A logarithmic opinion pool based STAPLE algorithm for the fusion of segmentations with associated reliability weights.
A logarithmic opinion pool based STAPLE algorithm for the fusion of segmentations with associated reliability weights.
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DOI：
10.1109/tmi.2014.2329603
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发表时间：
2014-10
影响因子：
10.6
通讯作者：
Warfield SK
中科院分区：
文献类型：
--
作者：
Akhondi-Asl A;Hoyte L;Lockhart ME;Warfield SK
盆底功能障碍在女性产后非常常见，盆底磁共振图像（MRI）的精确分割可能有助于患者的诊断和治疗。然而，由于盆底结构的复杂性，盆底的手动分割具有挑战性，并且受到专家评估者的评估者之间和评估者内部的高度变异性的影响。在这些类型的应用中，多模板融合算法是用于 MRI 分割的有前途的技术，但这些算法受到每个模板与目标对齐的缺陷以及模板分割错误的限制。在此类分割技术中，模板集合与目标对齐，并推断出目标的新分割。许多算法试图通过将图像强度和模板标签作为两个独立的信息源结合起来，通过局部强度加权投票方案进行决策融合来提高分割性能。此类方法是线性意见池的一种形式，并且对该应用程序实现的性能并不令人满意。我们假设，通过评估每个模板与目标图像的参考标准分割相比的贡献，可以实现更好的决策融合，并开发了一种新颖的分割算法，以实现女性盆底 MRI 的自动分割。该算法通过估计和补偿模板与目标图像的不完美配准以及模板分割的不准确性来实现高性能。该算法是 STAPLE 算法的推广，其中估计参考分割并用于推断模板融合的最佳权重。局部图像相似性度量用于推断局部可靠性权重，这有助于通过新颖的对数意见池进行融合。我们将我们的新算法与九种最先进的分割方法进行比较，并与从每个主题图像的重复手动分割得出的参考标准进行比较，并证明我们的算法实现了最高性能。这种自动分割算法有望在未来对女性盆底进行广泛的评估，以进行诊断和预后。
Pelvic floor dysfunction is very common in women after childbirth and precise segmentation of magnetic resonance images (MRI) of the pelvic floor may facilitate diagnosis and treatment of patients. However, because of the complexity of the structures of pelvic floor, manual segmentation of the pelvic floor is challenging and suffers from high inter and intra-rater variability of expert raters. Multiple template fusion algorithms are promising techniques for segmentation of MRI in these types of applications, but these algorithms have been limited by imperfections in the alignment of each template to the target, and by template segmentation errors. In this class of segmentation techniques, a collection of templates is aligned to a target, and a new segmentation of the target is inferred. A number of algorithms sought to improve segmentation performance by combining image intensities and template labels as two independent sources of information, carrying out decision fusion through local intensity weighted voting schemes. This class of approach is a form of linear opinion pooling, and achieves unsatisfactory performance for this application. We hypothesized that better decision fusion could be achieved by assessing the contribution of each template in comparison to a reference standard segmentation of the target image and developed a novel segmentation algorithm to enable automatic segmentation of MRI of the female pelvic floor. The algorithm achieves high performance by estimating and compensating for both imperfect registration of the templates to the target image and template segmentation inaccuracies. The algorithm is a generalization of the STAPLE algorithm in which a reference segmentation is estimated and used to infer an optimal weighting for fusion of templates. A local image similarity measure is used to infer a local reliability weight, which contributes to the fusion through a novel logarithmic opinion pooling. We evaluated our new algorithm in comparison to nine state-of-the-art segmentation methods by comparison to a reference standard derived from repeated manual segmentations of each subject image and demonstrate our algorithm achieves the highest performance. This automated segmentation algorithm is expected to enable widespread evaluation of the female pelvic floor for diagnosis and prognosis in the future.