Fully Automatic Heart Segmentation Model Analysis Using Residual Multi-Dilated Recurrent Convolutional U-Net


KIPS Transactions on Computer and Communication Systems, Vol. 9, No. 2, pp. 37-44, Feb. 2020
https://doi.org/10.3745/KTCCS.2020.9.2.37, Full Text:
Keywords: Deep Learning, Artificial intelligence, Heart Segmentation, algorithm, ANN, CNN
Abstract

In this paper, we proposed that a fully automatic multi-class whole heart segmentation algorithm using deep learning. The proposed method is based on U-Net architecture which consist of recurrent convolutional block, residual multi-dilated convolutional block. The evaluation was accomplished by comparing automated analysis results of the test dataset to the manual assessment. We obtained the average DSC of 96.88%, precision of 95.60%, and recall of 97.00% with CT images. We were able to observe and analyze after visualizing segmented images using three-dimensional volume rendering method. Our experiment results show that proposed method effectively performed to segment in various heart structures. We expected that our method can help doctors and radiologist to make image reading and clinical decision.


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Cite this article
[IEEE Style]
S. H. Lim and M. S. Lee, "Fully Automatic Heart Segmentation Model Analysis Using Residual Multi-Dilated Recurrent Convolutional U-Net," KIPS Transactions on Computer and Communication Systems, vol. 9, no. 2, pp. 37-44, 2020. DOI: https://doi.org/10.3745/KTCCS.2020.9.2.37.

[ACM Style]
Sang Heon Lim and Myung Suk Lee. 2020. Fully Automatic Heart Segmentation Model Analysis Using Residual Multi-Dilated Recurrent Convolutional U-Net. KIPS Transactions on Computer and Communication Systems, 9, 2, (2020), 37-44. DOI: https://doi.org/10.3745/KTCCS.2020.9.2.37.