Biomedical Image Computing Group, School of Computer Science and Engineering, University of New South Wales (UNSW), Sydney, NSW, Australia.
Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
Sci Rep. 2023 Jan 28;13(1):1617. doi: 10.1038/s41598-023-28210-1.
Segmentation of white matter tracts in diffusion magnetic resonance images is an important first step in many imaging studies of the brain in health and disease. Similar to medical image segmentation in general, a popular approach to white matter tract segmentation is to use U-Net based artificial neural network architectures. Despite many suggested improvements to the U-Net architecture in recent years, there is a lack of systematic comparison of architectural variants for white matter tract segmentation. In this paper, we evaluate multiple U-Net based architectures specifically for this purpose. We compare the results of these networks to those achieved by our own various architecture changes, as well as to new U-Net architectures designed automatically via neural architecture search (NAS). To the best of our knowledge, this is the first study to systematically compare multiple U-Net based architectures for white matter tract segmentation, and the first to use NAS. We find that the recently proposed medical imaging segmentation network UNet3+ slightly outperforms the current state of the art for white matter tract segmentation, and achieves a notably better mean Dice score for segmentation of the fornix (+ 0.01 and + 0.006 mean Dice increase for left and right fornix respectively), a tract that the current state of the art model struggles to segment. UNet3+ also outperforms the current state of the art when little training data is available. Additionally, manual architecture search found that a minor segmentation improvement is observed when an additional, deeper layer is added to the U-shape of UNet3+. However, all networks, including those designed via NAS, achieve similar results, suggesting that there may be benefit in exploring networks that deviate from the general U-Net paradigm.
在健康和疾病的大脑成像研究中，弥散磁共振图像中的白质束分割是一个重要的第一步。与一般的医学图像分割类似，一种流行的白质束分割方法是使用基于 U-Net 的人工神经网络架构。尽管近年来对 U-Net 架构提出了许多改进，但对于白质束分割的架构变体缺乏系统的比较。在本文中，我们专门针对这一目的评估了多种基于 U-Net 的架构。我们将这些网络的结果与我们自己的各种架构变化以及通过神经架构搜索 (NAS) 自动设计的新 U-Net 架构的结果进行了比较。据我们所知，这是第一项系统地比较用于白质束分割的多种基于 U-Net 的架构的研究，也是第一项使用 NAS 的研究。我们发现，最近提出的医学成像分割网络 UNet3+在白质束分割方面略优于当前的最先进水平，并且在分割穹窿（左右穹窿的平均 Dice 得分分别提高了+0.01 和+0.006）方面表现出色，当前最先进的模型在分割该束方面存在困难。当可用的训练数据很少时，UNet3+也优于当前的最先进水平。此外，手动架构搜索发现，当在 UNet3+的 U 形中添加一个额外的更深层时，可以观察到轻微的分割改进。然而，所有的网络，包括那些通过 NAS 设计的网络，都取得了相似的结果，这表明探索偏离一般 U-Net 范式的网络可能会有好处。
