Kostopoulos Spiros, Ravazoula Panagiota, Asvestas Pantelis, Kalatzis Ioannis, Xenogiannopoulos George, Cavouras Dionisis, Glotsos Dimitris
Medical Image and Signal Processing Laboratory (MEDISP), Department of Biomedical Engineering, Technological Educational Institute of Athens, Ag. Spyridonos Street, 122 10, Egaleo, Athens, Greece.
Department of Pathology, University Hospital of Patras, Rio, 265 04, Patras, Greece.
J Digit Imaging. 2017 Jun;30(3):287-295. doi: 10.1007/s10278-017-9947-8.
Histopathology image processing, analysis and computer-aided diagnosis have been shown as effective assisting tools towards reliable and intra-/inter-observer invariant decisions in traditional pathology. Especially for cancer patients, decisions need to be as accurate as possible in order to increase the probability of optimal treatment planning. In this study, we propose a new image collection library (HICL-Histology Image Collection Library) comprising 3831 histological images of three different diseases, for fostering research in histopathology image processing, analysis and computer-aided diagnosis. Raw data comprised 93, 116 and 55 cases of brain, breast and laryngeal cancer respectively collected from the archives of the University Hospital of Patras, Greece. The 3831 images were generated from the most representative regions of the pathology, specified by an experienced histopathologist. The HICL Image Collection is free for access under an academic license at http://medisp.bme.teiath.gr/hicl/ . Potential exploitations of the proposed library may span over a board spectrum, such as in image processing to improve visualization, in segmentation for nuclei detection, in decision support systems for second opinion consultations, in statistical analysis for investigation of potential correlations between clinical annotations and imaging findings and, generally, in fostering research on histopathology image processing and analysis. To the best of our knowledge, the HICL constitutes the first attempt towards creation of a reference image collection library in the field of traditional histopathology, publicly and freely available to the scientific community.
组织病理学图像处理、分析及计算机辅助诊断已被证明是传统病理学中做出可靠且观察者间/观察者内一致决策的有效辅助工具。尤其对于癌症患者,决策需要尽可能准确,以提高优化治疗方案规划的概率。在本研究中,我们提出了一个新的图像集库(HICL - 组织学图像集库),它包含三种不同疾病的3831张组织学图像,以促进组织病理学图像处理、分析及计算机辅助诊断方面的研究。原始数据分别包括从希腊帕特雷大学医院档案中收集的93例、116例和55例脑癌、乳腺癌和喉癌病例。这3831张图像由一位经验丰富的组织病理学家指定的病理学最具代表性区域生成。HICL图像集在学术许可下可免费访问,网址为http://medisp.bme.teiath.gr/hicl/ 。所提议库的潜在应用范围广泛,例如在图像处理中改善可视化效果、在细胞核检测的分割中、在用于二次会诊的决策支持系统中、在统计分析中研究临床注释与影像结果之间的潜在相关性,以及总体上在促进组织病理学图像处理和分析的研究中。据我们所知,HICL是在传统组织病理学领域创建一个可供科学界公开免费使用的参考图像集库的首次尝试。