LF-SynthSeg: Label-Free Brain Tissue-Assisted Tumor Synthesis and Segmentation.

IF 6.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Pengxiao Xu, Junyan Lyu, Li Lin, Pujin Cheng, Xiaoying Tang
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引用次数: 0

Abstract

Unsupervised brain tumor segmentation is pivotal in realms of disease diagnosis, surgical planning, and treatment response monitoring, with the distinct advantage of obviating the need for labeled data. Traditional methodologies in this domain, however, often fall short in fully capitalizing on the extensive prior knowledge of brain tissue, typically approaching the task merely as an anomaly detection challenge. In our research, we present an innovative strategy that effectively integrates brain tissues' prior knowledge into both the synthesis and segmentation of brain tumor from T2-weighted Magnetic Resonance Imaging scans. Central to our method is the tumor synthesis mechanism, employing randomly generated ellipsoids in conjunction with the intensity profiles of brain tissues. This methodology not only fosters a significant degree of variation in the tumor presentations within the synthesized images but also facilitates the creation of an essentially unlimited pool of abnormal T2-weighted images. These synthetic images closely replicate the characteristics of real tumor-bearing scans. Our training protocol extends beyond mere tumor segmentation; it also encompasses the segmentation of brain tissues, thereby directing the networkâs attention to the boundary relationship between brain tumor and brain tissue, thus improving the robustness of our method. We evaluate our approach across five widely recognized public datasets (BRATS 2019, BRATS 2020, BRATS 2021, PED and SSA), and the results show that our method outperforms state-of-the-art unsupervised tumor segmentation methods by large margins. Moreover, the proposed method achieves more than 92 % of the fully supervised performance on the same testing datasets.

LF-SynthSeg:无标记脑组织辅助肿瘤合成与分割。
无监督脑肿瘤分割在疾病诊断、手术规划和治疗反应监测领域至关重要,其明显优势是无需标记数据。然而,该领域的传统方法往往不能充分利用脑组织的大量先验知识,通常只是将该任务作为异常检测挑战来处理。在我们的研究中,我们提出了一种创新策略,它能有效地将脑组织的先验知识整合到 T2 加权磁共振成像扫描的脑肿瘤合成和分割中。我们方法的核心是肿瘤合成机制,采用随机生成的椭圆体与脑组织的强度曲线相结合。这种方法不仅能使合成图像中的肿瘤表现形式有很大程度的变化,还能帮助创建一个基本不受限制的 T2 加权异常图像库。这些合成图像紧密复制了真实肿瘤扫描图像的特征。我们的训练方案不仅包括肿瘤分割,还包括脑组织分割,从而引导网络关注脑肿瘤和脑组织之间的边界关系,从而提高我们方法的鲁棒性。我们在五个广受认可的公共数据集(BRATS 2019、BRATS 2020、BRATS 2021、PED 和 SSA)上对我们的方法进行了评估,结果表明我们的方法在很大程度上优于最先进的无监督肿瘤分割方法。此外,在相同的测试数据集上,所提出的方法达到了完全监督性能的 92% 以上。
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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
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