在双序列磁共振成像上进行自我监督学习以自动分割鼻咽癌

IF 5.4 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Zongyou Cai , Zhangnan Zhong , Haiwei Lin , Bingsheng Huang , Ziyue Xu , Bin Huang , Wei Deng , Qiting Wu , Kaixin Lei , Jiegeng Lyu , Yufeng Ye , Hanwei Chen , Jian Zhang
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引用次数: 0

摘要

鼻咽癌(NPC)的自动分割对治疗过程至关重要,但由于在积累大量标注数据集方面存在障碍,因此面临着挑战。尽管之前的研究已经应用了自我监督学习来利用未标记数据提高分割性能,但这些方法往往忽略了双序列磁共振成像(MRI)的优势。在本研究中,我们将自我监督学习与显著性转换模块相结合,利用未标记的双序列核磁共振成像进行准确的鼻咽癌分割。为了开发和评估我们的网络,我们收集了 44 位已标记和 72 位未标记的患者。令人印象深刻的是,我们的网络达到了 0.77 的平均 Dice 相似系数 (DSC),这与之前一项依赖于 4100 个标注病例的训练集的研究结果一致。研究结果进一步表明,我们的方法只需进行最小限度的调整,主要是对 DSC 进行 20% 的调整,即可达到临床标准。通过加强对鼻咽癌的自动分割,我们的方法减轻了肿瘤学家的注释负担,减少了主观性,并确保了可靠的鼻咽癌划分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Self-supervised learning on dual-sequence magnetic resonance imaging for automatic segmentation of nasopharyngeal carcinoma
Automating the segmentation of nasopharyngeal carcinoma (NPC) is crucial for therapeutic procedures but presents challenges given the hurdles in amassing extensively annotated datasets. Although previous studies have applied self-supervised learning to capitalize on unlabeled data to improve segmentation performance, these methods often overlooked the benefits of dual-sequence magnetic resonance imaging (MRI). In the present study, we incorporated self-supervised learning with a saliency transformation module using unlabeled dual-sequence MRI for accurate NPC segmentation. 44 labeled and 72 unlabeled patients were collected to develop and evaluate our network. Impressively, our network achieved a mean Dice similarity coefficient (DSC) of 0.77, which is consistent with a previous study that relied on a training set of 4,100 annotated cases. The results further revealed that our approach required minimal adjustments, primarily < 20% tweak in the DSC, to meet clinical standards. By enhancing the automatic segmentation of NPC, our method alleviates the annotation burden on oncologists, curbs subjectivity, and ensures reliable NPC delineation.
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来源期刊
CiteScore
10.70
自引率
3.50%
发文量
71
审稿时长
26 days
期刊介绍: The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.
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