Medical imaging segmentation is a highly active area of research, with deep learning-based methods achieving state-of-the-art results in several benchmarks. However, the lack of standardized tools for training, testing, and evaluating new methods makes the comparison of methods difficult. To address this, we introduce the Medical Imaging Segmentation Toolkit (MIST), a simple, modular, and end-to-end medical imaging segmentation framework designed to facilitate consistent training, testing, and evaluation of deep learning-based medical imaging segmentation methods. MIST standardizes data analysis, preprocessing, and evaluation pipelines, accommodating multiple architectures and loss functions. This standardization ensures reproducible and fair comparisons across different methods. We detail MIST's data format requirements, pipelines, and auxiliary features and demonstrate its efficacy using the BraTS Adult Glioma Post-Treatment Challenge dataset. Our results highlight MIST's ability to produce accurate segmentation masks and its scalability across multiple GPUs, showcasing its potential as a powerful tool for future medical imaging research and development.

翻译：医学影像分割是一个高度活跃的研究领域，基于深度学习的方法已在多个基准测试中取得了最先进的成果。然而，由于缺乏用于训练、测试和评估新方法的标准化工具，不同方法之间的比较变得困难。为解决这一问题，我们引入了医学影像分割工具包（MIST），这是一个简单、模块化且端到端的医学影像分割框架，旨在促进基于深度学习的医学影像分割方法在训练、测试和评估方面的一致性。MIST 标准化了数据分析、预处理和评估流程，可兼容多种网络架构和损失函数。这种标准化确保了不同方法之间可复现且公平的比较。我们详细介绍了 MIST 的数据格式要求、处理流程及辅助功能，并使用 BraTS 成人胶质瘤治疗后挑战数据集验证了其有效性。我们的结果突显了 MIST 生成精确分割掩码的能力及其在多 GPU 上的可扩展性，展示了其作为未来医学影像研究与开发强大工具的潜力。