The Importance of Skip Connections in Biomedical Image Segmentation (1608.04117v2)

Abstract: In this paper, we study the influence of both long and short skip connections on Fully Convolutional Networks (FCN) for biomedical image segmentation. In standard FCNs, only long skip connections are used to skip features from the contracting path to the expanding path in order to recover spatial information lost during downsampling. We extend FCNs by adding short skip connections, that are similar to the ones introduced in residual networks, in order to build very deep FCNs (of hundreds of layers). A review of the gradient flow confirms that for a very deep FCN it is beneficial to have both long and short skip connections. Finally, we show that a very deep FCN can achieve near-to-state-of-the-art results on the EM dataset without any further post-processing.

• The paper extends residual network architectures to FCNs by incorporating long and short skip connections that improve feature learning and spatial reconstruction.
• The paper empirically validates the approach on the EM ISBI 2012 dataset, achieving high scores with a foreground-restricted Rand score of 0.969 and an information theoretic score of 0.986 using Dice loss.
• The paper finds that combining skip connections accelerates convergence and stabilizes training in very deep network architectures.

The Importance of Skip Connections in Biomedical Image Segmentation

Overview

The paper "The Importance of Skip Connections in Biomedical Image Segmentation" by Michal Drozdzal et al. investigates the effectiveness of integrating both long and short skip connections within Fully Convolutional Networks (FCNs) to enhance semantic segmentation performance in biomedical imaging. The authors provide empirical evidence on the advantages of this network structure, particularly for tasks involving significant spatial reconstruction, such as those found in biomedical image segmentation.

Summary of Contributions

1. Extension of Residual Networks to FCNs: The paper extends the architecture of Residual Networks (ResNet) to FCNs, suited for semantic image segmentation, by adding an upsampling path. The integration of short skip connections, similar to those in residual networks, optimizes the very deep convolutional network's capacity for learning representational features efficiently.
2. Empirical Evaluation: The authors present robust empirical evaluations demonstrating that their augmented FCN, which employs both long and short skip connections, achieves performance on par with state-of-the-art methods on the EM ISBI 2012 dataset, often without the need for sophisticated post-processing techniques.
3. Analysis on Skip Connections: A thorough investigation into the benefits of long and short skip connections shows that these mechanisms facilitate faster convergence rates and stabilize training, particularly in very deep networks.

Methodology

Network Architecture

The proposed network architecture incorporates an expanding path for upsampling, mirroring the contracting path utilized for downsampling, typical in FCNs. This structure ensures the recovery of spatial information that might be lost during the downsampling process. Three primary types of blocks are utilized: bottleneck blocks, basic blocks, and simple blocks. These blocks integrate functionalities such as batch normalization, dropout, and short skip connections, contributing positively to training very deep networks.

Loss Functions

Two loss functions were evaluated: binary cross-entropy and Dice loss. The paper found that the Dice loss resulted in visually cleaner segmentations, suggesting that it could produce more binary results compared to cross-entropy loss, especially when combined with dropout during test time.

Training Setup

The model was implemented using Keras with a Theano backend and trained with the RMSprop optimizer. Data augmentation techniques such as random flipping, sheering, rotations, and spline warping were employed to enhance dataset variability and model robustness.

Experimental Results

The empirical results focus on segmenting electron microscopy (EM) data from the ISBI 2012 challenge. The dataset comprises 30 images for training and another 30 for testing. Key findings are as follows:

• Performance Metrics: The model achieved very competitive scores, with a maximal foreground-restricted Rand score ($V_{rand}$) of 0.969 and maximum foreground-restricted information theoretic score ($V_{info}$) of 0.986 using the Dice loss.
• Comparison with Existing Methods: The proposed method's performance aligns closely with top-ranking methods on the ISBI challenge leader board, often achieving high accuracy without extensive post-processing.
• Skip Connections Analysis: The analysis indicates that integrating both types of skip connections contributes to improved and stable convergence, confirming that short skip connections provide additional paths for gradient flow, thereby mitigating issues related to vanishing gradients.

Theoretical and Practical Implications

Practically, this research underscores the utility of adequately designed network architectures for medical image segmentation tasks, a field where accurate segmentation can be critical. Theoretically, it contributes to our understanding of how network depth and different types of skip connections can influence model performance and training dynamics.

Future Directions

Future research may focus on further optimizing skip connections for other segmentation tasks within medical imaging and beyond. Additionally, investigating other architectural innovations or combining short and long skip connections with other regularization or data augmentation techniques could yield further improvements.

In conclusion, the paper by Drozdzal et al. highlights the significant impact of skip connections on the performance of very deep FCNs for biomedical image segmentation, both confirming previous findings and paving the way for new research avenues in the efficient training of deep networks.