Detail publikačního výsledku

Automatic Segmentation of Myocardial Infarction in Rats Subjected to Regional Ischemia

JAKUBÍČEK, R.; CHMELÍK, J.; NECKÁŘ, J.; KOLÁŘ, R.

Originální název

Automatic Segmentation of Myocardial Infarction in Rats Subjected to Regional Ischemia

Anglický název

Automatic Segmentation of Myocardial Infarction in Rats Subjected to Regional Ischemia

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

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%.

Anglický abstrakt

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%.

Klíčová slova

image segmentation; infarct of myocard; histological image; heart; rat

Klíčová slova v angličtině

image segmentation; infarct of myocard; histological image; heart; rat

Autoři

JAKUBÍČEK, R.; CHMELÍK, J.; NECKÁŘ, J.; KOLÁŘ, R.

Rok RIV

2019

Vydáno

12.11.2018

Nakladatel

Computing in Cardiology

Místo

Maastricht, Netherlands

Kniha

Computing in Cardiology 2018

ISSN

2325-887X

Periodikum

Computing in Cardiology

Svazek

45

Číslo

1

Stát

Spojené státy americké

Strany od

1

Strany do

4

Strany počet

4

URL

http://www.cinc.org/archives/2018/pdf/CinC2018-128.pdf

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",
  booktitle="Computing in Cardiology 2018",
  year="2018",
  journal="Computing in Cardiology",
  volume="45",
  number="1",
  pages="1--4",
  publisher="Computing in Cardiology",
  address="Maastricht, Netherlands",
  doi="10.22489/CinC.2018.128",
  url="http://www.cinc.org/archives/2018/pdf/CinC2018-128.pdf"
}

Dokumenty

128_CinCFinalPDF