Conditional Random Fields (CRFs) are often used to improve the output of an initial segmentation model, such as a convolutional neural network (CNN). Conventional CRF approaches in medical imaging use manually defined features, such as intensity to improve appearance similarity or location to improve spatial coherence. These features work well for some tasks, but can fail for others. For example, in medical image segmentation applications where different anatomical structures can have similar intensity values, an intensity-based CRF may produce incorrect results. As an alternative, we propose Posterior-CRF, an end-to-end segmentation method that uses CNN-learned features in a CRF and optimizes the CRF and CNN parameters concurrently. We validate our method on three medical image segmentation tasks: aorta and pulmonary artery segmentation in non-contrast CT, white matter hyperintensities segmentation in multi-modal MRI, and ischemic stroke lesion segmentation in multi-modal MRI. We compare this with the state-of-the-art CNN-CRF methods. In all applications, our proposed method outperforms the existing methods in terms of Dice coefficient, average volume difference, and lesion-wise F1 score.

条件随机场 (CRF) 通常用于改进初始分割模型的输出，例如卷积神经网络 (CNN)。医学成像中的传统 CRF 方法使用手动定义的特征，例如提高外观相似性的强度或提高空间相干性的位置。这些功能适用于某些任务，但可能会失败。例如，在不同解剖结构可能具有相似强度值的医学图像分割应用中，基于强度的 CRF 可能会产生不正确的结果。作为替代方案，我们提出后 CRF，一种端到端的分割方法，在 CRF 中使用 CNN 学习的特征，并同时优化 CRF 和 CNN 参数。我们在三个医学图像分割任务上验证了我们的方法：非对比 CT 中的主动脉和肺动脉分割、多模态 MRI 中的白质高信号分割以及多模态 MRI 中的缺血性中风病变分割。我们将其与最先进的 CNN-CRF 方法进行比较。在所有应用中，我们提出的方法在 Dice 系数、平均体积差和病变 F1 分数方面都优于现有方法。