In recent years, there were many suggestions regarding modifications of the well-known U-Net architecture in order to improve its performance. The central motivation of this work is to provide a fair comparison of U-Net and its five extensions using identical conditions to disentangle the influence of model architecture, model training, and parameter settings on the performance of a trained model. For this purpose each of these six segmentation architectures is trained on the same nine data sets. The data sets are selected to cover various imaging modalities (X-rays, computed tomography, magnetic resonance imaging), single- and multi-class segmentation problems, and single- and multi-modal inputs. During the training, it is ensured that the data preprocessing, data set split into training, validation, and testing subsets, optimizer, learning rate change strategy, architecture depth, loss function, supervision and inference are exactly the same for all the architectures compared. Performance is evaluated in terms of Dice coefficient, surface Dice coefficient, average surface distance, Hausdorff distance, training, and prediction time. The main contribution of this experimental study is demonstrating that the architecture variants do not improve the quality of inference related to the basic U-Net architecture while resource demand rises.

中文翻译：

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医学图像分割深度架构的基准测试


近年来，有许多关于修改著名的 U-Net 架构以提高其性能的建议。这项工作的核心动机是使用相同的条件对 U-Net 及其五个扩展进行公平的比较，以理清模型架构、模型训练和参数设置对训练模型性能的影响。为此，这六种分割架构中的每一种都在相同的九个数据集上进行训练。选择的数据集涵盖各种成像模式（X 射线、计算机断层扫描、磁共振成像）、单类和多类分割问题以及单模态和多模态输入。在训练过程中，确保所有比较的架构的数据预处理、数据集分割为训练、验证和测试子集、优化器、学习率改变策略、架构深度、损失函数、监督和推理完全相同。性能根据 Dice 系数、表面 Dice 系数、平均表面距离、Hausdorff 距离、训练和预测时间进行评估。这项实验研究的主要贡献是证明，当资源需求增加时，架构变体不会提高与基本 U-Net 架构相关的推理质量。