Feasibility of Automatic Seed Generation Applied to Cardiac MRI Image Analysis

Radu Mărginean, Anca Andreica, Laura Dioşan and Zoltán Bálint
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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
Laura Dioşan: IMOGEN Research Institute, County Clinical Emergency Hospital, 400006 Cluj-Napoca, Romania
Zoltán Bálint: IMOGEN Research Institute, County Clinical Emergency Hospital, 400006 Cluj-Napoca, Romania

Mathematics, 2020, vol. 8, issue 9, 1-18

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.

Keywords: region growing; cellular automata; computer vision; automatic MRI analysis; cardiac MRI segmentation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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