Taveira Luis F R, Kurc Tahsin, Melo Alba C M A, Kong Jun, Bremer Erich, Saltz Joel H, Teodoro George
Department of Computer Science, University of Brasília, Brasília, Brazil.
Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA.
J Digit Imaging. 2019 Jun;32(3):521-533. doi: 10.1007/s10278-018-0138-z.
We propose a software platform that integrates methods and tools for multi-objective parameter auto-tuning in tissue image segmentation workflows. The goal of our work is to provide an approach for improving the accuracy of nucleus/cell segmentation pipelines by tuning their input parameters. The shape, size, and texture features of nuclei in tissue are important biomarkers for disease prognosis, and accurate computation of these features depends on accurate delineation of boundaries of nuclei. Input parameters in many nucleus segmentation workflows affect segmentation accuracy and have to be tuned for optimal performance. This is a time-consuming and computationally expensive process; automating this step facilitates more robust image segmentation workflows and enables more efficient application of image analysis in large image datasets. Our software platform adjusts the parameters of a nuclear segmentation algorithm to maximize the quality of image segmentation results while minimizing the execution time. It implements several optimization methods to search the parameter space efficiently. In addition, the methodology is developed to execute on high-performance computing systems to reduce the execution time of the parameter tuning phase. These capabilities are packaged in a Docker container for easy deployment and can be used through a friendly interface extension in 3D Slicer. Our results using three real-world image segmentation workflows demonstrate that the proposed solution is able to (1) search a small fraction (about 100 points) of the parameter space, which contains billions to trillions of points, and improve the quality of segmentation output by × 1.20, × 1.29, and × 1.29, on average; (2) decrease the execution time of a segmentation workflow by up to 11.79× while improving output quality; and (3) effectively use parallel systems to accelerate parameter tuning and segmentation phases.
我们提出了一个软件平台，该平台集成了用于组织图像分割工作流程中多目标参数自动调整的方法和工具。我们工作的目标是提供一种通过调整输入参数来提高细胞核/细胞分割管道准确性的方法。组织中细胞核的形状、大小和纹理特征是疾病预后的重要生物标志物，而这些特征的准确计算取决于细胞核边界的精确勾勒。许多细胞核分割工作流程中的输入参数会影响分割准确性，必须对其进行调整以实现最佳性能。这是一个耗时且计算成本高昂的过程；自动化这一步骤有助于实现更稳健的图像分割工作流程，并能在大型图像数据集中更高效地应用图像分析。我们的软件平台会调整细胞核分割算法的参数，以在最小化执行时间的同时最大化图像分割结果的质量。它实现了几种优化方法来高效搜索参数空间。此外，该方法被开发为可在高性能计算系统上执行，以减少参数调整阶段的执行时间。这些功能被打包在一个Docker容器中以便于部署，并且可以通过3D Slicer中的友好界面扩展来使用。我们使用三个实际图像分割工作流程的结果表明，所提出的解决方案能够：（1）在包含数十亿到数万亿个点的参数空间中搜索一小部分（约100个点），平均将分割输出质量提高1.20倍、1.29倍和1.29倍；（2）在提高输出质量的同时，将分割工作流程的执行时间减少多达11.79倍；（3）有效利用并行系统来加速参数调整和分割阶段。
