Mitosis detection in breast cancer histological images An ICPR 2012 contest.
Mitosis detection in breast cancer histological images An ICPR 2012 contest.
复制标题
DOI：
10.4103/2153-3539.112693
复制
发表时间：
2013
影响因子：
--
通讯作者：
Gurcan MN
中科院分区：
文献类型：
--
作者：
Roux L;Racoceanu D;Loménie N;Kulikova M;Irshad H;Klossa J;Capron F;Genestie C;Le Naour G;Gurcan MN
在认知显微镜(MICO)项目的框架下，我们为2012年ICPR会议设立了一项关于乳腺癌H和E染色切片图像中有丝分裂检测的竞赛。有丝分裂细胞计数是判断乳腺癌预后的重要指标。然而，数字组织病理学中的有丝分裂检测是一个具有挑战性的问题，需要更深入的研究。事实上，有丝分裂的检测是困难的，因为有丝分裂是具有多种形状的小物体，因此它们很容易与图像中存在的其他物体或伪影混淆。我们通过使用两台不同分辨率的不同幻灯片扫描仪和一台多光谱显微镜来生成红绿蓝(RGB)图像，并使用了一台多光谱显微镜来生成10个不同光谱波段和17层Z堆叠的图像，从而进一步增加了比赛的维度。17个团队参与了这项研究，其中最好的团队达到了0.7的召回率和0.89的准确率。据报道，已有几项关于处理数字化载玻片的自动工具的研究，主要集中在细胞核或小管检测上。有丝分裂的检测是一个具有挑战性的问题，在文献中还没有得到很好的解决。有丝分裂细胞计数是乳腺癌分级的一个重要参数，因为它可以评估肿瘤的侵袭性。然而，同一张玻片的一致性、重复性和有丝分裂细胞计数的一致性在不同的病理学家之间可能有很大的差异。这项任务的自动化工具可能有助于达到更好的一致性，同时减轻这一要求苛刻的任务对病理学家的负担。法国巴黎Pitiι-SalpιTriιRe医院病理科的Frιdιrique Capron教授团队挑选了一组五张乳腺癌幻灯片。用三种不同的设备进行扫描：Aperio ScanScope XT玻片扫描仪、Hamamatsu NanoZomer 2.0-HT玻片扫描仪和10波段多光谱显微镜。数据集由来自5张不同幻灯片的50个高倍场(HPF)组成，这些幻灯片是在×40倍的放大下扫描的。每台滑板有10个高压泵。病理学家已经手动对所有有丝分裂细胞进行了注释。HPF的尺寸为512μm×512μm(即面积为0.262 mm2，相当于显微镜视场直径0.58 mm的表面)。这50个HPF在两种扫描仪的图像上总共包含326个有丝分裂细胞，在多光谱显微镜上包含322个有丝分裂细胞。多达129支队伍报名参加了比赛。然而，只有17个团队提交了他们对有丝分裂细胞的检测。最佳团队的表现非常有前途，F-MEASURE高达0.78。然而，到目前为止，我们提供的数据库太小，无法很好地评估所提出算法的可靠性和健壮性。有丝分裂细胞计数是许多类型癌症分级的重要标准，然而，由于缺乏可用数据，对有丝分裂细胞自动检测的研究很少。这项竞赛的一个主要目标是提出一个数字化乳腺癌组织病理学切片上的有丝分裂细胞数据库，以启动有丝分裂细胞自动检测的工作。在未来，我们希望扩展这个数据库，以拥有更多来自不同患者和不同类型癌症的图像。此外，有丝分裂细胞应该由几位病理学家进行注释，以反映它们之间的部分一致。
In the framework of the Cognitive Microscope (MICO) project, we have set up a contest about mitosis detection in images of H and E stained slides of breast cancer for the conference ICPR 2012. Mitotic count is an important parameter for the prognosis of breast cancer. However, mitosis detection in digital histopathology is a challenging problem that needs a deeper study. Indeed, mitosis detection is difficult because mitosis are small objects with a large variety of shapes, and they can thus be easily confused with some other objects or artefacts present in the image. We added a further dimension to the contest by using two different slide scanners having different resolutions and producing red-green-blue (RGB) images, and a multi-spectral microscope producing images in 10 different spectral bands and 17 layers Z-stack. 17 teams participated in the study and the best team achieved a recall rate of 0.7 and precision of 0.89. Several studies on automatic tools to process digitized slides have been reported focusing mainly on nuclei or tubule detection. Mitosis detection is a challenging problem that has not yet been addressed well in the literature. Mitotic count is an important parameter in breast cancer grading as it gives an evaluation of the aggressiveness of the tumor. However, consistency, reproducibility and agreement on mitotic count for the same slide can vary largely among pathologists. An automatic tool for this task may help for reaching a better consistency, and at the same time reducing the burden of this demanding task for the pathologists. Professor Frιdιrique Capron team of the pathology department at Pitiι-Salpκtriθre Hospital in Paris, France, has selected a set of five slides of breast cancer. The slides are stained with H and E. They have been scanned by three different equipments: Aperio ScanScope XT slide scanner, Hamamatsu NanoZoomer 2.0-HT slide scanner and 10 bands multispectral microscope. The data set is made up of 50 high power fields (HPF) coming from 5 different slides scanned at ×40 magnification. There are 10 HPFs/slide. The pathologist has annotated all the mitotic cells manually. A HPF has a size of 512 μm × 512 μm (that is an area of 0.262 mm 2 , which is a surface equivalent to that of a microscope field diameter of 0.58 mm. These 50 HPFs contain a total of 326 mitotic cells on images of both scanners, and 322 mitotic cells on the multispectral microscope. Up to 129 teams have registered to the contest. However, only 17 teams submitted their detection of mitotic cells. The performance of the best team is very promising, with F-measure as high as 0.78. However, the database we provided is by far too small for a good assessment of reliability and robustness of the proposed algorithms. Mitotic count is an important criterion in the grading of many types of cancers, however, very little research has been made on automatic mitotic cell detection, mainly because of a lack of available data. A main objective of this contest was to propose a database of mitotic cells on digitized breast cancer histopathology slides to initiate works on automated mitotic cell detection. In the future, we would like to extend this database to have much more images from different patients and also for different types of cancers. In addition, mitotic cells should be annotated by several pathologists to reflect the partial agreement among them.