摘要
肿瘤是常见的危害人类健康的恶性疾病之一。对于尚未广泛转移的肿瘤患者,目前普遍采用的是基于图像引导的放射疗法。这种疗法需要依据患者的断层图像来定位肿瘤边界和危及器官,并据此计算患者体内的放射剂量分布,制定放疗计划。
目前,医院在制定放疗计划时普遍采用手工勾画的方式来确定放疗靶区。医生需要逐层筛查病人的断层图像,并用专业的放疗计划软件勾画出所有图像序列中的肿瘤轮廓。在勾画过程中,医生既要保证病灶靶区的准确勾画,又要尽量规避正常组织和重要器官。然而,手工勾画存在诸多弊端：一方面,由于病人的图像往往多达上百个断层,肿瘤也可能转移至若干处,这直接导致医生的筛查过程非常耗时；另一方面,某些类型的肿瘤在图像中(例如,鼻咽部肿瘤的CT图像)边界并不明显,医生在勾画此类图像时,需要结合肿瘤生长造成的解剖结构变化来勾勒大致的肿瘤边缘。即使对于具有良好的解剖知识的临床医师而言也需要反复揣摩,仔细推敲。由此可见,手工勾画过程枯燥、困难耗时,并带有一定的主观性,重复性差。研究肿瘤图像的自动/半自动分割方法,为肿瘤靶区的勾画提供快捷、且重复性较高的方案,成为图像引导放疗技术领域的一个重要研究分支。
传统的医学图像分割方法有基于阈值和区域生长这类简单的图像分割方法,也有基于统计形状模型(ASM)和图割(Graph Cut)这类较复杂的分割方法。然而,这些传统的医学图像分割方法往往是基于个体的单模态图像。近些年来,多模态图像已经被广泛的引入到临床诊疗当中,这也为多模态图像分割方法的研究提供了应用基础和发展机遇。由于单模态图像能提供的诊疗信息非常有限,传统的简单分割方法难以满足高精度的临床需求,而多模态图像能够提供病人病灶及周边的更多信息,且不同模态的信息存在一定的互补作用,因此,利用多模态图像,理论上可以达成目标区域的更精确分割。
然而,即使采用多模态图像指导分割,肿瘤图像的分割依然具有挑战性。这是因为肿瘤产生和生长的病理过程不可预测,肿瘤形状无规律,边界不规则,甚至肿瘤内部出现灰度不均一的情况,同时也存在图像采集过程中引入噪声等因素。这些因素使得设计一套完全自动的分割方法非常困难。事实上,将某些的传统分割方法做一些改进,并依据实际情况组合应用是提高多模态图像分割精度的重要策略。本文选择基于多模态MR图像的脑肿瘤分割和基于PETtCT的鼻咽部肿瘤分割作为多模态图像分割研究的切入点,主要做了以下工作：
(1)对于多模态MR图像的脑肿瘤分割问题,提出了结合群体和个体特征的多模态MR图像分割方法。通过对多模态图像进行多尺度Gabor小波变换,以及不同模态间图像的运算,强化了多模态肿瘤图像在特征空间的表达。分别构建了群体和个体特征：将训练集所有肿瘤轮廓及周边组织分别作为正负样本；通过手工交互选取少量待分割图像中的正负样本。依据群体和个体正负样本提取图像特征,并将图像特征利用CFML方法映射到新的距离测度空间,在优化后的距离测度空间利用AdaBoost方法训练出分类器。对待分割图像的预测结果用于构造图割算法的代价函数,并求得最终分割结果。通过遍历群体和个体信息的权重,比较最终的分割结果就可以得到群体和个体信息最佳权重。结果表明,分割过程中同时引入多模态信息和群体特征能显著改善算法的鲁棒性。
(2)针对基于PET/CT图像的鼻咽部肿瘤分割问题,本文首先对FDG-PET/CT和Choline-PET/CT对鼻咽部肿瘤的显影效果进行了定性和定量的分析。结果显示相较于FDG-PET/CT, Choline-PET/CT对于鼻咽部肿瘤对肿瘤靶区勾画具有更明显的作用,特别是对于发生颅底侵犯的鼻咽部肿瘤。鉴于11C-Choline示踪剂制作复杂、成本较高,临床上普遍采用FDG-PET/CT作为鼻咽部肿瘤的一种常规检查方式,相应的图像数据也更容易获得。本文后续主要研究了基于FDG-PET/CT的多模态鼻咽部肿瘤图像联合分割。针对鼻咽部肿瘤出现颅底侵犯时,FDG-PET/CT图像的分割结果容易出现分割溢出的现象,提出了用位置分布图(Location Distribution Map)来约束分割结果的方法。分别利用支持向量机(SVM)和局部线性表达分类(LLRC)作为分割框架核心,将这个约束添加到多模态分割框架中,实验取得了一些正面的初步结果。后续工作(包括分割框架的完善和不同方法分割效果的评估)还有待进一步完善。
Cancer is a kind of common malignant and serious disease. Image guided radiotherapy is generally adopted for cancer patients without widely tumor metastasis. In this treatment, the cross-sectional images of patients are needed to locate the boundary of tumors and the organs at risk. With that, dosage distribution of the patient's body are calculated, and radiotherapy plan are formulated.
