Given the complex geometry of white matter streamlines, Autoencoders have been proposed as a dimension-reduction tool to simplify the analysis streamlines in a low-dimensional latent spaces. However, despite these recent successes, the majority of encoder architectures only perform dimension reduction on single streamlines as opposed to a full bundle of streamlines. This is a severe limitation of the encoder architecture that completely disregards the global geometric structure of streamlines at the expense of individual fibers. Moreover, the latent space may not be well structured which leads to doubt into their interpretability. In this paper we propose a novel Differentiable Vector Quantized Variational Autoencoder, which are engineered to ingest entire bundles of streamlines as single data-point and provides reliable trustworthy encodings that can then be later used to analyze streamlines in the latent space. Comparisons with several state of the art Autoencoders demonstrate superior performance in both encoding and synthesis.

翻译：鉴于白质纤维束的复杂几何结构，自编码器被提出作为一种降维工具，以简化在低维潜在空间中分析纤维束的任务。然而，尽管近期取得了成功，大多数编码器架构仅对单条纤维束进行降维，而非完整的纤维束集合。这是编码器架构的一个严重局限，它完全忽视了纤维束的全局几何结构，仅关注单个纤维。此外，潜在空间可能结构不佳，导致对其可解释性存疑。本文提出一种新型可微分向量量化变分自编码器（Differentiable Vector Quantized Variational Autoencoder），该模型能够将完整纤维束集合作为单个数据点输入，并提供可靠可信的编码，可用于后续在潜在空间中分析纤维束。与多种现有最先进自编码器的比较表明，该模型在编码与合成方面均表现更优。