Diseased tissue shows changes in tissue morphology, which are indicators of disease onset and progress and provide rich information with which to study disease biology at the subcellular level. Microscopic examination of whole slide tissue specimens by pathologists has long been considered a de facto standard for disease diagnosis and prognosis. We propose and experimentally evaluate a software platform that integrates a suite of methods and tools to enable automatic parameter tuning in analysis algorithms that segment nuclei in digitized images of tissue specimens fixed on glass slides, also called Whole Slide Tissue Images (WSIs). 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. These capabilities are packaged in a Docker container for easy deployment and can be used through a friendly interface extension in 3D Slicer. In addition, the methodology is developed to execute on high-performance computing systems to reduce the execution time of the parameter tuning phase. It implements several optimization methods to search the parameter space efficiently. Our software platform adjusts the parameters of a nuclear segmentation algorithm to maximize the quality of image segmentation results while minimizing the execution time. 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. Input parameters in many nucleus segmentation workflows affect segmentation accuracy and have to be tuned for optimal performance. 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. The goal of our work is to provide an approach for improving the accuracy of nucleus/cell segmentation pipelines by tuning their input parameters. We propose a software platform that integrates methods and tools for multi-objective parameter auto-tuning in tissue image segmentation workflows.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |