Heart segmentation is challenging due to the poor image contrast of heart in the CT images. Since manual segmentation of the heart is tedious and time-consuming, we propose an attention based- Convolution Neural Network(CNN) for heart segmentation. First, one-hot preprocessing is performed on the multi-tissue CT images. U-Net network with Attention-gate is then applied to obtain the heart region. We compared our method with several CNN methods in terms of dice coefficient. Results show that our method outperforms other methods for segmentation.

Zhang, G., Liu, Y.u., Guo, W., Tan, W., Gong, Z., Farooq, M.A. (2022). Automatic heart segmentation based on convolutional networks using attention mechanism. SPIE [10.1117/12.2643378].

Automatic heart segmentation based on convolutional networks using attention mechanism

Muhammad Azaz Farooq
2022

Abstract

Heart segmentation is challenging due to the poor image contrast of heart in the CT images. Since manual segmentation of the heart is tedious and time-consuming, we propose an attention based- Convolution Neural Network(CNN) for heart segmentation. First, one-hot preprocessing is performed on the multi-tissue CT images. U-Net network with Attention-gate is then applied to obtain the heart region. We compared our method with several CNN methods in terms of dice coefficient. Results show that our method outperforms other methods for segmentation.
2022
Proceedings of SPIE - The International Society for Optical Engineering
1
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Zhang, G., Liu, Y.u., Guo, W., Tan, W., Gong, Z., Farooq, M.A. (2022). Automatic heart segmentation based on convolutional networks using attention mechanism. SPIE [10.1117/12.2643378].
Zhang, Guodong; Liu, Yu; Guo, Wei; Tan, Wenjun; Gong, Zhaoxuan; Farooq, Muhammad Azaz
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/1036626
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