Over the past few years, the rapid development of deep learning technologies for computer vision has significantly improved the performance of medical image segmentation (MedISeg). However, the diverse implementation strategies of various models have led to an extremely complex MedISeg system, resulting in a potential problem of unfair result comparisons. In this paper, we collect a series of MedISeg tricks for different model implementation phases (i.e., pre-training model, data pre-processing, data augmentation, model implementation, model inference, and result post-processing), and experimentally explore the effectiveness of these tricks on consistent baselines. With the extensive experimental results on both the representative 2D and 3D medical image datasets, we explicitly clarify the effect of these tricks. Moreover, based on the surveyed tricks, we also open-sourced a strong MedISeg repository, where each component has the advantage of plug-and-play. We believe that this milestone work not only completes a comprehensive and complementary survey of the state-of-the-art MedISeg approaches, but also offers a practical guide for addressing the future medical image processing challenges including but not limited to small dataset, class imbalance learning, multi-modality learning, and domain adaptation. The code and training weights have been released at: https://github.com/hust-linyi/seg_trick.

翻译：近年来，面向计算机视觉的深度学习技术快速发展，显著提升了医学图像分割（MedISeg）的性能。然而，各类模型多样化的实现策略导致MedISeg系统极为复杂，引发结果对比不公平的潜在问题。本文针对不同模型实现阶段（即预训练模型、数据预处理、数据增强、模型实现、模型推理和结果后处理）收集了一系列MedISeg技巧，并在统一基线上通过实验探究这些技巧的有效性。基于在代表性2D和3D医学图像数据集上的广泛实验结果，我们明确阐释了这些技巧的影响。此外，基于所调研的技巧，我们开源了一个强大的MedISeg仓库，其中各组件具备即插即用的优势。我们相信，这项里程碑式的工作不仅对当前最优的MedISeg方法进行了全面互补的综述，还为应对未来医学图像处理挑战（包括但不限于小数据集、类别不平衡学习、多模态学习和领域自适应）提供了实用指南。代码和训练权重已发布于：https://github.com/hust-linyi/seg_trick。