1、滤泡淋巴瘤分级Histopathological Image Analysis Using Model-BasedIntermediate Representations and Color Texture:Follicular Lymphoma Grading滤泡淋巴瘤分级使用基于中间表示法模型的和颜色纹理信息分析组织病理图像Olcay Sertel Jun Kong Umit V. Catalyurek Gerard Lozanski Joel H. Saltz Metin N. GurcanAbstract Follicular lymphoma (FL) is a cancer of
2、lymph system and it is the second most common lymphoid malignancy in the western world. Currently, the risk stratification of FL relies on histological grading method, where pathologists evaluate hematoxilin and eosin (H&E) stained tissue sections under a microscope as recommended by the World Healt
3、h Organization.This manual method requires intensive labor in nature. Due to the sampling bias, it also suffers from inter- and intra-reader variability and poor reproducibility. We are developing a computer-assisted system to provide quantitative assessment of FL images for more consistent evaluati
4、on of FL. In this study, we proposed a statistical framework to classify FL images based on their histological grades. We introduced model-based intermediate representation (MBIR) of cytological components that enables higher level semantic description of tissue characteristics. Moreover, we introdu
5、ced a novel color-texture analysis approach that combines the MBIR with low level texture features, which capture tissue characteristics at pixel level. Experimental results on real follicular lymphoma images demonstrate that the combined feature space improved the accuracy of the system significant
6、ly. The implemented system can identify the most aggressive FL (grade III) with 98.9% sensitivity and 98.7% specificity and the overall classification accuracy of the system is 85.5%.摘要滤泡淋巴瘤(FL)是一种淋巴系统癌症,它是西方世界排名第二的恶性淋巴肿瘤。目前，FL的恶性分级依赖于组织病理图像。目前，FL的恶性分级依赖于组织学分级方法，由世界卫生组织建议病理学家在显微镜下评估由苏木精-伊红染色法（HE）染色的
7、组织切片。这种人工方法需要很大的精力。由于抽样误差，它也受到来自医生间和医生本身的差异和不可重复性的约束。我们正在开发的电脑辅助系统对FL图像进行定量评估以便提供更一致的FL评价。在这项研究中，我们提出了一个统计框架，根据其组织学分级分类FL图像。我们推出了基于模型的中间表示（MBIR）的细胞学部件，实现了更高层次的语义描述的组织特性。此外，我们引入了一个捕捉像素级的组织特征的新的彩色纹理分析方法，它结合了MBIR和低级别的纹理特征。在真正的滤泡性淋巴瘤图像上的实验结果表明，该组合的特征空间上显着改善了系统的精度。实现的系统可以识别的恶性的FL（III级），灵敏度为98.9和特异度为98.7，
8、系统的总分类精度为85.5。Keywords Histopathological image analysis Model-based intermediate representation Color texture analysis Follicular lymphoma关键词：病理组织学图像分析；基于模型的中间表示；彩色纹理分析；滤泡性淋巴瘤。1 Introduction1 简介Follicular lymphoma (FL) is a cancer of lymph system and it is the second most common lymphoid malignancy
9、in the western world. FL is a mature B lymphocyte malignancy of follicular center cell origin. Diagnosis of FL is based on specific morphologic, immunophenotypic and cytogenetic findings in lymph node/tissue biopsy specimens.滤泡性淋巴瘤（FL）是一种淋巴系统的癌症，这是西方世界排名第二的恶性淋巴肿瘤。佛罗里达州是一个成熟的B淋巴细胞恶性的滤泡中心细胞来源。诊断FL是基于特
10、定淋巴结/组织活检标本形态学，免疫表型和细胞遗传学的研究结果。 FL has a highly variable clinical course ranging from an indolent to a highly aggressive disease. Patients withindolent disease often live for many decades and may never require therapy, while the patients with aggressive FL have short survival if not treated early wi
11、th aggressive chemotherapy. It is important to note that in contrast to aggressive FL, the indolent FL patients do not benefit from early chemotherapy and that treatment should be avoided in these patients to prevent serious side effects. This variable clinical presentation requires an accurate risk
