Sarubbo Silvio, Vavassori Laura, Zigiotto Luca, Corsini Francesco, Annicchiarico Luciano, Rozzanigo Umberto, Avesani Paolo
Department of Neurosurgery, "S. Chiara" University-Hospital, Azienda Provinciale per i Servizi Sanitari, 39122 Trento, Italy.
Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole, 101, Mattarello, 38123 Trento, Italy.
Brain Sci. 2024 Dec 7;14(12):1232. doi: 10.3390/brainsci14121232.
In glioma surgery, maximizing the extent of resection while preserving cognitive functions requires an understanding of the unique architecture of the white matter (WM) pathways of the single patient and of their spatial relationship with the tumor. Tractography enables the reconstruction of WM pathways, and bundle segmentation allows the identification of critical connections for functional preservation. This study evaluates the effectiveness of a streamline-based approach for bundle segmentation on a clinical dataset as compared to the traditional ROI-based approach. We performed bundle segmentation of the arcuate fasciculus, of its indirect anterior and posterior segments, and of the inferior fronto-occipital fasciculus in the healthy hemisphere of 25 high-grade glioma patients using both ROI- and streamline-based approaches. ROI-based segmentation involved manually delineating ROIs on MR anatomical images in Trackvis (V0.6.2.1). Streamline-based segmentations were performed in Tractome, which integrates clustering algorithms with the visual inspection and manipulation of streamlines. Shape analysis was conducted on each bundle. A paired -test was performed on the irregularity measurement to compare segmentations achieved with the two approaches. Qualitative differences were evaluated through visual inspection. Streamline-based segmentation consistently yielded significantly lower irregularity scores ( < 0.001) compared to ROI-based segmentation for all the examined bundles, indicating more compact and accurate bundle reconstructions. Qualitative assessment identified common biases in ROI-based segmentations, such as the inclusion of anatomically implausible streamlines or streamlines with undesired trajectories. Streamline-based bundle segmentation with Tractome provides reliable and more accurate reconstructions compared to the ROI-based approach. By directly manipulating streamlines rather than relying on voxel-based ROI delineations, Tractome allows us to discern and discard implausible or undesired streamlines and to identify the course of WM bundles even when the anatomy is distorted by the lesion. These features make Tractome a robust tool for bundle segmentation in clinical contexts.
在胶质瘤手术中，在保留认知功能的同时最大化切除范围需要了解单个患者白质（WM）通路的独特结构及其与肿瘤的空间关系。纤维束成像能够重建WM通路，而束分割则有助于识别对功能保留至关重要的连接。本研究评估了一种基于流线的束分割方法与传统的基于感兴趣区域（ROI）的方法相比，在临床数据集上的有效性。我们使用基于ROI和基于流线的方法，对25例高级别胶质瘤患者健康半球的弓形束、其间接的前、后段以及额枕下束进行了束分割。基于ROI的分割涉及在Trackvis（V0.6.2.1）中的MR解剖图像上手动勾勒ROI。基于流线的分割在Tractome中进行，Tractome将聚类算法与流线的视觉检查和操作相结合。对每个束进行形状分析。对不规则性测量进行配对检验，以比较两种方法实现的分割。通过视觉检查评估定性差异。与基于ROI的分割相比，基于流线的分割在所有检查的束中始终产生显著更低的不规则性分数（<0.001），表明束重建更紧凑、准确。定性评估确定了基于ROI的分割中常见的偏差，例如包含解剖学上不合理的流线或具有不期望轨迹的流线。与基于ROI的方法相比，使用Tractome进行基于流线的束分割可提供更可靠、准确的重建。通过直接操作流线而不是依赖基于体素的ROI勾勒，Tractome使我们能够辨别和舍弃不合理或不期望的流线，即使在解剖结构因病变而扭曲时也能识别WM束的走向。这些特性使Tractome成为临床环境中束分割的强大工具。
