Abstract
CNN-based methods for WSI segmentation are time-consuming under the limits of communication bandwidth and memory usage, due to the high pixel resolution of WSIs. In this paper, we propose a novel framework for accelerating the segmentation of digital histopathology WSIs in the frequency domain. Based on the characteristics of the JPEG format in data storage and transmission on the existing digital histopathological diagnosis cloud platform, we extract DCT coefficients from the JEPG decoding and compress them into the DCT feature cubes by a frequency selection block. Based on the DCT feature cubes, we propose an extremely light-weighted model named Efficient DCT-Network (EDCT-Net). The size of the input data, as well as the bandwidth requirement for CPU-GPU transmitting, for EDCT-net reduces by 96% compared to the common CNN-based methods. And, the number of model parameters and the floating-point operations (FLOPs) for EDCT-Net decreases by 98% and 94% compared to the baseline method. The experimental results have demonstrated that our method achieves a Dice score of 0.811 with only 8 frequency channels in the task of endometrial histopathology WSI segmentation, which is comparable with state-of-the-art methods.
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Acknowledgment
This work was partly supported by the National Natural Science Foundation of China (Grant No. 61771031, 61901018, and 61906058), partly by China Postdoctoral Science Foundation (No. 2019M650446) and partly by Tianjin Science and Technology Major Project (Grant No. 18ZXZNSY00260).
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Luo, W., Zheng, Y., Hu, D., Li, J., Xue, C., Jiang, Z. (2021). Frequency-Based Convolutional Neural Network for Efficient Segmentation of Histopathology Whole Slide Images. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12889. Springer, Cham. https://doi.org/10.1007/978-3-030-87358-5_47
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