通过使用本地训练的潜在扩散模型进行领域适应以缓解3D前列腺双参数MRI数据稀缺问题
Mitigating 3D Prostate Biparametric MRI Data Scarcity through Domain Adaptation using Locally-Trained Latent Diffusion Models for Prostate Cancer Detection
AI总结：
本文提出CCELLA++，一种新的潜在扩散模型流程，用于同时生成3D双参数前列腺MRI（bpMRI），包括轴向T2加权（AxT2）、高b值扩散系列（HighB）和表观扩散系数图（ADC），以克服数据稀缺问题。
摘要：
目的：潜在扩散模型（LDMs）可以缓解影响医疗图像解释机器学习开发的数据稀缺挑战。最近的CCELLA LDM通过合成MRI用于分类器训练提高了前列腺癌检测性能，但仅限于轴向T2加权（AxT2）序列，未研究跨机构领域偏移，并优先考虑PI-RADS而非组织学结果。方法：我们提出CCELLA++，一种新的LDM流程，用于同时生成3D双参数前列腺MRI（bpMRI），包括AxT2、高b值扩散系列（HighB）和表观扩散系数图（ADC），以克服这些限制。我们研究了源无关领域适应，使用在单个机构真实或LDM生成的合成数据上预训练的分类器，在微调前进行外部分布数据集的分数。结果：CCELLA++在AxT2核启动距离上与CCELLA相当（0.0128，0.0131分别）。CCELLA++合成bpMRI预训练在AP和AUC上优于真实bpMRI，达到12.5%（n≤166）外部数据集体积（p<0.01所有），无预训练在AUC上达到25%外部体积（n=332，p<0.05所有），并且CCELLA AxT2-only预训练在数据稀缺（n=83，p<0.001 AP和AUC）和完整数据（n=1329，p<0.05 AP和AUC）场景中均优于。结论：CCELLA++合成bpMRI可以提高下游分类器的泛化能力和性能，优于真实bpMRI或CCELLA生成的AxT2-only图像。未来的工作应量化医疗图像质量，平衡bpMRI LDM训练，并将LDM与额外信息相结合。意义：CCELLA++可以生成优于真实数据的合成bpMRI，用于数据稀缺外部机构的领域适应，推动医疗影像机器学习的发展。我们的代码可在https://github.com/grabkeem/CCELLA-plus-plus获取。
英文摘要：
Objective: Latent diffusion models (LDMs) could mitigate data scarcity challenges affecting machine learning development for medical image interpretation. The recent CCELLA LDM improved prostate cancer detection performance using synthetic MRI for classifier training but was limited to the axial T2-weighted (AxT2) sequence, did not investigate inter-institutional domain shift, and prioritized PI-RADS over histopathology outcomes. Methods: We propose CCELLA++, a novel LDM pipeline for simultaneous 3D biparametric prostate MRI (bpMRI) generation, including the AxT2, high b-value diffusion series (HighB) and apparent diffusion coefficient map (ADC), to overcome these limitations. We investigated source-free domain adaptation with classifiers pretrained on single institution real or LDM-generated synthetic data prior to fine-tuning on fractions of an out-of-distribution, external dataset. Results: CCELLA++ achieved comparable AxT2 Kernel Inception Distance to CCELLA (0.0128, 0.0131 respectively). CCELLA++ synthetic bpMRI pretraining outperformed real bpMRI in AP and AUC up to 12.5% (n<=166) external dataset volume (p<0.01 all), no pretraining in AUC up to 25% external volume (n=332, p<0.05 all), and CCELLA AxT2-only pretraining in both data-scarce (n=83, p<0.001 AP and AUC) and full data (n=1329, p<0.05 AP and AUC) scenarios. Conclusion: CCELLA++ synthetic bpMRI can improve downstream classifier generalization and performance beyond real bpMRI or CCELLA-generated AxT2-only images. Future work should quantify medical image quality, balance bpMRI LDM training, and condition the LDM with additional information. Significance: CCELLA++ can generate synthetic bpMRI that outperforms real data for domain adaptation with data-scarce external institutions, advancing machine learning development for medical imaging. Our code is available at https://github.com/grabkeem/CCELLA-plus-plus
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
