用错误图引导扩散模型，提升脑肿瘤MRI分割精度
DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images
- 将输入图像与误差图拼接，指导扩散模型修正分割结果
- 在BraTS2020上达Dice 93.46，HD95为5.94mm，优于现有方法
- 适合需要高精度脑肿瘤分割的临床研究与辅助诊断场景
准确分割MRI中的脑肿瘤对临床诊断和治疗规划至关重要。近期扩散模型在图像生成与分割任务中表现出色。本文提出一种基于扩散模型的修正分割新方法——DMCIE（输入与误差拼接的扩散模型），用于多模态MRI扫描中的脑肿瘤精准分割。首先使用3D U-Net生成初始分割掩码，并通过对比预测与真实标签生成误差图。将误差图与原始MRI图像拼接后输入扩散模型，以聚焦误判区域进行修正。利用多模态MRI输入（T1、T1ce、T2、FLAIR），DMCIE显著提升分割精度。在BraTS2020数据集上的评估显示，其Dice分数达93.46，平均表面距离HD95为5.94mm，优于多个先进扩散模型方法，验证了误差引导扩散在生成精确可靠脑肿瘤分割结果方面的有效性。
原文摘要 · Abstract (English)
Accurate segmentation of brain tumors in MRI scans is essential for reliable clinical diagnosis and effective treatment planning. Recently, diffusion models have demonstrated remarkable effectiveness in image generation and segmentation tasks. This paper introduces a novel approach to corrective segmentation based on diffusion models. We propose DMCIE (Diffusion Model with Concatenation of Inputs and Errors), a novel framework for accurate brain tumor segmentation in multi-modal MRI scans. We employ a 3D U-Net to generate an initial segmentation mask, from which an error map is generated by identifying the differences between the prediction and the ground truth. The error map, concatenated with the original MRI images, are used to guide a diffusion model. Using multimodal MRI inputs (T1, T1ce, T2, FLAIR), DMCIE effectively enhances segmentation accuracy by focusing on misclassified regions, guided by the original inputs. Evaluated on the BraTS2020 dataset, DMCIE outperforms several state-of-the-art diffusion-based segmentation methods, achieving a Dice Score of 93.46 and an HD95 of 5.94 mm. These results highlight the effectiveness of error-guided diffusion in producing precise and reliable brain tumor segmentations.
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