VASCage-Research Centre on Vascular Ageing and Stroke, Innsbruck, Austria; Department of Applied Mathematics, University of Innsbruck, Austria.
Department of Neuroradiology, Medical University of Innsbruck, Austria; Neuroimaging Research Core Facility, Medical University of Innsbruck, Austria.
Artif Intell Med. 2022 Oct;132:102384. doi: 10.1016/j.artmed.2022.102384. Epub 2022 Aug 24.
Segmentation of specific brain tissue from MRI volumes is of great significance for brain disease diagnosis, progression assessment, and monitoring of neurological conditions. Manual segmentation is time-consuming, laborious, and subjective, which significantly amplifies the need for automated processes. Over the last decades, the active development in the field of deep learning, especially convolutional neural networks (CNNs), and the associated performance improvements have increased the demand for the application of CNN-based methods to provide consistent measurements and quantitative analyses. In this paper, we present an efficient deep learning approach for the segmentation of brain tissue. More specifically, we address the problem of segmentation of the posterior limb of the internal capsule (PLIC) in preterm neonates. To this end, we propose a CNN-based pipeline comprised of slice-selection modules and a multi-view segmentation model, which exploits the 3D information contained in the MRI volumes to improve segmentation performance. One special feature of the proposed method is its ability to identify one desired slice out of the whole image volume, which is relevant for pediatricians in terms of prognosis. To increase computational efficiency, we apply a strategy that automatically reduces the information contained in the MRI volumes to its relevant parts. Finally, we conduct an expert rating alongside standard evaluation metrics, such as dice score, to evaluate the performance of the proposed framework. We demonstrate the benefit of the multi-view technique by comparing it with its single-view counterparts, which reveals that the proposed method strikes a good balance between exploiting the available image information and reducing the required computing power compared to 3D segmentation networks. Standard evaluation metrics as, well as expert-based assessment, confirm the good performance of the proposed framework, with the latter being more relevant in terms of clinical applicability. We demonstrate that the proposed deep learning pipeline can compete with the experts in terms of accuracy. To prove the generalisability of the proposed method, we additionally assess our deep learning pipeline to data from the Developing Human Connectome Project (dHCP).
从 MRI 体素中分割特定的脑组织对于脑疾病诊断、进展评估和神经状况监测具有重要意义。手动分割既耗时、费力又主观，这大大增加了对自动化流程的需求。在过去几十年中，深度学习领域，特别是卷积神经网络（CNN）的快速发展，以及相关性能的提高，增加了对应用基于 CNN 的方法的需求，以提供一致的测量和定量分析。在本文中，我们提出了一种高效的深度学习方法来分割脑组织。更具体地说，我们解决了早产儿内囊后肢（PLIC）分割的问题。为此，我们提出了一个基于 CNN 的流水线，包括切片选择模块和多视图分割模型，该模型利用 MRI 体素中包含的 3D 信息来提高分割性能。该方法的一个特殊特点是它能够从整个图像体积中识别出所需的一个切片，这对于儿科医生的预后具有重要意义。为了提高计算效率，我们应用了一种策略，自动将 MRI 体素中的信息减少到其相关部分。最后，我们进行了专家评估以及标准评估指标（如骰子分数），以评估所提出框架的性能。我们通过将多视图技术与单视图技术进行比较来证明多视图技术的优势，这表明与 3D 分割网络相比，该方法在利用可用图像信息和减少所需计算能力方面取得了很好的平衡。标准评估指标以及基于专家的评估都证实了所提出框架的良好性能，后者在临床应用方面更为相关。我们证明了所提出的深度学习流水线在准确性方面可以与专家相媲美。为了证明所提出方法的通用性，我们还将我们的深度学习流水线应用于发育人类连接组计划（dHCP）的数据进行评估。
