Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel dropout.
Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel dropout.
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DOI：
10.1088/1361-6560/accac9
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发表时间：
2023-04-25
影响因子：
3.5
通讯作者：
中科院分区：
文献类型：
--
作者：
Objective.头颈部（H&N）癌症的放射治疗依赖于原发肿瘤的准确分割。一个强大的，准确的，自动化的总肿瘤体积分割方法是保证H&N癌症治疗管理。本研究的目的是基于独立和组合的CT和FDG-PET模式开发一种新的H&N癌症深度学习分割模型。Approach.在这项研究中，我们开发了一个强大的基于深度学习的模型，利用来自CT和PET的信息。我们实现了一个具有5级编码和解码的3D U-Net架构，通过深度监督计算模型损失。我们使用了一个通道丢弃技术来模拟不同的输入方式的组合。这种技术可以防止只有一种模态可用时的潜在性能问题，从而提高模型的鲁棒性。我们通过将两种类型的卷积与不同的感受野（常规和扩张）相结合来实现集成建模，以提高对精细细节和全局信息的捕获。主要结果。我们提出的方法产生了有希望的结果，当部署在CT和PET组合上时，Dice相似系数（DSC）为0.802，当部署在CT上时，DSC为0.610，当部署在PET上时，DSC为0.750。意义通道丢弃方法的应用允许单个模型在部署在单模态图像（CT或PET）或组合模态图像（CT和PET）上时实现高性能。所提出的分割技术在临床上与来自特定模态的图像可能并不总是可用的应用相关。
Objective. Radiation therapy for head and neck (H&N) cancer relies on accurate segmentation of the primary tumor. A robust, accurate, and automated gross tumor volume segmentation method is warranted for H&N cancer therapeutic management. The purpose of this study is to develop a novel deep learning segmentation model for H&N cancer based on independent and combined CT and FDG-PET modalities. Approach. In this study, we developed a robust deep learning-based model leveraging information from both CT and PET. We implemented a 3D U-Net architecture with 5 levels of encoding and decoding, computing model loss through deep supervision. We used a channel dropout technique to emulate different combinations of input modalities. This technique prevents potential performance issues when only one modality is available, increasing model robustness. We implemented ensemble modeling by combining two types of convolutions with differing receptive fields, conventional and dilated, to improve capture of both fine details and global information. Main Results. Our proposed methods yielded promising results, with a Dice similarity coefficient (DSC) of 0.802 when deployed on combined CT and PET, DSC of 0.610 when deployed on CT, and DSC of 0.750 when deployed on PET. Significance. Application of a channel dropout method allowed for a single model to achieve high performance when deployed on either single modality images (CT or PET) or combined modality images (CT and PET). The presented segmentation techniques are clinically relevant to applications where images from a certain modality might not always be available.
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影响因子：
10.6
作者：
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通讯作者：
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影响因子：
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具有参考数据集的集成超分辨率
影响因子：
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作者：
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影响因子：
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作者：
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通讯作者：
Majid, Muhammad
DOI：
10.1007/s00259-007-0607-y
发表时间：
2008-02-01
影响因子：
9.1
作者：
Lautamaeki, Riikka;Brown, Tracy L. Y.;Bengel, Frank M.
通讯作者：
Bengel, Frank M.