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首页> 外文期刊>Physics in medicine and biology. >Attention-enabled 3D boosted convolutional neural networks for semantic CT segmentation using deep supervision
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Attention-enabled 3D boosted convolutional neural networks for semantic CT segmentation using deep supervision

机译：使用深度监控，支持注意力的3D推动了语义CT分段的卷积神经网络

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摘要

A deeply supervised attention-enabled boosted convolutional neural network (DAB-CNN) is presented as a superior alternative to current state-of-the-art convolutional neural networks (CNNs) for semantic CT segmentation. Spatial attention gates (AGs) were incorporated into a novel 3D cascaded CNN framework to prioritize relevant anatomy and suppress redundancies within the network. Due to the complexity and size of the network, incremental channel boosting was used to decrease memory usage and facilitate model convergence. Deep supervision was used to encourage semantically meaningful deep features and mitigate local minima traps during training. The accuracy of DAB-CNN is compared to seven architectures: a variation of U-Net (UNet), attention-enabled U-Net (A-UNet), boosted U-Net (B-UNet), deeply-supervised U-Net (D-UNet), U-Net with ResNeXt blocks (ResNeXt), life-long learning segmentation CNN (LL-CNN), and deeply supervised attention-enabled U-Net (DA-UNet). The accuracy of each method was assessed based on Dice score compared to manually delineated contours as the gold standard. One hundred and twenty patients who had definitive prostate radiotherapy were used in this study. Training, validation, and testing followed Kaggle competition rules, with 80 patients used for training, 20 patients used for internal validation, and 20 test patients used to report final accuracies. Comparatorp-values indicate that DAB-CNN achieved significantly superior Dice scores than all alternative algorithms for the prostate, rectum, and penile bulb. This study demonstrated that attention-enabled boosted convolutional neural networks (CNNs) using deep supervision are capable of achieving superior prediction accuracy compared to current state-of-the-art automatic segmentation methods.
机译：甲深深监督注意启用升压卷积神经网络（DAB-CNN）被呈现为用于语义CT分割当前状态的最先进的卷积神经网络（细胞神经网络）一种更好的选择。空间注意门（AGS）被纳入一个新的3D级联CNN框架网络中的相关解剖和抑制冗余优先。由于网络的复杂性和规模，提高使用增量通道以减少内存使用量，并促进模式接轨。深监督是用来鼓励语义上有意义深的特点和训练过程中减轻局部极小陷阱。 DAB-CNN的精度相比七架构：UNET（UNET）的变化，注意启用UNET（A-UNET），提振UNET（B-UNET），深受监督UNET （d-UNET），UNET与ResNeXt块（ResNeXt），启用重视终身学习分割CNN（LL-CNN），并深深地监督UNET（DA-UNET）。各方法的准确度是根据骰子的分数相比，手动划定轮廓的黄金标准评估。谁曾明确前列腺放疗一百二十病人使用了这项研究。培训，验证和测试其次Kaggle竞争规则，以用于培训，用于内部确认20例患者80例，20个例试验用于报告最终精度。 Comparatorp值表明，DAB-CNN实现显著优越骰子分数比对于前列腺，直肠所有替代算法，和阴茎灯泡。这项研究表明，使用深监督注意启用升压卷积神经网络（细胞神经网络）能够相对于当前状态的最先进的自动分割的方法获得优异的预测的准确性。

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