MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data (2002.03366v2)

Abstract: 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.

• The paper introduces domain-specific batch normalization and multi-site guided knowledge transfer to enhance segmentation accuracy in heterogeneous MRI data.
• It adapts the network architecture to manage inter-site variance, outperforming both joint and separate training approaches across multiple clinical settings.
• The framework offers robust segmentation performance and paves the way for deploying generalized models in diverse real-world clinical environments.

An Overview of MS-Net: Enhancing Prostate Segmentation with Multi-Site MRI Data

The paper "MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data" addresses an essential challenge in modern medical imaging: improving the accuracy of prostate segmentation when confronted with data variants due to differences in scanning protocols and equipment across multiple sites. The authors introduce MS-Net, a specialized network structure designed to leverage the knowledge embedded in heterogeneous datasets for enhancing model robustness and segmentation accuracy.

Core Contributions and Methodology

Central to this paper is adapting the neural network architecture to manage inter-site heterogeneity effectively. The authors propose Domain-Specific Batch Normalization (DSBN) layers, which fundamentally alter the network's normalization operations by assigning a separate batch normalization layer for each dataset site. This design choice aims to address the variance in scanner-specific data distribution across different institutional sources.

Moreover, the introduction of a Multi-site-guided Knowledge Transfer (MSKT) mechanism is crucial in enhancing the network's learning capability. This strategy employs auxiliary branches, each tailored to specific datasets, to better capture unique site-specific characteristics, thus enriching the universal network's shared kernels with comprehensive multi-site knowledge. The combination of DSBN and MSKT allows MS-Net to concurrently leverage site-specific details while learning generalized features beneficial across various datasets.

Experimental Validation and Results

The experimentation is robust, involving data from three distinct clinical settings, showcasing varying resolutions and equipment types. Compared against baseline models such as Joint and Separate approaches, MS-Net consistently outperformed them in segmenting prostate MRI scans. The authors present comparative evaluations against state-of-the-art methods in multi-domain learning, where MS-Net demonstrates superior efficacy, underscoring the potential of their architecture in handling domain shift in medical imaging contexts.

Theoretical and Practical Implications

From a theoretical standpoint, MS-Net offers a promising framework for addressing domain heterogeneity in medical imaging, potentially extending beyond prostate MRI segmentation. The method showcases a feasible approach to blend domain-specific tuning with broader, cross-domain representation learning, a tactic that could be extrapolated to other areas where data variability is prominent.

Practically, MS-Net’s architecture provides a pathway for enhancing model deployment in real-world clinical environments, where data diversity is a norm. Its ability to reconcile inter-site heterogeneity and improve segmentation accuracy reduces the need for developing bespoke models for individual datasets. This principle not only enhances computational efficiency but also broadens the utility of centralized, high-performing models in diverse medical settings.

Future Directions in AI and Medical Imaging

The research invites further exploration into life-long, self-adjusting models that retain effectiveness across new, unseen data domains without extensive retraining phases. Additionally, there remains fertile ground for integrating such approaches with unsupervised and semi-supervised learning paradigms to robustly adapt to evolving data landscapes. Further investigating feature calibration techniques or fine-tuning algorithms may offer additional refinements to enhance cross-domain generalization capabilities.

In summary, the MS-Net framework presents a significant advancement in the domain of medical image segmentation, skillfully addressing the challenges imposed by data heterogeneity across different clinical sites. The methodology serves as a foundation for developing AI-driven solutions capable of operating with high accuracy across diverse datasets, thus broadening their applicability in clinical practice.