RRM-TransUNet: Deep-Learning Driven Interactive Model for Precise Pancreas Segmentation in CT Images

IF 2.3 3区医学 Q2 SURGERY

International Journal of Medical Robotics and Computer Assisted Surgery Pub Date : 2025-04-10 DOI:10.1002/rcs.70065

Yulan Wang, Weimin Liu, Peng Yu, Xin Huang, Junjun Pan

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引用次数: 0

Abstract

Background

Pancreatic diseases such as cancer and pancreatitis pose significant health risks. Early detection requires precise segmentation results. Fully automatic segmentation algorithms cannot integrate clinical expertise and correct output errors, while interactive methods can offer a better chance for higher accuracy and reliability.

Methods

We proposed a new network—RRM-TransUNet for the interactive pancreas segmentation task in CT images aiming to provide more reliable and precise results. The network incorporates Rotary Position Embedding, Root Mean Square Normalisation, and a Mixture of Experts mechanism. An intuitive interface is constructed for user-aided pancreas segmentation.

Results

RRM-TransUNet achieves outstanding performance on multiple datasets, with a Dice Similarity Coefficient (DSC) of 93.82% and an Average Symmetric Surface Distance error (ASSD) of 1.12 mm on MSD, 93.79%/1.15 mm on AMOS, and 93.68%/1.18 mm on AbdomenCT-1K.

Conclusion

Our method outperforms previous methods and provides doctors with an efficient and user-friendly interactive pancreas segmentation experience through the intuitive interface.

查看原文本刊更多论文

RRM-TransUNet: CT图像胰腺精确分割的深度学习驱动交互模型

胰腺癌和胰腺炎等胰腺疾病对健康构成重大威胁。早期检测需要精确的分割结果。全自动分割算法无法整合临床专业知识和纠正输出错误，而交互式方法可以提供更高的准确性和可靠性。方法提出了一种新的rrm - transunet网络，用于CT图像的交互式胰腺分割任务，以提供更可靠和精确的结果。该网络结合了旋转位置嵌入、均方根归一化和混合专家机制。为用户辅助胰腺分割构建了直观的界面。结果RRM-TransUNet在多数据集上表现出色，在MSD上的骰子相似系数（DSC）为93.82%，平均对称表面距离误差（ASSD）为1.12 mm，在AMOS上为93.79%/1.15 mm，在腹部上为93.68%/1.18 mm。结论该方法优于以往的方法，通过直观的界面为医生提供了高效、友好的交互式胰腺分割体验。

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.