用3D小波扩散模型修复脑肿瘤图像并补全缺失的MRI模态。
Brain Tumour Removing and Missing Modality Generation using 3D WDM
- 基于条件3D小波扩散模型，实现全分辨率图像重建。
- 在48GB显存GPU上无需分块或下采样，保留完整信息。
- 适合处理脑肿瘤图像分析中模态缺失问题的研究者。
本文展示了参加BraTS 2024任务7及任务8的第二名解决方案。随着自动化脑部分析算法在临床中的应用增加，许多算法在存在脑部病变或部分MRI模态缺失时表现不佳。脑部形态变化导致高度可变性，使仅在健康脑数据上训练的预测模型性能下降。缺少某些模态提供的信息也降低了多模态训练模型的可靠性。为提升模型性能，本文提出使用条件3D小波扩散模型。该方法支持全分辨率图像训练与预测，在配备48GB VRAM的GPU上无需分块或下采样，完整保留用于预测的信息。相关代码已开源：https://github.com/ShadowTwin41/BraTS_2023_2024_solutions。
原文摘要 · Abstract (English)
This paper presents the second-placed solution for task 8 and the participation solution for task 7 of BraTS 2024. The adoption of automated brain analysis algorithms to support clinical practice is increasing. However, many of these algorithms struggle with the presence of brain lesions or the absence of certain MRI modalities. The alterations in the brain's morphology leads to high variability and thus poor performance of predictive models that were trained only on healthy brains. The lack of information that is usually provided by some of the missing MRI modalities also reduces the reliability of the prediction models trained with all modalities. In order to improve the performance of these models, we propose the use of conditional 3D wavelet diffusion models. The wavelet transform enabled full-resolution image training and prediction on a GPU with 48 GB VRAM, without patching or downsampling, preserving all information for prediction. The code for these tasks is available at https://github.com/ShadowTwin41/BraTS_2023_2024_solutions.
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