mlsanityData to 14 Oct 2026

ReCoSeg++:Extended Residual-Guided Cross-Modal Diffusion for Brain Tumor Segmentation

arXiv 2508.01058 ↗First posted 1 Aug 2025eess.IV cs.CVcited by 3

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Accurate segmentation of brain tumors in MRI scans is critical for clinical diagnosis and treatment planning. We propose a semi-supervised, two-stage framework that extends the ReCoSeg approach to the larger and more heterogeneous BraTS 2021 dataset, while eliminating the need for ground-truth masks for the segmentation objective. In the first stage, a residual-guided denoising diffusion probabilistic model (DDPM) performs cross-modal synthesis by reconstructing the T1ce modality from FLAIR, T1, and T2 scans. The residual maps, capturing differences between predicted and actual T1ce images, serve as spatial priors to enhance downstream segmentation. In the second stage, a lightweight U-Net takes as input the concatenation of residual maps, computed as the difference between real T1ce and synthesized T1ce, with T1, T2, and FLAIR modalities to improve whole tumor segmentation. To address the increased scale and variability of BraTS 2021, we apply slice-level filtering to exclude non-informative samples and optimize thresholding strategies to balance precision and recall. Our method achieves a Dice score of 93.02% and an IoU of 86.7% for whole tumor segmentation on the BraTS 2021 dataset, outperforming the ReCoSeg baseline on BraTS 2020 (Dice: 91.7%, IoU: 85.3%), and demonstrating improved accuracy and scalability for real-world, multi-center MRI datasets.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 31 of its 48 references that are in the map. First posted on arXiv 1 Aug 2025.

Builds on

31 of 48 references.

Closest ancestors · 8

Same direction, newest first

  1. 2025DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images1 Jul
  2. 2025Diffusion Models for Computational Neuroimaging: A Survey10 Feb
  3. 2024Promptable Counterfactual Diffusion Model for Unified Brain Tumor Segmentation and Generation with MRIs17 Jul
  4. 2023DDMM-Synth: A Denoising Diffusion Model for Cross-modal Medical Image Synthesis with Sparse-view Measurement Embedding28 Mar
  5. 2023MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer19 Jan
  6. 2022Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation27 Oct
  7. 2021Diffusion Models for Implicit Image Segmentation Ensembles6 Dec
  8. 2021SegDiff: Image Segmentation with Diffusion Probabilistic Models1 Dec

Other references · 23

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  5. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
  6. 2022Generative Adversarial Networkscited by 5,627
  7. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  8. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
  9. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  10. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  11. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  12. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
11 more
  1. 2022Imagic: Text-Based Real Image Editing with Diffusion Modelscited by 1,178
  2. 2021TransBTS: Multimodal Brain Tumor Segmentation Using Transformercited by 265
  3. 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257
  4. 2023Implicit Diffusion Models for Continuous Super-Resolutioncited by 218
  5. 2018Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networkscited by 147
  6. 2024LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentationcited by 54
  7. 2020Optimization for Medical Image Segmentation: Theory and Practice when evaluating with Dice Score or Jaccard Indexcited by 52
  8. 2021Optimized U-Net for Brain Tumor Segmentationcited by 40
  9. 2020Vox2Vox: 3D-GAN for Brain Tumour Segmentationcited by 30
  10. 2021Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIscited by 15
  11. 2020A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentationcited by 9

Built on by

Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025VGDM: Vision-Guided Diffusion Model for Brain Tumor Detection and Segmentationcited by 1
  2. 2025SER-Diff: Synthetic Error Replay Diffusion for Incremental Brain Tumor Segmentationcited by 0
  3. 2025NNDM: NN_UNet Diffusion Model for Brain Tumor Segmentationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Diffusion-Refined Segmentation and Vision-Language Interpretation for Pediatric Brain Tumor MRI
  2. 2025Prior-Guided Residual Diffusion: Calibrated and Efficient Medical Image Segmentation
  3. 2023A Recycling Training Strategy for Medical Image Segmentation with Diffusion Denoising Models
  4. 20243D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes
  5. 2026VS-DDPM: Efficient Low-Cost Diffusion Model for Medical Modality Translation
  6. 2023Memory-Efficient 3D Denoising Diffusion Models for Medical Image Processing
  7. 2024cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis
  8. 2024Stable Diffusion Segmentation for Biomedical Images with Single-step Reverse Process