Detail publikace

Automatic Segmentation of Myocardial Infarction in Rats Subjected to Regional Ischemia

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

Jazyk

en

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%.

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"
}