用去噪自编码器检测器官分割错误，支持多模态且可解释。
Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders
- 对真实分割图加噪声，让自编码器尝试还原，以识别错误区域。
- 在MR和CT数据上均表现良好，多数器官检测准确率优于现有方法。
- 输出重建图像，直观展示分割问题位置，适合临床医生核查。
在放疗规划中，关键器官的分割不准确可能导致治疗效果不佳，若未被医生发现则风险更大。为应对这一挑战，本文提出一种基于去噪自编码器的方法来检测器官分割错误。通过向真实分割图添加噪声，训练自编码器进行去噪还原。该方法在基于MRI和CT扫描生成的器官分割结果上进行了验证，证明其对成像模态具有独立性。通过提供重建结果，本方法可直观显示分割误差区域，实现更可解释的异常检测。与文献中已有方法相比，该方法在多数器官上的检测性能更优。
原文摘要 · Abstract (English)
In radiation therapy planning, inaccurate segmentations of organs at risk can result in suboptimal treatment delivery, if left undetected by the clinician. To address this challenge, we developed a denoising autoencoder-based method to detect inaccurate organ segmentations. We applied noise to ground truth organ segmentations, and the autoencoders were tasked to denoise them. Through the application of our method to organ segmentations generated on both MR and CT scans, we demonstrated that the method is independent of imaging modality. By providing reconstructions, our method offers visual information about inaccurate regions of the organ segmentations, leading to more explainable detection of suboptimal segmentations. We compared our method to existing approaches in the literature and demonstrated that it achieved superior performance for the majority of organs.
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