Publication detail
Automatic Segmentation of Myocardial Infarction in Rats Subjected to Regional Ischemia
JAKUBÍČEK, R. CHMELÍK, J. NECKÁŘ, J. KOLÁŘ, R.
Original Title
Automatic Segmentation of Myocardial Infarction in Rats Subjected to Regional Ischemia
English Title
Automatic Segmentation of Myocardial Infarction in Rats Subjected to Regional Ischemia
Type
conference paper
Language
en
Original Abstract
The experimental and preclinical studies of ischemia and reperfusion on animal models usually evaluate the sizes of area at risk (AR) of infarction and infarct area (IA) as fundamental parameters. The authors usually don’t provide any detailed information about the image processing of their data, though the IA or AR segmentation is often challenging and prone to be expert-depending. Here, we describe a new approach for automatic IA and AR segmentation based on combination of Random Forest classifier and two-step pixel-wise k-means classification of image pixels. The evaluation has been performed on the set of 16 images from 8 rat hearts. We compared sizes of normal perfused tissues, viable area and IA (normalized to percentage of total area) obtained by our method with manually segmentation by biologist. We achieved mean absolute error of 2.59% with mean standard deviation of 1.61%.
English abstract
The experimental and preclinical studies of ischemia and reperfusion on animal models usually evaluate the sizes of area at risk (AR) of infarction and infarct area (IA) as fundamental parameters. The authors usually don’t provide any detailed information about the image processing of their data, though the IA or AR segmentation is often challenging and prone to be expert-depending. Here, we describe a new approach for automatic IA and AR segmentation based on combination of Random Forest classifier and two-step pixel-wise k-means classification of image pixels. The evaluation has been performed on the set of 16 images from 8 rat hearts. We compared sizes of normal perfused tissues, viable area and IA (normalized to percentage of total area) obtained by our method with manually segmentation by biologist. We achieved mean absolute error of 2.59% with mean standard deviation of 1.61%.
Keywords
image segmentation; infarct of myocard; histological image; heart; rat
Released
12.11.2018
Publisher
Computing in Cardiology
Location
Maastricht, Netherlands
Pages from
1
Pages to
4
Pages count
4
URL
Documents
BibTex
@inproceedings{BUT151065,
author="Roman {Jakubíček} and Jiří {Chmelík} and Jan {Neckář} and Radim {Kolář}",
title="Automatic Segmentation of Myocardial Infarction in Rats Subjected to Regional Ischemia",
annote="The experimental and preclinical studies of ischemia and reperfusion on animal models usually evaluate the sizes of area at risk (AR) of infarction and infarct area (IA) as fundamental parameters. The authors usually don’t provide any detailed information about the image processing of their data, though the IA or AR segmentation is often challenging and prone to be expert-depending. Here, we describe a new approach for automatic IA and AR segmentation based on combination of Random Forest classifier and two-step pixel-wise k-means classification of image pixels. The evaluation has been performed on the set of 16 images from 8 rat hearts. We compared sizes of normal perfused tissues, viable area and IA (normalized to percentage of total area) obtained by our method with manually segmentation by biologist. We achieved mean absolute error of 2.59% with mean standard deviation of 1.61%.",
address="Computing in Cardiology",
booktitle="Computing in Cardiology 2018",
chapter="151065",
doi="10.22489/CinC.2018.128",
howpublished="online",
institution="Computing in Cardiology",
number="1",
year="2018",
month="november",
pages="1--4",
publisher="Computing in Cardiology",
type="conference paper"
}