MF-ResUnet: A 3D Liver Image Segmentation Method Based on Multi-Scale Feature Fusion

IF 2.3 3区医学 Q2 SURGERY

International Journal of Medical Robotics and Computer Assisted Surgery Pub Date : 2025-05-21 DOI:10.1002/rcs.70068

Jun Qin, Yang Li, Guihe Qin

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引用次数: 0

Abstract

Background

Due to the variable shapes of the liver parenchyma, minimal voxel intensity differences with adjacent organs, and discontinuous liver boundaries, automatic liver segmentation from computerised tomography images poses significant challenges.

Methods

In this study, we propose a 3D liver segmentation method based on multiscale feature fusion. This network employs SE channel attention to recalibrate liver features. Additionally, it utilises an AMF module for multiscale feature fusion to obtain rich spatial information. Furthermore, we introduce the NGAB module to address the deteriorating effects of dilated convolutions as the dilation rate increases, contributing to enhanced feature representation and improving accuracy in liver segmentation.

Results

Experimental results on the publicly available LiTS2017 dataset and 3DIRCADb dataset show that our proposed framework achieves a DSC of 0.977 and 0.967 in liver segmentation, respectively.

Conclusions

The proposed method can adequately capture multiscale characteristics, showing promising prospects for automatic liver segmentation.

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MF-ResUnet：基于多尺度特征融合的三维肝脏图像分割方法

由于肝实质形状多变，与邻近器官体素强度差异极小，以及肝脏边界不连续，从计算机断层扫描图像中自动分割肝脏提出了重大挑战。方法提出一种基于多尺度特征融合的三维肝脏分割方法。该网络利用SE通道关注来重新校准肝脏特征。利用AMF模块进行多尺度特征融合，获取丰富的空间信息。此外，我们引入了NGAB模块来解决扩张卷积随着扩张率的增加而恶化的影响，有助于增强特征表示并提高肝脏分割的准确性。结果在公开的LiTS2017数据集和3DIRCADb数据集上的实验结果表明，我们提出的框架在肝脏分割方面的DSC分别达到了0.977和0.967。结论该方法能充分捕捉多尺度特征，在肝脏自动分割中具有广阔的应用前景。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Medical Robotics and Computer Assisted Surgery 医学-外科

CiteScore

4.50

自引率

12.00%

发文量

131

审稿时长

6-12 weeks

期刊介绍： The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.