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Feasibility of Automatic Seed Generation Applied to Cardiac MRI Image Analysis

Author

Listed:
  • Radu Mărginean

    (IMOGEN Research Institute, County Clinical Emergency Hospital, 400006 Cluj-Napoca, Romania)

  • Anca Andreica

    (IMOGEN Research Institute, County Clinical Emergency Hospital, 400006 Cluj-Napoca, Romania
    Faculty of Mathematics and Computer Science, Babeş–Bolyai University, 400084 Cluj-Napoca, Romania)

  • Laura Dioşan

    (IMOGEN Research Institute, County Clinical Emergency Hospital, 400006 Cluj-Napoca, Romania
    Faculty of Mathematics and Computer Science, Babeş–Bolyai University, 400084 Cluj-Napoca, Romania)

  • Zoltán Bálint

    (IMOGEN Research Institute, County Clinical Emergency Hospital, 400006 Cluj-Napoca, Romania
    Faculty of Physics, Babeş–Bolyai University, 400084 Cluj-Napoca, Romania)

Abstract

We present a method of using interactive image segmentation algorithms to reduce specific image segmentation problems to the task of finding small sets of pixels identifying the regions of interest. To this end, we empirically show the feasibility of automatically generating seeds for GrowCut, a popular interactive image segmentation algorithm. The principal contribution of our paper is the proposal of a method for automating the seed generation method for the task of whole-heart segmentation of MRI scans, which achieves competitive unsupervised results (0.76 Dice on the MMWHS dataset). Moreover, we show that segmentation performance is robust to seeds with imperfect precision, suggesting that GrowCut-like algorithms can be applied to medical imaging tasks with little modeling effort.

Suggested Citation

  • Radu Mărginean & Anca Andreica & Laura Dioşan & Zoltán Bálint, 2020. "Feasibility of Automatic Seed Generation Applied to Cardiac MRI Image Analysis," Mathematics, MDPI, vol. 8(9), pages 1-18, September.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1511-:d:408986
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    Cited by:

    1. Ying Li & Ye Tang, 2023. "Novel Creation Method of Feature Graphics for Image Generation Based on Deep Learning Algorithms," Mathematics, MDPI, vol. 11(7), pages 1-17, March.

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