  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.
