Meply: A Large-scale Dataset and Baseline Evaluations for Metastatic Perirectal Lymph Node Detection and Segmentation
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| Format: | Preprint |
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2024
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| _version_ | 1866929313270464512 |
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| author | Guo, Weidong Zhang, Hantao Wan, Shouhong Zou, Bingbing Wang, Wanqin Qiu, Chenyang Li, Jun Jin, Peiquan |
| author_facet | Guo, Weidong Zhang, Hantao Wan, Shouhong Zou, Bingbing Wang, Wanqin Qiu, Chenyang Li, Jun Jin, Peiquan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08916 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Meply: A Large-scale Dataset and Baseline Evaluations for Metastatic Perirectal Lymph Node Detection and Segmentation Guo, Weidong Zhang, Hantao Wan, Shouhong Zou, Bingbing Wang, Wanqin Qiu, Chenyang Li, Jun Jin, Peiquan Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Meply: A Large-scale Dataset and Baseline Evaluations for Metastatic Perirectal Lymph Node Detection and Segmentation |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2404.08916 |