At present, the radiotherapy targets are often sketched manually in hospital when formulating a radiotherapy plan. Doctors have to check patients'cross-sectional images layer by layer and contour the boundary of the tumors in sequences of images by professional radiotherapy planning software. During the process of sketch, doctors should not only delineate the target accurately but also avoid normal tissues and vital organs are include in the target zone. However, this kind of process has many disadvantages:first, hundreds of cross-sectional images and the possibilities of tumor metastasis in several parts of human body directly indicate that, this is a time consuming and boring task. Then, some kinds of tumors don't show apparent boundries in images(for example, Nasopharyngeal tumor in CT images). Under these circumstances, anatomy changes caused by tumor growth are considered, and provide and important clue to approximate tumor boundry during doctors' contouring. Even doctors wiht good knowledge of anatomy have to think over and over again before reach a consensus with an abundance of caution. Therefore, the sketching process by hand is boring, difficult and time-consuming, and the contouring results are very subjective and low reproducible. The study of the methods for automatical or semi-automatical segmentation of tumor has became an important research topic in the field of the image-guided radiation therapy technology, and offered a solution to help doctors to delineat the tumor targets with the higher speed and repeatability.
There are many traditional methods to achive medical image segmentation, including simple methods, like threshold and region-grow, and complicate methods, like ASM (Active shape modle) and Graph Cut. However, these classical methods usually use single modality. In recent years, multi-modal images have been widly used in clinlical diagnoses, and this fact provide solid basis of the application/research and development opportunity for multi-modal image segmentation methods. Because single modal image can provide limited information about desease, traditional methods cannot yield accurate result to satisfy the clinical demands, but multi-modal images can provied more complementary information of the lesion and around tissues. Thus, theoretically, the segmentation results of the target area should be more accurate when using multi-modal images.
However, even with multimodal images, image segmentation is quite a challenging task because the pathological process responsible for the creation and growth of brain tumors is inherently unpredictable. Consequently, the geometric properties of the tumor do not conform to a particular shape/size distribution, which makes the tumor boundaries universally and irregularly distorted. Furthermore, the tumor is heterogeneous and the border is difficult to localize. Another aspect that complicates segmentation is that artifacts and noise can be easily interfused into images during data acquisition. These obstacles make it impossible to use any kind of shape prior on these normal structures to aid in tumor segmentation, and make it difficult to design a fully automatic segmentation method. It is actually a trend to adopt more than one of the methods mentioned above to complement the drawbacks in a single one. This paper will take the segmentation of multi-modal MR brain tumor images and bio-modal PET/CT NPC(Nasal Pharyngeal Cancer) images as entry points to discusse the work I have done:
1) This paper present a population-and patient-specific information based method for the segmentation of brain tumors in multimodal MR images. A Gabor filter bank is used to capture the texture properties. To enhance the multi-modal information, the image difference between the two modalities are also considered as a sub-feature. With these features, positive and negative samples are sampled in/outside the tumor automatically in the training set, and manually sampled in/outside the tumor. An optimal distance metric is learned, aimed at improving the discrimination in the feature space by employing Closed-Form Metric Learning. Furthermore, a AdaBoost classifier is introduced to estimate the probabilities of voxels belonging to the target and the background in the projected feature space. Based on this idea, a new cost function is constructed and optimized via Graph Cut. The optimum weight of population-and patient-specific information weight are obtained by traverse the weighting factor and compare the final segmentation results. The results of experiments show that use both population-and patient-specific information of multi-modal images can significantly improve the stability and accuracy of the algorithm.
2) To the question of NPC (Nasal Pharyngeal Cancer) segmentation using PET/CT images, this paper performed qualitative and quantitative analysis about the NPC enhancement effect of FDG-PET/CT and Choline-PET/CT. The evaluation results show that Choline-PET/CT may be superior to FDG-PET/CT for determing gross tumor volume in patients, especially the ones with locally advanced NPC. Since the preparation of tracer11C-Choline are more complicate and expensive than18F-FDG, FDG-PET/CT are widely used as clinical tumor tracer for NPC diagnosed, and the image data used for study are easier to obtain. The rest of the paper discussed the topic of bi-modality co-segmentation of NPC using FDG-PET/CT images. Contrapose the main problem of segmentation "lekage" existing in FDG-PET/CT segmentation, the current paper proposed a method to use location distribution map to restrain the segmentation result. Experiments of adding the restrain to the segmentation framework have been performed, including SVM and sparse representation as the core of segmentation framework. The encouraging evaluation results obtained. Follow-up work will concentrate on the consummation of the segmentation framework and the evaluation of the segmentation result of different methods.
引文
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