Coronary Artery Vascular Segmentation on Limited Data via Pseudo-Precise Label

The scale of training data is significant in segmentation task, especially in segmenting the medical coronary artery angiograms. Traditional semantic segmentation networks have been restricted in this field, due to the particularity of cardiac coronary angiography data, that is, it is very difficult...

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Bibliographic Details
Published in2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2019; pp. 816 - 819
Main Authors Zhai, Mo, Du, Tianming, Yang, Ruolin, Zhang, Honggang
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.07.2019
Subjects
Arteries
Convolution
Coronary Angiography
Coronary Vessels
Heart
Image segmentation
Labeling
Manuals
Silicon
Training
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Summary:The scale of training data is significant in segmentation task, especially in segmenting the medical coronary artery angiograms. Traditional semantic segmentation networks have been restricted in this field, due to the particularity of cardiac coronary angiography data, that is, it is very difficult to balance the manual labeling costs and network accuracy. On the basis of these observations, we propose a new method to generate the so-called 'pseudo-precise' label and a complementary training pipeline, which can improve the performance of the networks on the premise of reducing labor costs as much as possible. Our method can thus increase the f1-score by 4%-11% with the same amount of precisely labeled data.
ISSN:1557-170X
1558-4615
DOI:10.1109/EMBC.2019.8856682