MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data

By Quande Liu, Qi-Yan Dou, Lequan Yu, P. Heng. IEEE Transactions on Medical Imaging, 2020

356 citations recorded by the source corpus.

Automated prostate segmentation in MRI is highly demanded for computer-assisted diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress in this task, usually relying on large amounts of training data. Due to the nature of scarcity for medical images, it is important to effectively aggregate data from multiple sites for robust model training, to alleviate the insufficiency of single-site samples. However, the prostate MRIs from different sites present heterogeneity due to the differences in scanners and imaging protocols, raising challenges for effective ways of aggregating multi-site data for network training. In this paper, we propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations, leveraging multiple sources of data. To compensate for the inter-site heterogeneity of different MRI datasets, we develop Domain-Specific Batch Normalization layers in the network backbone, enabling the network to estimate statistics and perform feature normalization for each site separately. Considering the difficulty of capturing the shared knowledge from multiple datasets, a novel learning paradigm, i.e., Multi-site-guided Knowledge Transfer, is proposed to enhance the kernels to extract more generic representations from multi-site data. Extensive experiments on three heterogeneous prostate MRI datasets demonstrate that our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/adb0dc3ebcedfc298518e8f373b2727b9089ef55

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Citations in the corpus

Domain Generalization: A Survey (2021, background): “cation [23], [35], but also in other domains such as speech recognition [40], natural language processing [34], medical imaging [41], [42], and reinforcement learning [23].”
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization (2021, methodology): “Here, an effective approach in practice is utilizing Batch Normalization (BN) (Ioffe & Szegedy, 2015): recent work has proposed BN as a tool to mitigate domain shifts in domain adaptation tasks with promising results achieved (Li et al., 2016; Liu et al., 2020; Chang et al., 2019).”
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space (2021, methodology): “We further collect prostate T2-weighted MRI images from 6 different data sources partitioned from the public datasets [2, 21, 31, 33] for prostate MRI segmentation task.”
MedViT: A Robust Vision Transformer for Generalized Medical Image Classification (2023, background): “In addition, variations in epidemiology at different sites could impact the distribution of ground truth labels between various populations [10, 11].”