12、 stratification of FL samples as a guidance for oncologist in making decisions on timing and type of therapy.As a result, it can contribute to reducing the likelihood of making under and over treatments.FL是一种具有高度可变从一个懒惰的一个极具攻击性的临床过程的疾病。良性FL疾病经常潜伏几十年或者可能从不需要治疗,而恶性FL患者不及早治疗与化疗，则会很快夺去性命。所以，正确区别恶性FL和良性F
13、L就非常关键，早期化疗对良性FL病人没有好处，所以良性FL病人应避免这些治疗以防止产生哪些严重的副作用。这个变化的临床过程需要一个准确的FL恶性分级以便为肿瘤医生们提供一个依据去决对治疗的时间、类型和方法。所以，它有助于减少做出错误疗法的可能性。Currently, the most commonly used FL risk stratification method is histological grading (HG) system adopted by the World Health Organization 1. The HG method is based on average
14、 count of large malignant cells called centroblasts (CB) per standard microscopic high power field (HPF) defined as 0.159 mm2. Follicular lymphoma cases are stratified into three histological grades: Grade I (05 CB/HPF), grade II (615 CB/HPF) and grade III(15CB/HPF). Grades I and II are considered l
15、ow risk category while grade III is considered high risk category. In this method the average centroblast count per HPF is based on CB count in ten random HPFs representing malignant follicles.The CB count is performed manually by the pathologistusing an optical microscope and hematoxilin and eosin
16、(H&E) stained tissue section(s). Since this is a highly subjective method, the results show well documented inter- and intra-observer variability 2, 3 for the various grades of FL even among the experts 4. Moreover,since this method, for practical reasons, uses only ten high power fields for CB coun
17、t, the results for specimens with high tumor heterogeneity are vulnerable to sampling bias. This poor reliability and reproducibility of FL histological grading may lead to inappropriate clinical decisions on timing and type of therapy and result in under or over treatment for the individual FL pati
18、ent with many serious clinical consequences. Using computerized image analysis, it is possible to extract more objective and accurate prognostic clues, which may not be easily observed by qualitative analysis performed by pathologists. Besides, instead of evaluating only representative regions, a co
19、mputerized system can process the whole-slide and prevent the sampling bias.目前，最常用的FL危险分层的方法是组织学分级系统（HG）通过世界卫生组织1。 HG方法是基于大的恶性细胞称为平均计数每标准微观高倍视野（HPF）中心母细胞（CB）定义为0.159平方毫米。滤泡性淋巴瘤病例分为三种组织学分级：级（0-5 CB / HPF），级（6-15 CB / HPF）和级（15CB/HPF）。 I和II级被认为是低风险类别，而级被认为是高风险类别。在该方法中，每HPF的的平均centroblast计数是根据代表恶性卵泡CB计
20、数在10随机住房公积金CB计数是手动执行的由光学显微镜和苏木精-伊红染色法（HE）染色的组织切片（）。由于这是一个非常主观的方法，结果表明有据可查间和观察者内的变异2，3，甚至专家之间的各种档次的FL4。此外，由于这种方法中，由于实际原因，只使用了10个高倍视野CB数为肿瘤的异质性高的标本，结果很容易受到抽样误差。这个可怜的可靠性和可重复性的FL组织学分级可能会导致不适当的临床治疗，并导致了许多严重的临床后果的个人FL患者治疗过度或不足，时间和类型的决定。利用计算机图像分析技术，它可以提取更客观，更准确的预后线索，这可能不是很容易观察到的病理学家进行定性分析。此外，一个计算机化的系统，而不是只
21、代表性的地区进行评估，可以处理整个幻灯片，防止取样偏差。 As reported by Meijer et al., the roots of image analysis for a more objective and reproducible prognosis date back to seventeenth century 5. Being amazingly precise, Leeuwenhoek had developed a system to measure the size of human erytrocytes using sand grain and hairs f
22、rom his head. However, the real acceleration in histopathological image analysis is due to the recent developments in whole-slide scanners. Whole slide scanners allow digitization of whole microscope slides at high magnifications up to 40 and provide very high resolution images. Recently, several im
