Continued Development and Maintenance of ITK-SNAP 3D Image Segmentation Software

该项目旨在继续开发和维护ITK-SNAP软件应用程序， 用户引导自动分割和手动注释3D体积的功能， 生物医学成像ITK-SNAP是一个免费的开源软件工具，在全球拥有大量用户。 自2006年以来，为200多份出版物撰稿， 涵盖广泛的生物医学应用和成像模式。此外，ITK-SNAP占据了 在当今成像研究人员可用的开源工具中占有独特的地位， 界面和功能专门集中在图像分割的问题。这个广泛的目标 项目是为了确保ITK-SNAP的长期可用性和可行性，面对不断增加的 成像数据集的复杂性和快速变化的软件环境;并显著扩展 这类生物医学图像分割问题可以从ITK-SNAP的自动功能中受益。 为实现这些目标，提出了五个具体目标。Aim 1将开发一个新的软件框架， 多模态和多通道成像数据的半自动分割。这一目标将扩大现有 活动轮廓分割框架，具有灵活的工具箱，用于用户引导的对象/背景生成 概率图从图像卷。该工具箱将支持纹理分析和模式分类， 以及用户生成的空间分割先验。目标2将通过以下方式提高ITK-SNAP的性能： 采用图形卡加速，并将更改内部数据结构，以允许该工具与 非常大的图像体积，如高分辨率多层CT或共聚焦显微镜产生的图像。目标3 将消除ITK-SNAP对老化且支持不足的FLTK用户界面软件库的依赖性， 而不是过渡到QT库，它有强大的社区和行业支持。这一目标还将 对ITK-SNAP的可用性进行关键改进，包括对项目范例的支持。在目标4中， 将开发基于新ITK-SNAP功能的分段协议，以解决 生物医学图像分割问题。然后将针对手动分割验证这些协议 使用公共数据集。成功的标准是实现两倍或更好的分割减少 观察者间或观察者内可靠性无损失的时间。目标5是继续支持ITK-SNAP 通过实现用户要求的功能，纠正软件中的缺陷，并提供 全面的文件，包括视频教程，以及培训和外联工作。

This project seeks to continue developing and maintaining a software application ITK-SNAP, which provides functionality for user-guided automatic segmentation and manual annotation of 3D volumes generated by biomedical imaging. ITK-SNAP is a free, open-source software tool that has a large number of users in the biomedical community (estimated in the thousands) and has contributed to over 200 publications since 2006, spanning a wide range of biomedical applications and imaging modalities. Furthermore, ITK-SNAP occupies a unique place in the spectrum of open-source tools available to today's imaging researcher, with a mature user interface and functionality specifically focused on the problem of image segmentation. The broad goals of this project are to ensure the long-term availability and viability of ITK-SNAP in the face of ever increasing complexity of imaging datasets and rapidly changing software environment; and to significantly expand the class of biomedical image segmentation problems that can benefit from the automatic features of ITK-SNAP. Five specific aims are proposed to achieve these goals. Aim 1 will develop a novel software framework for semi-automatic segmentation of multimodality and multichannel imaging data. This aim will extend the existing active contour segmentation framework with a flexible toolbox for user-guided generation of object/background probability maps from image volumes. The toolbox will support texture analysis and pattern classification, as well as user-generated spatial segmentation priors. Aim 2 will boost the performance of ITK-SNAP by employing graphics card acceleration and will change the internal data structures to allow the tool to work with very large image volumes, like those produced by high-resolution multi-slice CT or confocal microscopy. Aim 3 will remove ITK-SNAP dependencies on an aging and poorly supported FLTK user interface software library, transitioning instead to the QT library, which has strong community and industry support. This aim will also make critical improvements to ITK-SNAP usability, including support for the project paradigm. In Aim 4, segmentation protocols based on new ITK-SNAP functionality will be developed to address a diverse set of biomedical image segmentation problems. These protocols will then be validated against manual segmentation using public datasets. The criterion for success is to achieve a two-fold or better reduction in segmentation time with no penalty in inter-observer or intra-observer reliability. Aim 5 is to continue supporting the ITK-SNAP user community by implementing user-requested features, correcting defects in the software, and providing thorough documentation, including video tutorials, and training and outreach efforts.