Abstract
Lumber paraspinal muscles (LPM) segmentation is of essential importance in predicting response to treatment of low back pain. To date, all LPM segmentation methods are manually based instead of automatic. Manual segmentation of LPM requires vast radiological knowledge and experience. Moreover, the manual segmentation usually induces subjective variance. Therefore, an automatic segmentation is desireable. It is challenging to achieve automatic segmentation mainly because the ambiguous boundary of the LPM can be very difficult to locate. In this paper, we present a novel encoder-decoder and attention based deep convolutional neural network (CNN) to address this problem. With the help of skip connections, the encoder-decoder structure can capture both shadow and deep features which represent local and global information. Pre-trained VGG11 in ImageNet performed as encoder. In the decoder part, an attention block is applied to recalibrate the input feature. With the help of attention block, meaningful features are highlighted while irrelevant features are suppressed. To fully evaluate the performance of our proposed network, we construct the first large-scale LPM segmentation dataset with 1080 images and its segmentation masks. Experimental results show that our proposed network can not only achieve a good LPM segmentation result with a high dice score of 0.94 but also outperforms other state-of-the-art segmentation methods.
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Zhang, Y. et al. (2019). LumNet: A Deep Neural Network for Lumbar Paraspinal Muscles Segmentation. In: Liu, J., Bailey, J. (eds) AI 2019: Advances in Artificial Intelligence. AI 2019. Lecture Notes in Computer Science(), vol 11919. Springer, Cham. https://doi.org/10.1007/978-3-030-35288-2_46
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