Purpose: Conventional automated segmentation of the head anatomy in MRI distinguishes different brain and non-brain tissues based on image intensities and prior tissue probability maps (TPM). This works well for normal head anatomies, but fails in the presence of unexpected lesions. Deep convolutional neural networks leverage instead spatial patterns and can learn to segment lesions, but often ignore prior probabilities. Approach: We add three sources of prior information to a three-dimensional convolutional network, namely, spatial priors with a TPM, morphological priors with conditional random fields, and spatial context with a wider field-of-view at lower resolution. We train and test these networks on 3D images of 43 stroke patients and 4 healthy individuals which have been manually segmented. Results: We demonstrate the benefits of each sources of prior information, and we show that the new architecture, which we call Multiprior network, improves the performance of existing segmentation software, such as SPM, FSL, and DeepMedic for abnormal anatomies. The relevance of the different priors was compared and the TPM was found to be most beneficial. The benefit of adding a TPM is generic in that it can boost the performance of established segmentation networks such as the DeepMedic and a UNet. We also provide an out-of-sample validation and clinical application of the approach on an additional 47 patients with disorders of consciousness. We make the code and trained networks freely available. Conclusions: Biomedical images follow imaging protocols that can be leveraged as prior information into deep convolutional neural networks to improve performance. The network segmentations match human manual corrections performed in 3D, and are comparable in performance to human segmentations obtained from scratch in 2D for abnormal brain anatomies.
翻译:目的: MRI 头部解剖的常规自动分解法根据图像强度和先前的组织概率图(TPM) 区分不同的大脑和非脑组织。 这对正常的头部解剖很有效,但因出现意外损伤而失败。 深层神经网络利用空间模式,可以学习分形损伤,但往往忽视先前的概率。 方法: 我们在三维变形网络中添加三个先前的信息来源, 即: 带有TPM的空间前端, 具有有条件随机字段的形态前端, 以及带有范围更广的2号观察场图像的空间背景。 我们对正常的头部解剖图进行良好的培训, 但有意外损伤。 结果是: 我们展示了以往每一种信息来源的好处, 并可以学习多端网络, 改进现有分解软件的性能, 如 SPM、 FSL 和 Deep Medic 用于异常解剖。 不同的前端网络的相关性是比较的, 深度解析网络中的深度解析和深度解析过程也是我们所了解的。 在通用网络中, 我们的解算和深度解算中, 能够进行更精确的解析的解算, 我们的解算和深度的机能能能可以提供一种功能的功能的功能的演化。