GLENDA（妇科腹腔镜子宫内膜异位症数据集）包含超过350张注释的子宫内膜异位症病变图像，这些图像来自100多次妇科腹腔镜手术，以及来自20多次手术的超过13,000张未注释的非病理图像。该数据集专门用于自动内容分析问题，特别是在子宫内膜异位症识别的背景下。数据集的结构包括二进制分类和多类分类配置，每个配置都有详细的特征描述和数据分割信息。数据集的注释由子宫内膜异位症治疗领域的医学专家生成。数据集的使用仅限于科学研究目的，不能用于商业用途。 子宫内膜异位症是育龄妇女的一种良性但可能疼痛的疾病，涉及子宫外部位子宫样组织的生长。相应的病变可以在不同的位置和严重程度中发现，通常每个患者在多个情况下需要医生确定其范围。这通常是通过结合使用两个流行的分类系统（修订后的美国生殖医学学会 （rASRM） 和欧洲 Enzian 分数来计算其幅度来实现的。子宫内膜异位症无法被外行人可靠地识别，因此，该数据集是在子宫内膜异位症治疗领域的医学专家的帮助下创建的。

腹部

@inproceedings{10.1007/978-3-030-37734-2_36,
    abstract = {Gynecologic laparoscopy as a type of minimally invasive surgery (MIS) is performed via a live feed of a patient's abdomen surveying the insertion and handling of various instruments for conducting treatment. Adopting this kind of surgical intervention not only facilitates a great variety of treatments, the possibility of recording said video streams is as well essential for numerous post-surgical activities, such as treatment planning, case documentation and education. Nonetheless, the process of manually analyzing surgical recordings, as it is carried out in current practice, usually proves tediously time-consuming. In order to improve upon this situation, more sophisticated computer vision as well as machine learning approaches are actively developed. Since most of such approaches heavily rely on sample data, which especially in the medical field is only sparsely available, with this work we publish the Gynecologic Laparoscopy ENdometriosis DAtaset (GLENDA) -- an image dataset containing region-based annotations of a common medical condition named endometriosis, i.e. the dislocation of uterine-like tissue. The dataset is the first of its kind and it has been created in collaboration with leading medical experts in the field.},
    address = {Cham},
    author = {Leibetseder, Andreas and Kletz, Sabrina and Schoeffmann, Klaus and Keckstein, Simon and Keckstein, J{\"o}rg},
    booktitle = {MultiMedia Modeling},
    editor = {Ro, Yong Man and Cheng, Wen-Huang and Kim, Junmo and Chu, Wei-Ta and Cui, Peng and Choi, Jung-Woo and Hu, Min-Chun and De Neve, Wesley},
    isbn = {978-3-030-37734-2},
    pages = {439--450},
    publisher = {Springer International Publishing},
    title = {GLENDA: Gynecologic Laparoscopy Endometriosis Dataset},
    year = {2020}
}

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