ITKIT简化了CT图像分析流程，让新手也能快速上手。
ITKIT: Feasible CT Image Analysis based on SimpleITK and MMEngine
- 基于SimpleITK和MMEngine构建轻量级流程，支持从DICOM到3D分割的完整分析
- 12项实验验证可满足多数基础场景需求，适合低算力环境使用
- 提供命令行与灵活配置双入口，兼顾初学者与高级用户
CT影像在临床诊断与治疗中广泛应用，其数据已形成以DICOM为标准的通用格式。该格式清晰易用，可高效支持深度学习等数据驱动分析方法。过去十年间，开源社区涌现出众多医学图像分析框架。ITKIT通过对这些框架特性的分析，旨在提供更易用且可配置性更强的解决方案。它提供从DICOM到3D分割推理的完整流程，核心流程仅包含必要步骤，使计算能力较弱的用户也能通过文档指导，借助命令行界面快速上手。对进阶用户，提供OneDL-MMEngine框架，支持灵活模型配置与部署。本文通过12项典型实验验证，ITKIT能有效满足大多数基础应用场景的需求。
原文摘要 · Abstract (English)
CT images are widely used in clinical diagnosis and treatment, and their data have formed a de facto standard - DICOM. It is clear and easy to use, and can be efficiently utilized by data-driven analysis methods such as deep learning. In the past decade, many program frameworks for medical image analysis have emerged in the open-source community. ITKIT analyzed the characteristics of these frameworks and hopes to provide a better choice in terms of ease of use and configurability. ITKIT offers a complete pipeline from DICOM to 3D segmentation inference. Its basic practice only includes some essential steps, enabling users with relatively weak computing capabilities to quickly get started using the CLI according to the documentation. For advanced users, the OneDL-MMEngine framework provides a flexible model configuration and deployment entry. This paper conducted 12 typical experiments to verify that ITKIT can meet the needs of most basic scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。