Automatic Polyp Segmentation via Multi-scale Subtraction Network

By Xiaoqi Zhao, Lihe Zhang, Huchuan Lu. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021

355 citations recorded by the source corpus.

More than 90\% of colorectal cancer is gradually transformed from colorectal polyps. In clinical practice, precise polyp segmentation provides important information in the early detection of colorectal cancer. Therefore, automatic polyp segmentation techniques are of great importance for both patients and doctors. Most existing methods are based on U-shape structure and use element-wise addition or concatenation to fuse different level features progressively in decoder. However, both the two operations easily generate plenty of redundant information, which will weaken the complementarity between different level features, resulting in inaccurate localization and blurred edges of polyps. To address this challenge, we propose a multi-scale subtraction network (MSNet) to segment polyp from colonoscopy image. Specifically, we first design a subtraction unit (SU) to produce the difference features between adjacent levels in encoder. Then, we pyramidally equip the SUs at different levels with varying receptive fields, thereby obtaining rich multi-scale difference information. In addition, we build a training-free network"LossNet"to comprehensively supervise the polyp-aware features from bottom layer to top layer, which drives the MSNet to capture the detailed and structural cues simultaneously. Extensive experiments on five benchmark datasets demonstrate that our MSNet performs favorably against most state-of-the-art methods under different evaluation metrics. Furthermore, MSNet runs at a real-time speed of $\sim$70fps when processing a $352 \times 352$ image. The source code will be publicly available at \url{https://github.com/Xiaoqi-Zhao-DLUT/MSNet}. \keywords{Colorectal Cancer \and Automatic Polyp Segmentation \and Subtraction \and LossNet.}

Subfields: Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/f36ff155a741f4c404fcac9de1d47e53aa99e71b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

  1. Ling Shao
  2. Deng-Ping Fan
  3. Xiaoqi Zhao
  4. Huchuan Lu
  5. Huan Fu
  6. Ming-Ming Cheng
  7. Youwei Pang
  8. Jianbing Shen

In Industrial and Manufacturing Engineering

  1. Huchuan Lu

In Media Technology

  1. Deng-Ping Fan
  2. Ling Shao
  3. Youwei Pang
  4. Huchuan Lu
  5. Huan Fu
  6. Xiaoqi Zhao
  7. Wenhai Wang

Citations in the corpus