaging.

Experimental Results

The MedSegDiff model was validated on diverse datasets representing different imaging modalities, demonstrating substantial improvements over existing SOTA methods. Notably, it achieved:

• Optic Cup Segmentation: Demonstrated a Dice score improvement with clear delineation against competing methods.
• Brain Tumor Segmentation: Surpassed DPM-based models like EnsemDiff, highlighting its robust adaptability and effectiveness.
• Thyroid Nodule Segmentation: Achieved significant precision, validating its cross-modal applicability.

Implications and Future Directions

The results of the paper suggest a promising avenue for employing diffusion models in structured and high-stakes domains like medical imaging. MedSegDiff not only enhances segmentation accuracy but also sets a foundation for further exploration of DPM adaptations in other medical imaging tasks. Future developments could explore extended model configurations, integration with other AI frameworks, and tailored optimizations for specific medical fields.

Moreover, the generalization potential observed across different datasets hints at the utility of MedSegDiff in real-world clinical settings, where diverse imaging types and conditions are commonplace. Exploration into automated diagnosis or treatment planning, supported by MedSegDiff's high precision, could further impact medical practices, potentially reducing the workload on professionals and increasing accuracy in diagnostics.

In conclusion, MedSegDiff offers a significant methodological advancement in medical image segmentation, combining the recent successes of DPMs with novel strategies tailored to the domain's challenges. This work paves the way for future research focused on integrating generative models within medical AI applications, suggesting numerous possibilities for both theoretical exploration and practical deployment.