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Meply: A Large-scale Dataset and Baseline Evaluations for Metastatic Perirectal Lymph Node Detection and Segmentation

Weidong Guo, Hantao Zhang, Shouhong Wan, Bingbing Zou, Wanqin Wang, Chenyang Qiu, Jun Li, Peiquan Jin

TL;DR

This work addresses the lack of pixel-level annotated data for metastatic perirectal lymph nodes in rectal cancer and the challenge of accurately segmenting small, low-contrast structures. It introduces Meply, a large-scale CT dataset with pixel-level LN annotations from 269 patients, and CoSAM, a prompt-free collaborative framework that jointly learns detection and segmentation using a 2.5D sequence-based detector and a SAM-based segmentation branch. CoSAM leverages sequence information and collaborative convergence to improve localization and mask accuracy, reducing false positives and achieving state-of-the-art segmentation metrics on Meply (Dice ~74.2%, IoU ~59.0%). The dataset and method collectively enable end-to-end, automatic LN segmentation in rectal cancer, with practical implications for staging and treatment planning, and are released for community use.

Abstract

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.

Meply: A Large-scale Dataset and Baseline Evaluations for Metastatic Perirectal Lymph Node Detection and Segmentation

TL;DR

This work addresses the lack of pixel-level annotated data for metastatic perirectal lymph nodes in rectal cancer and the challenge of accurately segmenting small, low-contrast structures. It introduces Meply, a large-scale CT dataset with pixel-level LN annotations from 269 patients, and CoSAM, a prompt-free collaborative framework that jointly learns detection and segmentation using a 2.5D sequence-based detector and a SAM-based segmentation branch. CoSAM leverages sequence information and collaborative convergence to improve localization and mask accuracy, reducing false positives and achieving state-of-the-art segmentation metrics on Meply (Dice ~74.2%, IoU ~59.0%). The dataset and method collectively enable end-to-end, automatic LN segmentation in rectal cancer, with practical implications for staging and treatment planning, and are released for community use.

Abstract

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.
Paper Structure (15 sections, 7 equations, 3 figures, 4 tables)

This paper contains 15 sections, 7 equations, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Lymph Node Dataset
  4. SAM in Medical Image Analysis.
  5. The Meply Dataset
  6. Overview
  7. Data construction
  8. Method
  9. Overview of the CoSAM
  10. 2.5D Sequence-based Lymph Node Detector
  11. Prompt-based RoI Refinement and Segmentation
  12. Experiments
  13. Implementation Details
  14. Evaluation Results
  15. Conclusion

Figures (3)

  • Figure 1: An overview of the annotated metastatic perirectal lymph nodes in CT. (a) demonstrates CT sequences of lymph nodes in various sizes. (b) illustrates the volume distribution of the lymph nodes in our dataset. (b) represents the views from 3 different perspectives and 3D rendering results of the annotations.
  • Figure 2: The framework of our proposed CoSAM.
  • Figure 3: Visual comparisons of different segmentation methods on Meply dataset.