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Open AccessArticle

Feasibility of Automatic Seed Generation Applied to Cardiac MRI Image Analysis

1
IMOGEN Research Institute, County Clinical Emergency Hospital, 400006 Cluj-Napoca, Romania
2
Faculty of Mathematics and Computer Science, Babeş–Bolyai University, 400084 Cluj-Napoca, Romania
3
Faculty of Physics, Babeş–Bolyai University, 400084 Cluj-Napoca, Romania
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(9), 1511; https://doi.org/10.3390/math8091511
Received: 20 July 2020 / Revised: 29 August 2020 / Accepted: 1 September 2020 / Published: 4 September 2020
(This article belongs to the Special Issue Recent Advances in Data Mining and Their Applications)
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. View Full-Text
Keywords: region growing; cellular automata; computer vision; automatic MRI analysis; cardiac MRI segmentation region growing; cellular automata; computer vision; automatic MRI analysis; cardiac MRI segmentation
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MDPI and ACS Style

Mărginean, R.; Andreica, A.; Dioşan, L.; Bálint, Z. Feasibility of Automatic Seed Generation Applied to Cardiac MRI Image Analysis. Mathematics 2020, 8, 1511.

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