利用三维密集 U-Net 在霍奇金淋巴瘤患者的 18F-FDG PET/CT 成像中自动检测和分割病灶。

IF 1.3 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Nuclear Medicine Communications Pub Date : 2024-11-01 Epub Date: 2024-10-08 DOI:10.1097/MNM.0000000000001892
Mohammad Amin Izadi, Nafiseh Alemohammad, Parham Geramifar, Ali Salimi, Zeinab Paymani, Roya Eisazadeh, Rezvan Samimi, Babak Nikkholgh, Zaynab Sabouri
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引用次数: 0

摘要

目的:PET/CT 图像中肿瘤自动分割的准确性对于有效治疗和监测霍奇金淋巴瘤至关重要。本研究旨在解决某些分割算法因 PET 图像分辨率和肿瘤异质性而在准确区分淋巴瘤和正常器官摄取方面所面临的挑战:编码器-解码器架构的变体是最先进的图像分割模型。在这些架构中,U-Net 是最著名和最主要的医学图像分割架构之一。在本研究中,我们提出了一种用于霍奇金淋巴瘤分割的全自动方法,该方法结合了 U-Net 和 DenseNet 体系结构,使用 Tversky 损失函数对其进行训练,以减少极小病灶的网络损失。假设这两种深度学习模型的融合可以提高霍奇金淋巴瘤分割的准确性和鲁棒性。我们使用了一个包含 141 个样本的数据集来训练我们提出的网络。同时,为了测试和评估所提出的网络,我们分别分配了两个包含 20 个样本的数据集:我们的 Dice 相似度系数平均值为 0.759,中位值为 0.767,四分位数范围为 0.647-0.837。测试图像的基本真实值与预测体积之间的一致性很好,精确度和召回分数分别为 0.798 和 0.763:本研究表明,与类似研究相比,U-Net 和 DenseNet 架构与 Tversky 损失函数的整合能显著提高 PET/CT 图像中霍奇金淋巴瘤分割的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic detection and segmentation of lesions in 18 F-FDG PET/CT imaging of patients with Hodgkin lymphoma using 3D dense U-Net.

Objective: The accuracy of automatic tumor segmentation in PET/computed tomography (PET/CT) images is crucial for the effective treatment and monitoring of Hodgkin lymphoma. This study aims to address the challenges faced by certain segmentation algorithms in accurately differentiating lymphoma from normal organ uptakes due to PET image resolution and tumor heterogeneity.

Materials and methods: Variants of the encoder-decoder architectures are state-of-the-art models for image segmentation. Among these kinds of architectures, U-Net is one of the most famous and predominant for medical image segmentation. In this study, we propose a fully automatic approach for Hodgkin lymphoma segmentation that combines U-Net and DenseNet architectures to reduce network loss for very small lesions, which is trained using the Tversky loss function. The hypothesis is that the fusion of these two deep learning models can improve the accuracy and robustness of Hodgkin lymphoma segmentation. A dataset with 141 samples was used to train our proposed network. Also, to test and evaluate the proposed network, we allocated two separate datasets of 20 samples.

Results: We achieved 0.759 as the mean Dice similarity coefficient with a median value of 0.767, and interquartile range (0.647-0.837). A good agreement was observed between the ground truth of test images against the predicted volume with precision and recall scores of 0.798 and 0.763, respectively.

Conclusion: This study demonstrates that the integration of U-Net and DenseNet architectures, along with the Tversky loss function, can significantly enhance the accuracy of Hodgkin lymphoma segmentation in PET/CT images compared to similar studies.

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来源期刊
CiteScore
2.20
自引率
6.70%
发文量
212
审稿时长
3-8 weeks
期刊介绍: Nuclear Medicine Communications, the official journal of the British Nuclear Medicine Society, is a rapid communications journal covering nuclear medicine and molecular imaging with radionuclides, and the basic supporting sciences. As well as clinical research and commentary, manuscripts describing research on preclinical and basic sciences (radiochemistry, radiopharmacy, radiobiology, radiopharmacology, medical physics, computing and engineering, and technical and nursing professions involved in delivering nuclear medicine services) are welcomed, as the journal is intended to be of interest internationally to all members of the many medical and non-medical disciplines involved in nuclear medicine. In addition to papers reporting original studies, frankly written editorials and topical reviews are a regular feature of the journal.
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