PDF(Pubmed)
Abstract：
OBJECTIVE: Deep learning-based auto-segmentation algorithms can improve clinical workflow by defining accurate regions of interest while reducing manual labor. Over the past decade, convolutional neural networks (CNNs) have become prominent in medical image segmentation applications. However, CNNs have limitations in learning long-range spatial dependencies due to the locality of the convolutional layers. Transformers were introduced to address this challenge. In transformers with self-attention mechanism, even the first layer of information processing makes connections between distant image locations. Our paper presents a novel framework that bridges these two unique techniques, CNNs and transformers, to segment the gross tumor volume (GTV) accurately and efficiently in computed tomography (CT) images of non-small cell-lung cancer (NSCLC) patients.
METHODS: Under this framework, input of multiple resolution images was used with multi-depth backbones to retain the benefits of high-resolution and low-resolution images in the deep learning architecture. Furthermore, a deformable transformer was utilized to learn the long-range dependency on the extracted features. To reduce computational complexity and to efficiently process multi-scale, multi-depth, high-resolution 3D images, this transformer pays attention to small key positions, which were identified by a self-attention mechanism. We evaluated the performance of the proposed framework on a NSCLC dataset which contains 563 training images and 113 test images. Our novel deep learning algorithm was benchmarked against five other similar deep learning models.
RESULTS: The experimental results indicate that our proposed framework outperforms other CNN-based, transformer-based, and hybrid methods in terms of Dice score (0.92) and Hausdorff Distance (1.33). Therefore, our proposed model could potentially improve the efficiency of auto-segmentation of early-stage NSCLC during the clinical workflow. This type of framework may potentially facilitate online adaptive radiotherapy, where an efficient auto-segmentation workflow is required.
CONCLUSIONS: Our deep learning framework, based on CNN and transformer, performs auto-segmentation efficiently and could potentially assist clinical radiotherapy workflow.
METHODS: Under this framework, input of multiple resolution images was used with multi-depth backbones to retain the benefits of high-resolution and low-resolution images in the deep learning architecture. Furthermore, a deformable transformer was utilized to learn the long-range dependency on the extracted features. To reduce computational complexity and to efficiently process multi-scale, multi-depth, high-resolution 3D images, this transformer pays attention to small key positions, which were identified by a self-attention mechanism. We evaluated the performance of the proposed framework on a NSCLC dataset which contains 563 training images and 113 test images. Our novel deep learning algorithm was benchmarked against five other similar deep learning models.
RESULTS: The experimental results indicate that our proposed framework outperforms other CNN-based, transformer-based, and hybrid methods in terms of Dice score (0.92) and Hausdorff Distance (1.33). Therefore, our proposed model could potentially improve the efficiency of auto-segmentation of early-stage NSCLC during the clinical workflow. This type of framework may potentially facilitate online adaptive radiotherapy, where an efficient auto-segmentation workflow is required.
CONCLUSIONS: Our deep learning framework, based on CNN and transformer, performs auto-segmentation efficiently and could potentially assist clinical radiotherapy workflow.
摘要：
目的：基于深度学习的自动分割算法可以通过定义准确的感兴趣区域来改善临床工作流程，同时减少体力劳动。在过去的十年里，卷积神经网络(CNN)在医学图像分割应用中已经变得突出。然而,由于卷积层的局部性,CNN在学习远程空间依赖性方面具有局限性。引入变形金刚来应对这一挑战。在具有自我注意机制的变压器中，即使是第一层的信息处理也会在遥远的图像位置之间建立连接。我们的论文提出了一个新颖的框架，将这两种独特的技术连接起来，CNN和变压器，在非小细胞肺癌（NSCLC）患者的计算机断层扫描（CT）图像中准确有效地分割肿瘤体积（GTV）。
方法：在此框架下，多分辨率图像的输入与多深度主干一起使用，以保留深度学习架构中高分辨率和低分辨率图像的优势。此外，使用可变形变压器来学习对提取特征的长期依赖性。为了降低计算复杂性并有效地处理多尺度，多深度,高分辨率3D图像，这种变压器注重小的关键位置，这是通过自我注意机制确定的。我们评估了所提出的框架在包含563个训练图像和113个测试图像的NSCLC数据集上的性能。我们新颖的深度学习算法以其他五个类似的深度学习模型为基准。
结果：实验结果表明，我们提出的框架优于其他基于CNN的框架，基于变压器，和混合方法在骰子得分(0.92)和豪斯多夫距离(1.33)方面。因此,我们提出的模型可能会提高临床工作流程中早期NSCLC自动分割的效率.这种类型的框架可能会促进在线自适应放射治疗，其中需要有效的自动分段工作流程。
结论：我们的深度学习框架，基于CNN和变压器，有效地执行自动分割，并可能有助于临床放射治疗工作流程。
方法：在此框架下，多分辨率图像的输入与多深度主干一起使用，以保留深度学习架构中高分辨率和低分辨率图像的优势。此外，使用可变形变压器来学习对提取特征的长期依赖性。为了降低计算复杂性并有效地处理多尺度，多深度,高分辨率3D图像，这种变压器注重小的关键位置，这是通过自我注意机制确定的。我们评估了所提出的框架在包含563个训练图像和113个测试图像的NSCLC数据集上的性能。我们新颖的深度学习算法以其他五个类似的深度学习模型为基准。
结果：实验结果表明，我们提出的框架优于其他基于CNN的框架，基于变压器，和混合方法在骰子得分(0.92)和豪斯多夫距离(1.33)方面。因此,我们提出的模型可能会提高临床工作流程中早期NSCLC自动分割的效率.这种类型的框架可能会促进在线自适应放射治疗，其中需要有效的自动分段工作流程。
结论：我们的深度学习框架，基于CNN和变压器，有效地执行自动分割，并可能有助于临床放射治疗工作流程。
