Accurate segmentation ofs organs-at-risk (OARs) in computed tomography (CT) is the key to planning treatment in radiation therapy (RT). Manually delineating OARs over hundreds of images of a typical CT scan can be time-consuming and error-prone. Deep convolutional neural networks with specific structures like U-Net have been proven effective for medical image segmentation. In this work, we propose an end-to-end deep neural network for multiorgan segmentation with higher accuracy and lower complexity. Compared with several state-of-the-art methods, the proposed accuracy-complexity adjustment module (ACAM) can increase segmentation accuracy and reduce the model complexity and memory usage simultaneously. An attention-based multiscale aggregation module (MAM) is also proposed for further improvement. Experiment results on chest CT datasets show that the proposed network achieves competitive Dice similarity coefficient results with fewer float-point operations (FLOPs) for multiple organs, which outperforms several state-of-the-art methods.

中文翻译：

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基于多尺度的可调节卷积神经网络用于多器官分割

在计算机断层扫描（CT）中准确分割危险器官（OARs）是计划放射治疗（RT）中治疗方案的关键。手动在典型CT扫描的数百幅图像上描绘OAR既费时又容易出错。具有特定结构的深卷积神经网络（如U-Net）已被证明对医学图像分割有效。在这项工作中，我们提出了一种用于多器官分割的端到端深度神经网络，具有更高的准确性和更低的复杂度。与几种最新方法相比，提出的精度复杂度调整模块（ACAM）可以提高分割精度，同时降低模型复杂度和内存使用量。还提出了一种基于注意力的多尺度聚合模块（MAM），以进行进一步的改进。