Capsules for Object Segmentation (1804.04241v1)

Abstract: Convolutional neural networks (CNNs) have shown remarkable results over the last several years for a wide range of computer vision tasks. A new architecture recently introduced by Sabour et al., referred to as a capsule networks with dynamic routing, has shown great initial results for digit recognition and small image classification. The success of capsule networks lies in their ability to preserve more information about the input by replacing max-pooling layers with convolutional strides and dynamic routing, allowing for preservation of part-whole relationships in the data. This preservation of the input is demonstrated by reconstructing the input from the output capsule vectors. Our work expands the use of capsule networks to the task of object segmentation for the first time in the literature. We extend the idea of convolutional capsules with locally-connected routing and propose the concept of deconvolutional capsules. Further, we extend the masked reconstruction to reconstruct the positive input class. The proposed convolutional-deconvolutional capsule network, called SegCaps, shows strong results for the task of object segmentation with substantial decrease in parameter space. As an example application, we applied the proposed SegCaps to segment pathological lungs from low dose CT scans and compared its accuracy and efficiency with other U-Net-based architectures. SegCaps is able to handle large image sizes (512 x 512) as opposed to baseline capsules (typically less than 32 x 32). The proposed SegCaps reduced the number of parameters of U-Net architecture by 95.4% while still providing a better segmentation accuracy.

An Expert Overview of "Capsules for Object Segmentation"

The paper "Capsules for Object Segmentation" by Rodney LaLonde and Ulas Bagci introduces a novel application of capsule networks for the task of object segmentation, specifically targeting pathological lung segmentation from CT scans. This paper is significant as it pioneers the use of capsule networks for segmentation tasks, which has predominantly been the domain of convolutional neural networks (CNNs).

Key Contributions

The authors extend the concepts of capsule networks, originally introduced by Sabour et al., to a segmentation task, which is a notable departure from prior applications that focused primarily on small image classification tasks. The key contributions of the paper are as follows:

1. SegCaps Architecture: The proposed segmentation capsule network, SegCaps, integrates convolutional and deconvolutional capsules with locally-connected routing. This architecture adapts the notion of part-whole relationships inherent in capsule networks to segment images effectively.
2. Efficiency and Performance: SegCaps demonstrates a substantial reduction in the parameter space compared to established models like U-Net, achieving a 95.4% reduction in parameters while maintaining improved segmentation accuracy. This efficiency highlights the potential of capsule networks in applications where computational resources are constrained.
3. Computational Improvements: The authors provide two key innovations to manage the computational expense typical of capsule networks:
   • Locally-constrained routing limits the connections between capsules to a localized region, thereby reducing computational overhead.
   • Shared transformation matrices across spatial locations within capsule types further optimize the memory and computational demands.
4. Handling Large Image Sizes: Traditional capsule networks were limited to small input sizes, but SegCaps handles images as large as 512x512 pixels, expanding the applicability of capsules to realistic medical imaging datasets.

Empirical Evaluation

The experimental evaluation focuses on the pathological lung segmentation task using the LUNA16 dataset. SegCaps outperforms U-Net and Tiramisu in terms of the Dice coefficient, while requiring significantly fewer parameters. This is particularly impressive given the complexity and variability associated with lung pathologies evident in CT data.

Theoretical and Practical Implications

Theoretically, the paper extends the scope of capsule networks by providing evidence that they can capture spatial hierarchies effectively in object segmentation when appropriately adapted. Practically, the reduction in parameters without sacrificing accuracy makes SegCaps a viable option for medical imaging applications, where computational resources may be limited and precision is critical.

Future Directions

The promising results of SegCaps suggest various future research directions. Exploring the integration of capsule networks with other modalities of medical imaging could validate the generalizability of these findings. Additionally, further optimization of dynamic routing algorithms could enhance the efficacy of segmentation outcomes. There is also scope for investigating the potential for real-time applications of SegCaps in clinical settings, particularly for tasks requiring immediate analysis.

In conclusion, "Capsules for Object Segmentation" marks a significant step towards leveraging capsule networks for complex segmentation problems, offering both a theoretical framework and practical benefits for computational efficiency and accuracy in the field of medical imaging.