Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA.
Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA.
Epilepsia Open. 2021 Sep;6(3):493-503. doi: 10.1002/epi4.12499. Epub 2021 May 15.
Stereotactic electroencephalography (SEEG) has been widely used to explore the epileptic network and localize the epileptic zone in patients with medically intractable epilepsy. Accurate anatomical labeling of SEEG electrode contacts is critically important for correctly interpreting epileptic activity. We present a method for automatically assigning anatomical labels to SEEG electrode contacts using a 3D-segmented cortex and coregistered postoperative CT images.
Stereotactic electroencephalography electrode contacts were spatially localized relative to the brain volume using a standard clinical procedure. Each contact was then assigned an anatomical label by clinical epilepsy fellows. Separately, each contact was automatically labeled by coregistering the subject's MRI to the USCBrain atlas using the BrainSuite software and assigning labels from the atlas based on contact locations. The results of both labeling methods were then compared, and a subsequent vetting of the anatomical labels was performed by expert review.
Anatomical labeling agreement between the two methods for over 17 000 SEEG contacts was 82%. This agreement was consistent in patients with and without previous surgery (P = .852). Expert review of contacts in disagreement between the two methods resulted in agreement with the atlas based over manual labels in 48% of cases, agreement with manual over atlas-based labels in 36% of cases, and disagreement with both methods in 16% of cases. Labels deemed incorrect by the expert review were then categorized as either in a region directly adjacent to the correct label or as a gross error, revealing a lower likelihood of gross error from the automated method.
The method for semi-automated atlas-based anatomical labeling we describe here demonstrates potential to assist clinical workflow by reducing both analysis time and the likelihood of gross anatomical error. Additionally, it provides a convenient means of intersubject analysis by standardizing the anatomical labels applied to SEEG contact locations across subjects.
立体定向脑电图（SEEG）已广泛用于探索药物难治性癫痫患者的癫痫网络和致痫区。准确的 SEEG 电极触点解剖学标记对于正确解释癫痫活动至关重要。我们提出了一种使用 3D 分割皮质和配准术后 CT 图像自动为 SEEG 电极触点分配解剖学标签的方法。
使用标准临床程序，将 SEEG 电极触点相对于脑体积进行空间定位。然后，由临床癫痫研究员为每个触点分配解剖学标签。另外，通过使用 BrainSuite 软件将受试者的 MRI 与 USCBrain 图谱配准，并根据触点位置从图谱分配标签，分别自动为每个触点分配标签。然后比较两种标记方法的结果，并由专家对解剖学标签进行后续审查。
两种方法对超过 17000 个 SEEG 触点的解剖学标记的一致性为 82%。这种一致性在有和没有先前手术的患者中是一致的（P=0.852）。对两种方法不一致的触点进行专家审查后，48%的病例与图谱一致，36%的病例与基于手动的一致，16%的病例与两种方法均不一致。专家审查认为不正确的标签随后被归类为正确标签的直接相邻区域或为严重错误，表明自动方法发生严重错误的可能性较低。
我们描述的这种基于图谱的半自动解剖学标记方法具有通过减少分析时间和降低严重解剖学错误的可能性来辅助临床工作流程的潜力。此外，它通过在受试者之间标准化应用于 SEEG 触点位置的解剖学标签，为跨受试者的分析提供了一种方便的方法。