23、age analysis approaches have been proposed for diverse types of cancer such as prostate 6, breast 7, brain 8 and neuroblastoma 9, 10. These methods commonly exploit texture, color or morphological properties of the tissue and propose quantitative methods to differentiate different histological grade
24、s.据报道Meijer等人，一个比较客观的和可再生的预后追溯到十七世纪的根源，图像分析5。令人惊讶的精确，列文虎克已经开发了一个系统的大小来衡量的人erytrocytes的沙粒和头发从他的头上。然而，真正的加速是由于最近的事态发展在整个幻灯片扫描仪在病理组织学图像分析。整个幻灯片扫描仪，使整个显微镜载玻片数字化高倍率高达40倍，提供了非常高的分辨率的图像。最近，一些图像分析方法已经被提出了不同类型的癌症，如前列腺癌6，乳腺癌7，脑8和神经母细胞瘤9，10。这些方法通常利用组织的质地，颜色或形态特征，并提出定量的方法来区分不同的组织学分级。In this study, our goal is t
25、o develop a computer-aided prognosis (CAP) system that will assist the pathologists in the grading of FL. The flowchart of the proposed system is given in Fig. 1.在这项研究中，我们的目标是开发了计算机辅助预测（CAP）在佛罗里达州的分级系统，这将有助于病理学家。所提出的系统的流程图给出在图1。We propose a novel approach that semantically describes histology images
26、 using model based intermediate representation (MBIR) and incorporates low level color texture analysis. In this approach, we first identify basic cytological components in the image and model the connected components of such regions using ellipses. An extensive set of features can be constructed fr
27、om this intermediate representation to characterize the tissue. Using this representation, we measure the relative amount and spatial distribution of these cytological components. We observe that the spatial distribution of these regions vary considerably between different histological grades and us
28、ing MBIR provides a convenient way to quantify our observations. Although this approach provides reasonable results especially identifying the most aggressive grade of FL, it is relatively less successful in classification of low grades. The tissue samples of these grades are better characterized by
29、 low level color texture features. Since graylevel features or other color texture features could not adequately model the microscopic tissue image content,we developed a non-linear color quantification based color texture feature constructed method. Due to the staining of the tissue samples, the re
30、sulting digitized FL images have considerably limited dynamic ranges in the color spectrum. Taking this fact into account, we propose the use of a non-linear color quantization using self-organizing maps (SOM). We used the quantized image to construct the co-occurrence matrix that is used to compute
31、 low level color texture features 12. By combining the statistical features constructed from the MBIR with the low level color texture features, the classification performance of the system is improved significantly.我们提出了一个新的方法，组织学图像语义描述模型的中间表示（MBIR），并采用低级别的色彩纹理分析。在这种方法中，我们首先确定基本的细胞学组件中的图像和模型等地区，使用椭
32、圆形的连接组件。广泛的功能集，可以由这个中间表示，该组织的特征。这表示，我们测量这些细胞学成分的相对含量和空间分布。我们观察到，这些地区的空间分布，不同病理分级之间有很大的不同和使用MBIR提供了一个方便的方法来量化我们的观察。虽然这种方法提供了合理的结果，特别是确定最积极的档次FL，低等级的分类是相对不太成功的。这些成绩的组织样本更好的特点是低级别的色彩纹理特征。由于graylevel功能或其他颜色的纹理特征不能充分模拟的微观组织图像内容，我们开发了一个非线性颜色量化为基础的色彩纹理特征构造方法。由于染色的组织样本，所得到的数字化的FL图像已相当有限的动态范围中的色彩频谱。考虑到这一点，我们提出了使用非直线的颜色量化，使用自组织映射（SOM）。我们使用的量化的图像构造的共生矩阵的，被用来计算低电平颜色纹理特征12。通过结合从MBIR与低级别的色彩纹理特征构成的统计特性，系统的分类性能显着提高。 The rest of the paper is organized as follows.Section 2 describes the image dataset used in this study;feature construction and extraction methods for the grading of FL
