  Accurate segmentation of metastatic lymph nodes in rectal cancer is crucial
for the staging and treatment of rectal cancer. However, existing segmentation
approaches face challenges due to the absence of pixel-level annotated datasets
tailored for lymph nodes around the rectum. Additionally, metastatic lymph
nodes are characterized by their relatively small size, irregular shapes, and
lower contrast compared to the background, further complicating the
segmentation task. To address these challenges, we present the first
large-scale perirectal metastatic lymph node CT image dataset called Meply,
which encompasses pixel-level annotations of 269 patients diagnosed with rectal
cancer. Furthermore, we introduce a novel lymph-node segmentation model named
CoSAM. The CoSAM utilizes sequence-based detection to guide the segmentation of
metastatic lymph nodes in rectal cancer, contributing to improved localization
performance for the segmentation model. It comprises three key components:
sequence-based detection module, segmentation module, and collaborative
convergence unit. To evaluate the effectiveness of CoSAM, we systematically
compare its performance with several popular segmentation methods using the
Meply dataset. Our code and dataset will be publicly available at:
https://github.com/kanydao/CoSAM.
