使用对比非配对图像翻译从非增强CT扫描中进行心脏腔室分割的前景与挑战：一项可行性研究
Promise and challenges of heart chamber segmentation from non-contrast CT scans using contrastive unpaired image translation: a feasibility study
AI总结：
提出ChameleonNet框架，利用对比非配对翻译生成非增强CT，结合深度学习分割心脏四腔，在合成图像上DSC达0.91-0.94，但真实CT上体积误差较大，需进一步改进。
摘要：
目的：评估使用对比非配对图像翻译和基于深度学习的分割方法从非增强CT扫描中进行心脏腔室分割的可行性和挑战。方法：我们开发了ChameleonNet框架，该框架利用带有解耦对比学习损失的对比非配对翻译网络从增强CT扫描合成非增强CT。使用来自增强扫描的四个心脏腔室（左心房、左心室、右心房、右心室）的标注，我们在合成的非增强图像上训练了Hausdorff距离损失增强的nnU-Net。翻译模型使用35,538张增强CT切片和37,197张非增强CT切片进行训练。分割模型使用292张合成的非增强扫描进行训练。性能通过Dice相似系数和95th Hausdorff距离在36张合成非增强扫描上评估，并通过Pearson相关性、平均绝对百分比误差和平均百分比误差在36张真实非增强CT扫描上评估体积一致性。结果：分割模型在合成非增强图像上对左心房、左心室、右心房、右心室分别实现了DSC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）毫米。在真实非增强CT扫描上，Pearson相关性分别为0.93、0.82、0.87和0.89（所有p<0.001），MAPE范围为9.22%至20.79%，MPE范围为-12.52%至4.67%。结论：ChameleonNet展示了在无需手动非增强标注的情况下从非增强CT进行心脏腔室分割的可行性。然而，体积误差，特别是左心室和右心室的误差，表明在临床使用前需要进一步改进和验证。
英文摘要：
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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