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Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion models

Tianyi Ren, Abhishek Sharma, Juampablo Heras Rivera, Harshitha Rebala, Ethan Honey, Agamdeep Chopra, Jacob Ruzevick, Mehmet Kurt

TL;DR

Glioblastoma tumor margin segmentation requires precise boundary delineation, yet contemporary models struggle with boundary fidelity. Re-DiffiNet introduces a discrepancy-focused diffusion approach: a baseline U-Net predicts segmentation masks, while a conditional diffusion model estimates the discrepancy between those predictions and the ground truth, with Δx_0 = |U(I) − x_0| and Δ̂x_0 guiding refinement to ̂x_0 = |U(I) − Δ̂x_0|. On the BraTS 2023 dataset with 5-fold cross-validation, this discrepancy modeling achieves a 16.28% reduction in HD95 and a 0.55% improvement in Dice compared to the baseline, outperforming direct diffusion-based segmentation. The results highlight that diffusion models excel at refining boundary details when used to model and correct prediction discrepancies, offering a practical boost for boundary-sensitive clinical decision-making and potential generalization to other tumor types.

Abstract

Identification of tumor margins is essential for surgical decision-making for glioblastoma patients and provides reliable assistance for neurosurgeons. Despite improvements in deep learning architectures for tumor segmentation over the years, creating a fully autonomous system suitable for clinical floors remains a formidable challenge because the model predictions have not yet reached the desired level of accuracy and generalizability for clinical applications. Generative modeling techniques have seen significant improvements in recent times. Specifically, Generative Adversarial Networks (GANs) and Denoising-diffusion-based models (DDPMs) have been used to generate higher-quality images with fewer artifacts and finer attributes. In this work, we introduce a framework called Re-Diffinet for modeling the discrepancy between the outputs of a segmentation model like U-Net and the ground truth, using DDPMs. By explicitly modeling the discrepancy, the results show an average improvement of 0.55\% in the Dice score and 16.28\% in HD95 from cross-validation over 5-folds, compared to the state-of-the-art U-Net segmentation model.

Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion models

TL;DR

Glioblastoma tumor margin segmentation requires precise boundary delineation, yet contemporary models struggle with boundary fidelity. Re-DiffiNet introduces a discrepancy-focused diffusion approach: a baseline U-Net predicts segmentation masks, while a conditional diffusion model estimates the discrepancy between those predictions and the ground truth, with Δx_0 = |U(I) − x_0| and Δ̂x_0 guiding refinement to ̂x_0 = |U(I) − Δ̂x_0|. On the BraTS 2023 dataset with 5-fold cross-validation, this discrepancy modeling achieves a 16.28% reduction in HD95 and a 0.55% improvement in Dice compared to the baseline, outperforming direct diffusion-based segmentation. The results highlight that diffusion models excel at refining boundary details when used to model and correct prediction discrepancies, offering a practical boost for boundary-sensitive clinical decision-making and potential generalization to other tumor types.

Abstract

Identification of tumor margins is essential for surgical decision-making for glioblastoma patients and provides reliable assistance for neurosurgeons. Despite improvements in deep learning architectures for tumor segmentation over the years, creating a fully autonomous system suitable for clinical floors remains a formidable challenge because the model predictions have not yet reached the desired level of accuracy and generalizability for clinical applications. Generative modeling techniques have seen significant improvements in recent times. Specifically, Generative Adversarial Networks (GANs) and Denoising-diffusion-based models (DDPMs) have been used to generate higher-quality images with fewer artifacts and finer attributes. In this work, we introduce a framework called Re-Diffinet for modeling the discrepancy between the outputs of a segmentation model like U-Net and the ground truth, using DDPMs. By explicitly modeling the discrepancy, the results show an average improvement of 0.55\% in the Dice score and 16.28\% in HD95 from cross-validation over 5-folds, compared to the state-of-the-art U-Net segmentation model.
Paper Structure (12 sections, 5 equations, 3 figures, 2 tables)

This paper contains 12 sections, 5 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Model Architectures
  4. Baseline U-Net
  5. U-Net augmented Diffusion (UA-Diffusion)
  6. Re-DiffiNet
  7. Training details
  8. Dataset
  9. Experiments and Results
  10. Discussion and Conclusion
  11. Examples of the segmentation results
  12. Statistics Analysis

Figures (3)

  • Figure 1: Re-DiffiNet uses MRI and predictions from baseline U-Net as inputs to generate predictions about incorrect voxels in U-Net predictions and corrects those voxels to generate redefined tumor masks.
  • Figure 2: A comparison between the segmentations generated by baseline U-Net, and Re-DiffiNet. In this example, Re-DiffiNet can predict the false positive lesion on Tumor core masks that was predicted by baseline U-Net. Meanwhile, Re-DiffiNet predicts a smoother boundary.
  • Figure A.1: The left example shows a case where Re-DiffiNet improves the HD95 score by an average of 0.12mm, with the Dice improvement being only 0.03%. Conversely, the right example is improved by Re-DiffiNet for 3.09% and 0.61mm for the Dice score and the HD95 score respectively, when compared to the baseline U-Net.