Alvarez Charlens, Martínez Fabio, Romero Eduardo
Computer Imaging and Medical Application Laboratory-CIM@LAB, Universidad Nacional de Colombia, Bogotá, Colombia.
Escuela de Ingeniería de Sistemas e Informática, Universidad Industrial de Santander UIS, Bucaramanga, Colombia.
Med Phys. 2017 Apr;44(4):1312-1323. doi: 10.1002/mp.12141.
Accurate prostate delineation is necessary in radiotherapy processes for concentrating the dose onto the prostate and reducing side effects in neighboring organs. Currently, manual delineation is performed over magnetic resonance imaging (MRI) taking advantage of its high soft tissue contrast property. Nevertheless, as human intervention is a consuming task with high intra- and interobserver variability rates, (semi)-automatic organ delineation tools have emerged to cope with these challenges, reducing the time spent for these tasks. This work presents a multiresolution representation that defines a novel metric and allows to segment a new prostate by combining a set of most similar prostates in a dataset.
The proposed method starts by selecting the set of most similar prostates with respect to a new one using the proposed multiresolution representation. This representation characterizes the prostate through a set of salient points, extracted from a region of interest (ROI) that encloses the organ and refined using structural information, allowing to capture main relevant features of the organ boundary. Afterward, the new prostate is automatically segmented by combining the nonrigidly registered expert delineations associated to the previous selected similar prostates using a weighted patch-based strategy. Finally, the prostate contour is smoothed based on morphological operations.
The proposed approach was evaluated with respect to the expert manual segmentation under a leave-one-out scheme using two public datasets, obtaining averaged Dice coefficients of 82% ± 0.07 and 83% ± 0.06, and demonstrating a competitive performance with respect to atlas-based state-of-the-art methods.
The proposed multiresolution representation provides a feature space that follows a local salient point criteria and a global rule of the spatial configuration among these points to find out the most similar prostates. This strategy suggests an easy adaptation in the clinical routine, as supporting tool for annotation.
在放射治疗过程中，准确勾勒前列腺轮廓对于将剂量集中在前列腺上并减少邻近器官的副作用至关重要。目前，利用磁共振成像（MRI）的高软组织对比度特性进行手动勾勒。然而，由于人工干预是一项耗时的任务，且观察者内和观察者间的变异性较高，（半）自动器官勾勒工具应运而生，以应对这些挑战，减少完成这些任务所需的时间。这项工作提出了一种多分辨率表示方法，该方法定义了一种新的度量标准，并允许通过组合数据集中一组最相似的前列腺来分割新的前列腺。
所提出的方法首先使用所提出的多分辨率表示法选择与新前列腺最相似的一组前列腺。这种表示法通过从包围该器官的感兴趣区域（ROI）中提取的一组显著点来表征前列腺，并利用结构信息进行细化，从而能够捕捉器官边界的主要相关特征。之后，使用基于加权补丁的策略，通过组合与先前选定的相似前列腺相关的非刚性配准专家勾勒结果，自动分割新的前列腺。最后，基于形态学操作对前列腺轮廓进行平滑处理。
在留一法方案下，使用两个公共数据集对所提出的方法与专家手动分割进行了评估，获得的平均骰子系数分别为82%±0.07和83%±0.06，并且相对于基于图谱的现有方法表现出了有竞争力的性能。
所提出的多分辨率表示法提供了一个特征空间，该空间遵循局部显著点标准以及这些点之间空间配置的全局规则，以找出最相似的前列腺。作为一种注释支持工具，该策略表明其易于在临床常规中应用。
