Multi-objective Parameter Auto-tuning for Tissue Image Segmentation Workflows.
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 sh...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 3; pp. 521 - 534 |
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| Autores principales: | , , , , , , |
| Formato: | algorithm pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=136223488&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136223488 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2019 vid: 32 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136223488 136223488 136223488 10.1007/s10278-018-0138-z 136223488 ppf: 521 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multi-objective Parameter Auto-tuning for Tissue Image Segmentation Workflows. aug: au: Taveira, Luis F. R. Melo, Alba C. M. A. Teodoro, George Bremer, Erich Saltz, Joel H. Kurc, Tahsin Kong, Jun affil: Department of Computer Science, University of Brasília, Brasília, Brazil sug: subj: Tissue Radiography Image Processing, Computer Assisted Methods Workflow Automation Software Utilization Human Cell Nucleus Cell Separation Diagnostic Imaging Equipment and Supplies Computers and Computerization Economics Algorithms Image Enhancement Methods ab: 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. pubtype: Academic Journal doctype: algorithm pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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