Abstract
Deep learning-based methods for mitochondria segmentation require sufficient annotations on Electron Microscopy (EM) volumes, which are often expensive and time-consuming to collect. Recently, Unsupervised Domain Adaptation (UDA) has been proposed to avoid annotating on target EM volumes by exploiting annotated source EM volumes. However, existing UDA methods for mitochondria segmentation only address the intra-section gap between source and target volumes but ignore the inter-section gap between them, which restricts the generalization capability of the learned model on target volumes. In this paper, for the first time, we propose a domain adaptive mitochondria segmentation method via enforcing inter-section consistency. The key idea is to learn an inter-section residual on the segmentation results of adjacent sections using a CNN. The inter-section residuals predicted from source and target volumes are then aligned via adversarial learning. Meanwhile, guided by the learned inter-section residual, we can generate pseudo labels to supervise the segmentation of adjacent sections inside the target volume, which further enforces inter-section consistency. Extensive experiments demonstrate the superiority of our proposed method on four representative and diverse EM datasets. Code is available at https://github.com/weih527/DA-ISC.
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Acknowledgement
This work was supported in part by the National Key R &D Program of China under Grant 2017YFA0700800, the National Natural Science Foundation of China under Grant 62021001, the University Synergy Innovation Program of Anhui Province No. GXXT-2019-025, and Anhui Provincial Natural Science Foundation under grant No. 1908085QF256.
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Huang, W., Liu, X., Cheng, Z., Zhang, Y., Xiong, Z. (2022). Domain Adaptive Mitochondria Segmentation via Enforcing Inter-Section Consistency. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13434. Springer, Cham. https://doi.org/10.1007/978-3-031-16440-8_9
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