Fast and accurate segmentation of neonatal brain images is highly desired to better understand and detect changes during development and disease. Yet, the limited availability of ground truth datasets, lack of standardized acquisition protocols, and wide variations of head positioning pose challenges for method development. A few automated image analysis pipelines exist for newborn brain MRI segmentation, but they often rely on time-consuming procedures and require resampling to a common resolution, subject to loss of information due to interpolation and down-sampling. Without registration and image resampling, variations with respect to head positions and voxel resolutions have to be addressed differently. In deep-learning, external augmentations are traditionally used to artificially expand the representation of spatial variability, increasing the training dataset size and robustness. However, these transformations in the image space still require resampling, reducing accuracy specifically in the context of label interpolation. We recently introduced the concept of resolution-independence with the Voxel-size Independent Neural Network framework, VINN. Here, we extend this concept by additionally shifting all rigid-transforms into the network architecture with a four degree of freedom (4-DOF) transform module, enabling resolution-aware internal augmentations (VINNA). In this work we show that VINNA (i) significantly outperforms state-of-the-art external augmentation approaches, (ii) effectively addresses the head variations present specifically in newborn datasets, and (iii) retains high segmentation accuracy across a range of resolutions (0.5-1.0 mm). The 4-DOF transform module is a powerful, general approach to implement spatial augmentation without requiring image or label interpolation. The specific network application to newborns will be made publicly available as VINNA4neonates.

翻译：快速准确地分割新生儿脑部图像对于更好地理解和检测发育及疾病过程中的变化具有重要价值。然而，真值数据集有限、标准化采集协议缺乏以及头部定位的广泛变异对方法开发构成了挑战。目前存在少量用于新生儿脑MRI分割的自动化图像分析流程，但这些流程通常依赖耗时的处理步骤，需要将图像重采样至统一分辨率，并因插值和降采样导致信息损失。在不进行配准和图像重采样的情况下，需通过其他方式处理头部位置和体素分辨率的变异。在深度学习中，传统上采用外部增广方法人为扩展空间变异的表征，增加训练数据集规模和模型鲁棒性。然而，这些图像空间的变换仍需重采样操作，尤其在标签插值环节会降低精度。我们近期提出了基于体素大小无关神经网络框架VINN的分辨率无关概念。本研究通过将所有刚性变换迁移至网络架构中，结合四自由度（4-DOF）变换模块，进一步扩展这一概念，从而实现了分辨率感知的内部增广（VINNA）。本研究表明，VINNA能够：（i）显著优于最先进的外部增广方法；（ii）有效应对新生儿数据集中特有的头部方位变化；（iii）在0.5-1.0毫米分辨率范围内保持高分割精度。4-DOF变换模块提供了一种无需图像或标签插值的通用空间增广方法。面向新生儿群体的特定网络应用将以VINNA4neonates形式公开提供。