Unsupervised boundary delineation of spinal neural foramina using a multi-feature and adaptive spectral segmentation
Unsupervised boundary delineation of spinal neural foramina using a multi-feature and adaptive spectral segmentation
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DOI：
10.1016/j.media.2016.10.009
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发表时间：
2017-02-01
影响因子：
10.9
通讯作者：
Li, Shuo
中科院分区：
文献类型：
--
作者：
He, Xiaoxu;Zhang, Heye;Li, Shuo
神经孔骨狭窄症（neural foramina stenosis， NFS）是老年人的常见病，其症状包括疼痛、残疾、跌倒风险和抑郁等，给生活质量带来了显著的负面影响。准确的边界划定对NFS的临床诊断和治疗至关重要。然而，现有的临床常规由于需要医生大量的手工描绘，因而极其繁琐和低效。由于神经孔图像的复杂性和可变性，自动描述是非常必要的，但面临着很大的挑战。在本文中，我们提出了一个纯图像驱动的无监督边界描绘框架，用于自动神经孔边界描绘。该框架基于一种新的多特征自适应光谱分割（MFASS）算法。MFASS首先利用区域特征和边缘特征相结合，生成可靠的、神经孔与周围环境分离良好的光谱特征，然后对每张图像估计一个最优分离阈值，将神经孔与周围环境分离开来。这种自调整的最佳分离阈值，从光谱特征估计，成功地克服了不同的外观和形状的变化。该框架具有多特征融合的鲁棒性和自适应最优分离阈值估计的灵活性，能够实现自动准确的边界划分。对来自56名临床受试者的280张神经孔MR图像进行了验证。我们的方法以经验丰富的医生手工边界为基准。结果表明，该方法与经验丰富的医生具有高度稳定的一致性（Dice: 90.58% +/- 2.79%; SMAD: 0.5657 +/- 0.1544 mm）。因此，所提出的框架可以成为诊断神经孔狭窄的有效和准确的临床工具。(C) 2016 Elsevier B.V.版权所有
As a common disease in the elderly, neural foramina stenosis (NFS) brings a significantly negative impact on the quality of life due to its symptoms including pain, disability, fall risk and depression. Accurate boundary delineation is essential to the clinical diagnosis and treatment of NFS. However, existing clinical routine is extremely tedious and inefficient due to the requirement of physicians' intensively manual delineation. Automated delineation is highly needed but faces big challenges from the complexity and variability in neural foramina images. In this paper, we propose a pure image-driven unsupervised boundary delineation framework for the automated neural foramina boundary delineation. This framework is based on a novel multi-feature and adaptive spectral segmentation (MFASS) algorithm. MFASS firstly utilizes the combination of region and edge features to generate reliable spectral features with a good separation between neural foramina and its surroundings, then estimates an optimal separation threshold for each individual image to separate neural foramina from its surroundings. This self-adjusted optimal separation threshold, estimated from spectral features, successfully overcome the diverse appearance and shape variations. With the robustness from the multi-feature fusion and the flexibility from the adaptively optimal separation threshold estimation, the proposed framework, based on MFASS, provides an automated and accurate boundary delineation. Validation was performed in 280 neural foramina MR images from 56 clinical subjects. Our method was benchmarked with manual boundary obtained by experienced physicians. Results demonstrate that the proposed method enjoys a high and stable consistency with experienced physicians (Dice: 90.58% +/- 2.79%; SMAD: 0.5657 +/- 0.1544 mm). Therefore, the proposed framework enables an efficient and accurate clinical tool in the diagnosis of neural foramina stenosis. (C) 2016 Elsevier B.V. All rights reserved.