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. Extensive experiments
demonstrate that the proposed U-SAM outperforms state-of-the-art methods on
these two datasets. These experiments can serve as the baseline for future
research and clinical application development.

本研究介绍了一个新颖的大规模直肠癌 CT 图像数据集 CARE，其具有对正常和癌症直肠进行像素级注释的宝贵资源，用于算法研究和临床应用开发；此外，我们提出了一种名为 U-SAM 的新型医疗癌症病变分割基准模型，它通过具备即时信息处理功能以解决腹部器官结构复杂性所带来的挑战，包含提示信息、捕捉病变细节的卷积模块以及保留和恢复空间信息的跳过连接；通过与几种流行的分割方法在 CARE 数据集上的性能比较以及在 WORD 数据集上的泛化验证，大量实验证明了所提出的 U-SAM 在这两个数据集上优于现有方法，为未来的研究和临床应用开发提供了基线。