Head and Neck Tumor Segmentation and Outcome Prediction - Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings
Head and Neck Tumor Segmentation and Outcome Prediction - Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings
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头颈肿瘤分割和结果预测 - 第二次挑战赛，HECKTOR 2021，与 MICCAI 2021 同期举行，法国斯特拉斯堡，2021 年 9 月 27 日，会议记录
DOI：
10.1007/978-3-030-98253-9_24
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Juanco-Müller Á
中科院分区：
文献类型：
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作者：
Juanco-Müller Á
风险评估技术，特别是生存分析，对于为头颈部（H&N）癌症患者提供个性化治疗至关重要。这些技术通常依赖于计算机断层扫描（CT）和正电子发射断层扫描（PET）图像中的大体肿瘤体积（GTV）区域的准确分割。这是一项具有挑战性的任务，因为CT中的低对比度和PET中缺乏解剖信息。最近基于卷积神经网络（CNN）的方法已经证明了GTV的自动3D分割，尽管具有高内存占用（GB/epoch）。在这项工作中，我们为HECKTOR 2021挑战中的分割任务提出了一个有效的解决方案（GB/epoch）。我们通过将简单线性迭代聚类（SLIC）算法与图卷积网络相结合来分割GTV，从而在挑战测试集上获得0.63的Dice分数。此外，我们还演示了所得到的分割的形状描述符是如何在Weibull加速故障时间模型中成为相关协变量的，这导致HECKTOR 2021挑战中任务2的一致性指数为0.59。
Risk assessment techniques, in particular Survival Analysis, are crucial to provide personalised treatment to Head and Neck (H&N) cancer patients. These techniques usually rely on accurate segmentation of the Gross Tumour Volume (GTV) region in Computed Tomography (CT) and Positron Emission Tomography (PET) images . This is a challenging task due to the low contrast in CT and lack of anatomical information in PET. Recent approaches based on Convolutional Neural Networks (CNNs) have demonstrated automatic 3D segmentation of the GTV, albeit with high memory footprints (GB/epoch). In this work, we propose an efficient solution (GB/epoch) for the segmentation task in the HECKTOR 2021 challenge. We achieve this by combining the Simple Linear Iterative Clustering (SLIC) algorithm with Graph Convolution Networks to segment the GTV, resulting in a Dice score of 0.63 on the challenge test set. Furthermore, we demonstrate how shape descriptors of the resulting segmentations are relevant covariates in the Weibull Accelerated Failure Time model, which results in a Concordance Index of 0.59 for task 2 in the HECKTOR 2021 challenge.