CARE直肠癌CT图像分割数据集
中国科学技术大学创建的直肠癌CT图像分割大型数据集，含399名患者像素级标注，用于提升分割算法精度。
基本信息
资源简介
CARE数据集是中国科学技术大学电磁空间信息重点实验室创建的直肠癌CT图像分割大型数据集，包含399名患者的CT扫描，总数据量超过74GB，提供像素级标注以区分正常和癌变直肠区域，旨在支持直肠癌临床诊断和分割算法研究。
下载信息
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使用方式
数据集说明
CARE直肠癌CT图像分割数据集 对应论文数据集（arXiv 预印本）。
数据获取指引
- 打开论文页面获取作者与项目信息：https://arxiv.org/abs/2308.08283v1
- 论文 Data Availability / Code Availability 章节标注了数据实际托管位置；
- 获取到实际数据链接后，按对应平台标准方式下载。
论文摘要：Abstract:Rectal cancer segmentation of CT image plays a crucial role in timely clinical diagnosis, radiotherapy treatment, and follow-up. Although current segmentation methods have shown promise in delineating cancerous tissues, they still encounter challenges in achieving high segmentation precision. These obstacles arise from the intricate anatomical structures of the rectum and the difficulties in performing differential diagnosis of rectal cancer. Additionally, a major obstacle is the lack of a large-scale, finely annotated CT image dataset for rectal cancer segmentation. To address these issues, this work introduces a novel large scale rectal cancer CT image dataset CARE with pixel-level annotations for both normal and cancerous rectum, which serves as a valuable resource for algorithm research and clinical application development. Moreover, we propose a novel medical cancer lesion segmentation benchmark model named U-SAM. The model is specifically designed to tackle the challenges posed by the intricate anatomical structures of abdominal organs by incorporating prompt information. U-SAM contains three key components: promptable information (e.g., points) to aid in target area localization, a convolution module for capturing low-level lesion details, and skip-connections to preserve and recover spatial information during the encoding-decoding process. To evaluate the effectiveness of U-SAM, we systematically compare its performance with several popular segmentation methods on the CARE dataset. The generalization of the model is further verified on the WORD dataset. Ext
精度瓶颈？数据缺失？
当前公开数据无法满足您的算法精度？千方提供针对 直肠癌 的高质量、多模态真实临床数据定制解决方案。
