Colorectal cancer is the third-most common cancer in the Western Hemisphere. The segmentation of colorectal and colorectal cancer by computed tomography is an urgent problem in medicine. Indeed, a system capable of solving this problem will enable the detection of colorectal cancer at early stages of the disease, facilitate the search for pathology by the radiologist, and significantly accelerate the process of diagnosing the disease. However, scientific publications on medical image processing mostly use closed, non-public data. This paper presents an extension of the Medical Decathlon dataset with colorectal markups in order to improve the quality of segmentation algorithms. An experienced radiologist validated the data, categorized it into subsets by quality, and published it in the public domain. Based on the obtained results, we trained neural network models of the UNet architecture with 5-part cross-validation and achieved a Dice metric quality of $0.6988 \pm 0.3$. The published markups will improve the quality of colorectal cancer detection and simplify the radiologist's job for study description.

翻译：结直肠癌是西半球第三大常见癌症。通过计算机断层扫描实现结直肠及结直肠癌的分割是医学领域的迫切课题。事实上，能够解决该问题的系统将实现结直肠癌的早期检测，辅助放射科医师进行病理搜寻，并显著加速疾病诊断流程。然而，当前医学图像处理领域的科学出版物大多使用封闭的非公开数据。本文通过添加结直肠标注对医学十项全能数据集进行扩展，旨在提升分割算法质量。经验丰富的放射科医师对数据进行了验证，按质量分级归类，并公开发布。基于所得结果，我们采用5折交叉验证训练了UNet架构的神经网络模型，获得了$0.6988 \pm 0.3$的Dice度量质量。已发布的标注数据将提升结直肠癌检测质量，并简化放射科医师的研究描述工作。