基于种子点不连续性的定量磁化率图中黑质和红核的分割方法。
Seed point discontinuity-based segmentation method for the substantia nigra and the red nucleus in quantitative susceptibility maps.
机构信息
Shanghai Key Laboratory of Magnetic Resonance and Department of Physics and Material Science, East China Normal University, Shanghai, China.
MR Collaboration NE Asia, Siemens Healthcare, Shanghai, China.
出版信息
J Magn Reson Imaging. 2018 Oct;48(4):1112-1119. doi: 10.1002/jmri.26023. Epub 2018 Mar 31.
BACKGROUND
The automatic segmentation of cerebral nuclei in the quantitative susceptibility mapping (QSM) images can provide assistance for surgical treatment and pathological mechanism studies. However, as the most frequently used segmentation method, the atlas method provides unsatisfactory results when segmenting the substantia nigra (SN) and the red nucleus (RN).
PURPOSE
To propose and evaluate an improved automatic method based on seed points-discontinuity for segmentations of the SN and the RN in QSM images.
STUDY TYPE
Prospective.
SUBJECTS
In all, 22 subjects, 11 patients with Parkinson's disease (PD), and 11 healthy subjects (mean age of 68.0 ± 6.9 years) underwent MR scans.
FIELD STRENGTH/SEQUENCE: 3T system and a 3D multiecho gradient echo sequence with monopolar readout gradient.
ASSESSMENT
Manual segmentations by two radiologists (both with over 10 years of experience in neuroimaging) were used to establish a baseline for assessment. The Dice coefficient and the center-of-gravity distance was employed to evaluate the segmentation accuracy.
STATISTICAL TESTS
The mean value and standard deviation of the Dice coefficient and center-of-gravity distance were calculated separately to compare segmentation results from the proposed method, the level set method, the atlas method (including the single-atlas method and the multi-atlas majority voting method).
RESULTS
The statistical results of Dice coefficient of the SN and the RN between the ground truth and the segmentation were 0.79 ± 0.14 and 0.77 ± 0.06 for the proposed method, 0.40 ± 0.10 and 0.65 ± 0.09 for the level set method, 0.68 ± 0.09 and 0.64 ± 0.07 for the single-atlas method, 0.70 ± 0.06 and 0.68 ± 0.05 for the multi-atlas majority voting method, respectively. The proposed method also provides the lowest center-of-gravity distance value (1.05 ± 0.71 for the SN and 0.74 ± 0.35 for the RN).
DATA CONCLUSION
The segmentation results of the proposed method performed well on the in vivo data and were closer to the manual segmentation than the atlas method.
LEVEL OF EVIDENCE
1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;48:1112-1119.
背景
在定量磁化率映射（QSM）图像中自动分割脑核可以为手术治疗和病理机制研究提供帮助。然而，作为最常用的分割方法，图谱法在分割黑质（SN）和红核（RN）时效果并不理想。
目的
提出并评估一种基于种子点-不连续的改进自动方法，用于 QSM 图像中 SN 和 RN 的分割。
研究类型
前瞻性。
受试者
共 22 例受试者，其中 11 例帕金森病（PD）患者和 11 例健康对照者（平均年龄 68.0±6.9 岁）接受了磁共振扫描。
磁场强度/序列：3T 系统和具有单极读出梯度的 3D 多回波梯度回波序列。
评估
两名具有 10 年以上神经影像学经验的放射科医生进行手动分割，作为评估的基线。采用 Dice 系数和重心距离评估分割准确性。
统计学检验
分别计算 Dice 系数和重心距离的平均值和标准差，以比较所提出方法、水平集方法、图谱方法（包括单图谱方法和多图谱多数投票方法）的分割结果。
结果
SN 和 RN 的 SN 和 RN 的 Dice 系数和重心距离的统计结果分别为所提出方法的 0.79±0.14 和 0.77±0.06，水平集方法的 0.40±0.10 和 0.65±0.09，单图谱方法的 0.68±0.09 和 0.64±0.07，多图谱多数投票方法的 0.70±0.06 和 0.68±0.05。所提出的方法还提供了最低的重心距离值（SN 为 1.05±0.71，RN 为 0.74±0.35）。
结论
所提出的方法在体内数据上的分割结果良好，与手动分割比图谱方法更接近。
证据水平
1 技术功效：阶段 1 J. Magn. Reson. Imaging 2018;48:1112-1119.
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