Deep Vessel Segmentation By Learning Graphical Connectivity (1806.02279v1)

Abstract: We propose a novel deep-learning-based system for vessel segmentation. Existing methods using CNNs have mostly relied on local appearances learned on the regular image grid, without considering the graphical structure of vessel shape. To address this, we incorporate a graph convolutional network into a unified CNN architecture, where the final segmentation is inferred by combining the different types of features. The proposed method can be applied to expand any type of CNN-based vessel segmentation method to enhance the performance. Experiments show that the proposed method outperforms the current state-of-the-art methods on two retinal image datasets as well as a coronary artery X-ray angiography dataset.

• The paper introduces a novel Vessel Graph Network (VGN) that combines CNN-based feature extraction with GCNs to capture both local appearance and vessel connectivity.
• The methodology constructs graphs from skeletonized vessel maps, ensuring a precise representation of intricate vascular networks.
• Experimental results on retinal and angiography datasets demonstrate that the approach outperforms traditional CNN models in precision and recall.

Deep Vessel Segmentation by Learning Graphical Connectivity

The paper "Deep Vessel Segmentation by Learning Graphical Connectivity" introduces a novel approach in the domain of medical image analysis, focusing specifically on the segmentation of blood vessels. By integrating a Graph Convolutional Network (GCN) with a Convolutional Neural Network (CNN), the authors aim to tackle the limitations of existing methods that predominantly rely on learning local appearances on a regular image grid without accounting for the vessel's structural connectivity.

Summary of the Methodology

The proposed Vessel Graph Network (VGN) incorporates a CNN module to generate pixelwise feature maps and vessel probability maps. In parallel, a GCN module operates on an irregular graph constructed from the skeletonized output of the CNN's initial segmentation. The GCN leverages the connectivity among neighboring vertices to improve vessel classification by integrating features reflecting both local appearance and global vessel structure.

1. Graph Construction: The VGN constructs graphs by thresholding vessel probability maps, skeletonizing them, and sampling equidistant vertices from the skeleton lines. This ensures that the entire vessel network is accurately represented in graph form.
2. Architecture Components:
   • CNN Module: The CNN is responsible for initial feature extraction and probability mapping of vessel presence in images. This module is based on the VGG-16 architecture, tailored to align with the requirements of vessel segmentation tasks.
   • GCN Module: This module enhances the representation of vessel structures based on neighborhood connectivity, which is crucial for capturing the elongated and branching nature of blood vessels.
   • Inference Module: The final segmentations are inferred by merging features from both CNN and GCN modules, ensuring that the vessel connectivity, learned by the GCN, enriches the pixel-level predictions of the CNN.
3. Training Protocol: The network undergoes a two-phase training regime: a standalone pretraining phase for the CNN, followed by joint training with the GCN using a compound loss function. This sequential strategy allows fine-tuning of the entire VGN, optimizing the integration between local and global features.

Experimental Evaluation

The effectiveness of the VGN is validated across datasets from different medical imaging modalities, namely retinal images (DRIVE and STARE datasets) and coronary artery X-ray angiography. Performance metrics such as Precision-Recall curves and Average Precision scores are used to benchmark against state-of-the-art methods. The results demonstrate that the VGN consistently outperforms traditional CNN-based models in vessel segmentation tasks, maintaining higher precision and recall across various datasets.

Implications and Future Directions

The integration of GCN into the vessel segmentation process represents a significant methodological advancement. By contextualizing pixel-wise predictions with neighborhood relationships, the VGN mitigates common segmentation errors such as discontinuities in vessel paths and false positives in complex anatomical areas.

The practical implications of this research are profound. Improving the accuracy and robustness of vessel segmentation can enhance diagnostic workflows in ophthalmology and cardiology, particularly for conditions where vascular morphology is a key clinical indicator.

Looking forward, the framework laid out by this paper could be adapted for more complex 3D imaging modalities, such as CT angiographies, and potentially incorporate temporal data from fluoro-cine sequences. Additionally, with further optimization, such GCN-CNN hybrids might find applications beyond vessel segmentation, extending into other domains needing enhanced spatial awareness and structural understanding within imagery data.

This paper not only contributes to the immediate goal of improving vessel segmentation but also sets the stage for future research employing GCNs in conjunction with CNNs for comprehensive feature learning across varied image-based tasks in artificial intelligence.