Abstract
UNet++, an encoder-decoder architecture constructed based on the famous UNet, has achieved state-of-the-art results on many medical image segmentation tasks. Despite improved performance, UNet++ introduces densely connected decoding blocks, some of which, however, are redundant for a specific task. In this paper, we propose \(\alpha \)-UNet++ that allows us to automatically identify and discard redundant decoding blocks without the loss of precision. To this end, we design an auxiliary indicator function layer to compress the network architecture via removing a decoding block, in which all individual responses are less than a given threshold \(\alpha \). We evaluated the segmentation architecture obtained respectively for liver segmentation and nuclei segmentation, denoted by UNet++\(^C\), against UNet and UNet++. Comparing to UNet++, our UNet++\(^C\) reduces the parameters by 18.89% in liver segmentation and 34.17% in nuclei segmentation, yielding an average improvement of IoU by 0.27% and 0.11% on two tasks. Our results suggest that the UNet++\(^C\) produced by the proposed \(\alpha \)-UNet++ not only improves the segmentation accuracy slightly but also reduces the model complexity considerably.
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Acknowledgment
This work was supported in part by the Science and Technology Innovation Committee of Shenzhen Municipality, China, under Grants JCYJ20180306171334997, and in part by the National Natural Science Foundation of China under Grants 61771397. We appreciate the efforts devoted by the organizers of the Medical Segmentation Decathlon (MSD) Challenge and 2018 Data Science Bowl Segmentation Challenge to collect and share the data for comparing medical image segmentation algorithms.
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Chen, Y., Ma, B., Xia, Y. (2020). \(\alpha \)-UNet++: A Data-Driven Neural Network Architecture for Medical Image Segmentation. In: Albarqouni, S., et al. Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning. DART DCL 2020 2020. Lecture Notes in Computer Science(), vol 12444. Springer, Cham. https://doi.org/10.1007/978-3-030-60548-3_1
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DOI: https://doi.org/10.1007/978-3-030-60548-3_1
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