基于多序列MRI的骨盆和骶骨肿瘤分割深度学习框架的开发和评估:一项回顾性研究。

IF 3.5 2区 医学 Q2 ONCOLOGY
Ping Yin, Weidao Chen, Qianrui Fan, Ruize Yu, Xia Liu, Tao Liu, Dawei Wang, Nan Hong
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引用次数: 0

摘要

背景:在多序列磁共振成像(MRI)中准确分割骨盆和骶骨肿瘤(PSTs)对于有效的治疗和手术计划至关重要。目的:开发一种深度学习框架,用于从多序列MRI中高效分割pst。材料和方法:本研究共纳入2011年4月至2022年5月期间病理证实的pst患者616例。我们提出了一个实用的DL框架,该框架集成了2.5D U-net和MobileNetV2,用于自动分割PST,并在多个MRI序列上快速注释策略,包括t1 -加权(T1-w), t2 -加权(T2-w),弥散加权成像(DWI)和对比度增强的t1 -加权(CET1-w)。提出了全序列分割模型和t2融合分割模型。在我们的深度学习模型的实现过程中,训练集中的所有感兴趣区域(roi)都被粗标记,而测试集中的roi被细标记。使用骰子得分和交汇联合(IoU)来评价模型的性能。结果:与2D和3D U-Net模型相比,2.5D MobileNetV2架构表现出更好的分割性能,Dice得分为0.741,IoU为0.615。使用四个MRI序列(T1-w, CET1-w, T2-w和DWI)融合训练的全序列模型表现出优异的性能,T1-w的Dice得分为0.659,CET1-w为0.763,T2-w为0.819,DWI为0.723。而以T2-w和CET1-w序列作为输入的T2-fusion分割模型,其Dice得分为0.833,IoU值为0.719。结论:在本研究中,我们开发了一个实用的多序列MRI PST分割DL框架,减少了对数据注释的依赖。这些模型为各种临床场景提供了解决方案,具有广泛应用的巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and evaluation of a deep learning framework for pelvic and sacral tumor segmentation from multi-sequence MRI: a retrospective study.

Background: Accurate segmentation of pelvic and sacral tumors (PSTs) in multi-sequence magnetic resonance imaging (MRI) is essential for effective treatment and surgical planning.

Purpose: To develop a deep learning (DL) framework for efficient segmentation of PSTs from multi-sequence MRI.

Materials and methods: This study included a total of 616 patients with pathologically confirmed PSTs between April 2011 to May 2022. We proposed a practical DL framework that integrates a 2.5D U-net and MobileNetV2 for automatic PST segmentation with a fast annotation strategy across multiple MRI sequences, including T1-weighted (T1-w), T2-weighted (T2-w), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted (CET1-w). Two distinct models, the All-sequence segmentation model and the T2-fusion segmentation model, were developed. During the implementation of our DL models, all regions of interest (ROIs) in the training set were coarse labeled, and ROIs in the test set were fine labeled. Dice score and intersection over union (IoU) were used to evaluate model performance.

Results: The 2.5D MobileNetV2 architecture demonstrated improved segmentation performance compared to 2D and 3D U-Net models, with a Dice score of 0.741 and an IoU of 0.615. The All-sequence model, which was trained using a fusion of four MRI sequences (T1-w, CET1-w, T2-w, and DWI), exhibited superior performance with Dice scores of 0.659 for T1-w, 0.763 for CET1-w, 0.819 for T2-w, and 0.723 for DWI as inputs. In contrast, the T2-fusion segmentation model, which used T2-w and CET1-w sequences as inputs, achieved a Dice score of 0.833 and an IoU value of 0.719.

Conclusions: In this study, we developed a practical DL framework for PST segmentation via multi-sequence MRI, which reduces the dependence on data annotation. These models offer solutions for various clinical scenarios and have significant potential for wide-ranging applications.

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来源期刊
Cancer Imaging
Cancer Imaging ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
7.00
自引率
0.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Cancer Imaging is an open access, peer-reviewed journal publishing original articles, reviews and editorials written by expert international radiologists working in oncology. The journal encompasses CT, MR, PET, ultrasound, radionuclide and multimodal imaging in all kinds of malignant tumours, plus new developments, techniques and innovations. Topics of interest include: Breast Imaging Chest Complications of treatment Ear, Nose & Throat Gastrointestinal Hepatobiliary & Pancreatic Imaging biomarkers Interventional Lymphoma Measurement of tumour response Molecular functional imaging Musculoskeletal Neuro oncology Nuclear Medicine Paediatric.
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