用AI把增强CT转成非增强CT，实现心脏腔室自动分割
Promise and challenges of heart chamber segmentation from non-contrast CT scans using contrastive unpaired image translation: a feasibility study
- 用对比无配对图像翻译生成非增强CT图像
- 分割准确率高（DSC达0.94），但左/右心室体积误差大
- 适合无需手动标注的医学影像自动化分析场景
目的：评估基于对比无配对图像转换与深度学习分割技术，从非增强CT扫描中进行心脏腔室分割的可行性与挑战。方法：我们提出ChameleonNet框架，利用带有解耦对比学习（DCL）损失的对比无配对翻译（CUT）网络，将增强CT转化为非增强CT。基于增强CT上的四个心腔（左心房LA、左心室LV、右心房RA、右心室RV）标注，我们在合成的非增强图像上训练了增强豪斯多夫距离损失的nnU-Net分割模型。翻译模型使用35,538个增强与37,197个非增强CT切片训练，分割模型在292个合成非增强扫描上训练。性能在36个合成非增强扫描上通过骰子相似系数（DSC）和95%豪斯多夫距离（HD95）评估，并在36个真实非增强CT上通过皮尔逊相关性、平均绝对百分比误差（MAPE）和平均百分比误差（MPE）评估体积一致性。结果：在合成数据上，各腔室的DSC分别为0.94（0.01）、0.91（0.04）、0.92（0.03）、0.93（0.02），HD95为3.63（1.49）、5.74（4.08）、5.18（1.77）、5.51（3.21）mm。在真实非增强数据上，相关系数分别为0.93、0.82、0.87、0.89（均p<0.001），MAPE在9.22%至20.79%之间，MPE在-12.52%至4.67%之间。结论：ChameleonNet展示了从非增强CT中进行心脏腔室分割的可行性，无需人工标注非增强图像。然而，尤其是左心室和右心室的体积误差表明，仍需进一步优化和验证方可用于临床。
原文摘要 · Abstract (English)
Purpose: To evaluate the feasibility and challenges of heart chamber segmentation from non-contrast CT scans using contrastive unpaired image translation and deep learning-based segmentation. Approach: We developed ChameleonNet, a framework utilizing the Contrastive Unpaired Translation (CUT) network with decoupled contrastive learning (DCL) loss to synthesize non-contrast CT from contrast CT scans. Using annotations of four heart chambers (left atrium (LA), left ventricle (LV), right atrium (RA), and right ventricle (RV)) from contrast scans, we trained a Hausdorff distance loss-enhanced nnU-Net on synthesized non-contrast images. The translation model was trained with 35,538 contrast-enhanced and 37,197 non-contrast CT slices. The segmentation model was trained with 292 synthesized non-contrast scans. Performance was evaluated using Dice similarity coefficient (DSC) and 95th Hausdorff distance (HD95) on 36 synthesized non-contrast scans, and volume agreement on 36 real non-contrast CT scans was assessed using Pearson correlation, mean absolute percentage error (MAPE), and mean percentage error (MPE). Results: The segmentation model achieved DSC of 0.94 (0.01), 0.91 (0.04), 0.92 (0.03), 0.93 (0.02), and HD95 of 3.63 (1.49), 5.74 (4.08), 5.18 (1.77), 5.51 (3.21) mm on synthesized non-contrast images for LA, LV, RA, and RV, respectively. On real non-contrast CT scans, Pearson correlations were 0.93, 0.82, 0.87, and 0.89 (all p<0.001), with MAPE ranging from 9.22% to 20.79%, and MPE ranging from -12.52% to 4.67%. Conclusions: ChameleonNet demonstrated feasibility for heart chamber segmentation from non-contrast CT without manual non-contrast annotations. However, volume errors, particularly for LV and RV, indicate that further refinement and validation are needed before clinical use.
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