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S2S-ARSNet: Sequence-to-Sequence automatic renal segmentation network
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DOI：10.1016/j.bspc.2022.104121.png)
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Abstract
En 中文
Accurate segmentation of kidney contours in diuretic renography has important implications for clinical diagnosis and treatments. However, the lack of clear boundaries and high-quality images makes automatic segmentation challenging. This paper proposes a novel automatic renal segmentation network, S2S-ARSNet, combining Convolutional Long Short-Term Memory (ConvLSTM) with the Unet structure. Unet is used to learn the spatial information of each sequence, and ConvLSTM is used to discover the temporal information between sequences and automatically update the temporal state of the sequence. Moreover, an additional pre-trained Unet is applied to generate coarse masks at different times to simulate the displacement that may occur during the detection process. In this way, the spatiotemporal information is modelled, and all the information of the 3D data is fully utilized to eliminate false positives and improve the segmentation accuracy. Extensive experiments were performed on the diuretic renography dataset. The experimental results show that the proposed method can significantly enhance the kidney segmentation performance compared with other single-imagebased deep learning segmentation methods.
Keywords:
ConvLSTM
Deep learning
Diuretic renography
Sequence -to -Sequence image segmentation
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