LIT-Unet:用于医学图像分割的轻量级有效模型。

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Radiological Physics and Technology Pub Date : 2024-12-01 Epub Date: 2024-09-20 DOI:10.1007/s12194-024-00844-4
Ru Wang, Qiqi Kou, Lina Dou
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引用次数: 0

摘要

本研究旨在设计一种简单高效的医学图像自动分割模型,以方便医生做出更准确的诊断和治疗方案。研究提出了一种具有对称编码器-解码器 U 型结构的混合轻量级网络 LIT-Unet。利用 Synapse 多器官分割数据集和自动心脏诊断挑战(ACDC)数据集测试该方法的分割性能。采用 Dice 相似性系数(DSC ↑)和 95% Hausdorff 距离(HD95 ↓)两个指标来评估和比较该方法与当前先进方法的分割能力。为了证明模型的轻便性和有效性,我们进行了消融实验。对于 Synapse 数据集,我们的模型获得了更高的 DSC 分数(80.40%),比典型的混合模型(TransUnet)提高了 3.8%。95 HD 值较低,为 20.67%。对于 ACDC 数据集,与所列其他网络相比,LIT-Unet 实现了最佳平均 DSC (%) 91.84。与补丁扩展相比,我们模型的 DSC 在可变形标记合并(DTM)的帮助下直观地提高了 1.62%。这些结果表明,所提出的分层 LIT-Unet 可以达到显著的准确性,有望为临床诊断和治疗提供可靠的依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LIT-Unet: a lightweight and effective model for medical image segmentation.

This study aimed to design a simple and efficient automatic segmentation model for medical images, so as to facilitate doctors to make more accurate diagnosis and treatment plan. A hybrid lightweight network LIT-Unet with symmetric encoder-decoder U-shaped architecture is proposed. Synapse multi-organ segmentation dataset and automated cardiac diagnosis challenge (ACDC) dataset were used to test the segmentation performance of the method. Two indexes, Dice similarity coefficient (DSC ↑) and 95% Hausdorff distance (HD95 ↓), were used to evaluate and compare the segmentation ability with the current advanced methods. Ablation experiments were conducted to demonstrate the lightweight nature and effectiveness of our model. For Synapse dataset, our model achieves a higher DSC score (80.40%), an improvement of 3.8% over the typical hybrid model (TransUnet). The 95 HD value is low at 20.67%. For ACDC dataset, LIT-Unet achieves the optimal average DSC (%) of 91.84 compared with other networks listed. Compared to patch expanding, the DSC of our model is intuitively improved by 1.62% with the help of deformable token merging (DTM). These results show that the proposed hierarchical LIT-Unet can achieve significant accuracy and is expected to provide a reliable basis for clinical diagnosis and treatment.

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来源期刊
Radiological Physics and Technology
Radiological Physics and Technology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
3.00
自引率
12.50%
发文量
40
期刊介绍: The purpose of the journal Radiological Physics and Technology is to provide a forum for sharing new knowledge related to research and development in radiological science and technology, including medical physics and radiological technology in diagnostic radiology, nuclear medicine, and radiation therapy among many other radiological disciplines, as well as to contribute to progress and improvement in medical practice and patient health care.
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