Abstract
This paper focuses on uterine segmentation, an important clue for understanding MRI images and medical analysis of expectant mothers, which has long been underestimated. Related works have proven that the receptive field is crucial in computer vision. However, current methods usually use pooling operations to continuously enlarge the receptive field, which leads to some inevitable information loss. In this paper, we design the Dynamic Selection Module (DSM) to effectively capture the spatial perception of medical images. Specifically, DSM adopts dynamic convolution kernel to adaptively adjust the receptive field in the horizontal and vertical directions. We then combine DSM and residual block to construct a Dynamic Residual Unit (DRU) which further learns feature representation. Then DRU is embedded in the standard U-Net termed Dynamic Selection Network (DSNet). We evaluate our method on the Uterus dataset we acquired. To validate the generalization of this method, we also do the same experiment on the Gland Segmentation dataset and Lung dataset. The results demonstrate that DSNet can significantly boost the performance of medical image segmentation than other related encoder-decoder architectures.
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This work was supported by the Artificial Intelligence Program of Shanghai under Grant 2019-RGZN-01077.
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Qin, X. et al. (2021). DSNet: Dynamic Selection Network for Biomedical Image Segmentation. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12893. Springer, Cham. https://doi.org/10.1007/978-3-030-86365-4_50
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