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.Comment: Under submissio