'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images (1902.01314v2)

Abstract: Deep neural networks enable highly accurate image segmentation, but require large amounts of manually annotated data for supervised training. Few-shot learning aims to address this shortcoming by learning a new class from a few annotated support examples. We introduce, a novel few-shot framework, for the segmentation of volumetric medical images with only a few annotated slices. Compared to other related works in computer vision, the major challenges are the absence of pre-trained networks and the volumetric nature of medical scans. We address these challenges by proposing a new architecture for few-shot segmentation that incorporates 'squeeze & excite' blocks. Our two-armed architecture consists of a conditioner arm, which processes the annotated support input and generates a task-specific representation. This representation is passed on to the segmenter arm that uses this information to segment the new query image. To facilitate efficient interaction between the conditioner and the segmenter arm, we propose to use 'channel squeeze & spatial excitation' blocks - a light-weight computational module - that enables heavy interaction between both the arms with negligible increase in model complexity. This contribution allows us to perform image segmentation without relying on a pre-trained model, which generally is unavailable for medical scans. Furthermore, we propose an efficient strategy for volumetric segmentation by optimally pairing a few slices of the support volume to all the slices of the query volume. We perform experiments for organ segmentation on whole-body contrast-enhanced CT scans from the Visceral Dataset. Our proposed model outperforms multiple baselines and existing approaches with respect to the segmentation accuracy by a significant margin. The source code is available at https://github.com/abhi4ssj/few-shot-segmentation.

• The paper presents a two-armed architecture that uses a conditioner to extract task-specific representations and a segmenter for precise segmentation.
• It leverages channel squeeze & spatial excitation blocks to facilitate effective interaction between network arms without increasing model complexity.
• Experimental results on contrast-enhanced CT scans demonstrate superior segmentation accuracy compared to existing few-shot methods.

Overview of 'Squeeze {content} Excite' Guided Few-Shot Segmentation of Volumetric Images

The paper "Squeeze {content} Excite' Guided Few-Shot Segmentation of Volumetric Images" addresses a significant challenge in medical image analysis: the requirement for large amounts of annotated data with traditional deep learning approaches for image segmentation. To mitigate this, the authors present a novel few-shot learning framework specifically designed for the segmentation of volumetric medical images, such as contrast-enhanced CT scans, with only a few annotated slices.

Methodology

The primary innovation described in this work is the two-armed architecture comprising a conditioner arm and a segmenter arm. The conditioner processes an annotated support input to generate a task-specific representation. This information is then used by the segmenter arm to perform segmentation on a new query image. The network employs 'channel squeeze {content} spatial excitation' (sSE) blocks that facilitate interaction between the conditioner and segmenter arms with minimal increase in model complexity.

The paper stands out in its contribution by allowing segmentation without reliance on pre-trained models, a common challenge in medical imaging due to the scarcity of suitable prior datasets. Additionally, the authors propose a volumetric segmentation strategy to optimally pair a few slices of the support volume with all the slices of the query volume, maintaining inter-slice consistency.

Experimental Results

The authors evaluated their proposed model using whole-body contrast-enhanced CT scans from the Visceral Dataset. The model demonstrated superior segmentation accuracy compared to several baselines, including adaptations of existing few-shot learning methods that utilize feature fusion and classifier regression techniques. The proposed method did not only outperform these existing approaches but also showed robustness in handling different numbers of support slices, demonstrating an effective balance between model complexity and segmentation performance.

Key Insights and Implications

• Strong Performance without Pre-trained Models: Unlike conventional methods in computer vision that leverage pre-trained weights, this framework effectively trains from scratch, highlighting its potential in domains with limited pre-training resources.
• Robust Framework for Volumetric Data: The few-shot segmentation strategy addresses the specific challenges posed by 3D medical imaging, extending the applicability of few-shot learning paradigms beyond traditional image segmentation tasks.
• Scalability and Adaptability: By orchestrating interactions across all layers of the network rather than solely at the final layer, the architecture allows effective gradient flow and information propagation, resulting in stable performance even with complex volumetric data.

Theoretical Implications

The research contributes to the theoretical understanding of few-shot learning by reinforcing the paradigm that effective task representations can be generated and harnessed with minimal data. The integration of sSE blocks into the interaction mechanism not only shows potential for medical imaging but also suggests broader applicability in scenarios where labeled data is scarce.

Future Directions

The work opens avenues for further exploration in several domains:

• Cross-Domain Applications: Extending the utility of this approach to other medical imaging modalities, such as MRI or PET scans, could reveal insights into the adaptability of the proposed network structure.
• Integration with Reinforcement Learning: Future research could explore integrating reinforcement learning strategies to dynamically select support slices, potentially improving the framework's adaptability to varying scan qualities.
• Enhancements via Multi-Task Learning: Embedding this approach within a multi-task learning framework to concurrently learn additional tasks, such as detection or classification, could enhance the utility of medical imaging systems by reducing the demand for extensive annotated datasets.

Overall, the paper offers a comprehensive framework for few-shot segmentation in medical imaging, addressing both practical and theoretical challenges within this rapidly evolving field.