基于多尺度卷积调制的医学图像分割

周新民; 熊智谋; 史长发; 杨健

doi:10.12263/DZXB.20231068

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基于多尺度卷积调制的医学图像分割

学术论文 | 更新时间：2025-12-08

- 基于多尺度卷积调制的医学图像分割
- Medical Image Segmentation Based on Multi‑Scale Convolution Modulation
- 电子学报 2024年52卷第9期页码：3159-3171
- 作者机构：
  
  1.湖南工商大学人工智能与先进计算学院，湖南长沙 410205
  2.湘江实验室，湖南长沙 410205
  3.湖南工商大学计算机学院，湖南长沙 410205
  4.湖南工商大学智能工程与智能制造学院，湖南长沙 410205
  5.湖南工商大学长沙人工智能社会实验室，湖南长沙 410205
- 作者简介：
  
  [ "周新民男，1977年5月出生于湖南省邵阳市.2010年5月博士毕业于同济大学计算机应用技术专业，2014年6月国防科技大学管理科学与工程博士后出站.现为湖南工商大学人工智能与先进计算学院副院长、教授、硕士生导师.主要研究方向为新型智慧城市、商务智能与大数据. E-mail: zhouxinmin2699@163.com" ]
  [ "熊智谋男，1995年3月出生于河南省信阳市.湖南工商大学计算机学院软件工程专业学术硕士在读.主要研究方向为医学图像处理与分析、计算机视觉. E-mail: Xzhimou@163.com" ]
  [ "史长发男，1985年2月出生于湖南省株洲市.2016年博士毕业于哈尔滨工业大学机械电子工程专业.现为湖南工商大学智能工程与智能制造学院副院长、副教授、硕士生导师.主要研究方向为医学图像处理与分析、深度学习. E-mail: ivanhanks@yeah.net" ]
  [ "杨健男，2000年7月出生于湖南省益阳市.湖南工商大学计算机学院电子信息专业硕士在读.主要研究方向为智慧医疗、计算机视觉.E-mail: 1468554194@qq.com" ]
- 基金信息：
  
  国家自然科学基金(72091515);国家社会科学基金(21BGL231)
- DOI：10.12263/DZXB.20231068
  中图分类号： TP391.4
- 收稿：2023-11-15，
  
  修回：2024-01-28，
  
  纸质出版：2024-09-25
- 稿件说明：
移动端阅览
周新民, 熊智谋, 史长发, 等. 基于多尺度卷积调制的医学图像分割[J]. 电子学报, 2024, 52(09): 3159-3171.

ZHOU Xin-min, XIONG Zhi-mou, SHI Chang-fa, et al. Medical Image Segmentation Based on Multi‑Scale Convolution Modulation[J]. Acta Electronica Sinica, 2024, 52(09): 3159-3171.
周新民, 熊智谋, 史长发, 等. 基于多尺度卷积调制的医学图像分割[J]. 电子学报, 2024, 52(09): 3159-3171. DOI：10.12263/DZXB.20231068

ZHOU Xin-min, XIONG Zhi-mou, SHI Chang-fa, et al. Medical Image Segmentation Based on Multi‑Scale Convolution Modulation[J]. Acta Electronica Sinica, 2024, 52(09): 3159-3171. DOI：10.12263/DZXB.20231068

摘要

当前，越来越多的医学图像分割模型都采用Transformer模型作为基础结构，然而，Transformer模型的计算复杂度与输入序列呈二次关系且需要大量的数据进行预训练才能取得较好的结果，在数据量不足的情况下无法发挥优势；此外，Transformer往往无法有效提取图像的局部信息.相比于Transformer，卷积神经网络则能够很好地规避上述两个问题.为了充分发挥卷积神经网络与Transformer的各自优势并进一步挖掘卷积神经网络的潜力，本文提出一个多尺度卷积调制网络模型（Multi-Scale Convolution Modulation Network，MSCMNet），该模型将视觉Transformer领域模型结构设计方法融入传统卷积网络.采用卷积调制和多尺度特征提取策略，构建基于多尺度卷积调制机制的特征提取模块（Multi-Scale Convolution Modulation，MSCM）.并提出高效的patch组合与patch分解策略分别用于特征图的下采样以及上采样，进一步提升模型的表征能力.在腹部多器官、心脏、皮肤癌以及细胞核四个不同类型以及不同规模的医学图像分割数据集上取得的mDice分别为0.805 7、0.923 3、0.923 9、0.854 8，以较低的运算量和参数量取得了最好的分割性能，为卷积神经网络以及Transformer在医学图像分割领域提供了一个新颖而高效的模型结构设计范式.

Abstract

Currently

more and more medical image segmentation models are using Transformer as their basic structure. However

the computational complexity of the Transformer model is quadratic with respect to the input sequence

and it requires a large amount of data for pre-training in order to achieve good results. In situations where there is insufficient data

the Transformer's advantages cannot be fully realized. Additionally

the Transformer often fails to effectively extract local information from images. In contrast

convolutional neural networks can effectively avoid these two problems. In order to fully leverage the strengths of both convolutional neural networks and Transformers and further explore the potential of convolutional neural networks

this paper proposes a multi-scale convolution modulation network (MSCMNet) model. This model incorporates the design methodology of visual Transformer models into traditional convolutional networks. By using convolution modulation and multi-scale feature extraction strategies

a feature extraction module based on multi-scale convolution modulation (MSCM) is constructed. Efficient patch combination and patch decomposition strategies are also proposed for downsampling and upsampling of feature maps

respectively

further enhancing the model's representation ability. The mDice scores obtained on four different types and sizes of medical image segmentation datasets - multiple organs in the abdomen

heart

skin cancer

and nucleus - are 0.805 7

0.923 3

0.923 9 and 0.854 8

respectively. With lower computational complexity and parameter count

MSCMNet achieves the best segmentation performance

providing a novel and efficient model structure design paradigm for convolutional neural networks and Transformers in the field of medical image segmentation.

关键词

Keywords

references

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