Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data (1912.03681v2)

Abstract: CNN-based volumetric methods that label individual voxels now dominate the field of biomedical segmentation. However, 3D surface representations are often required for proper analysis. They can be obtained by post-processing the labeled volumes which typically introduces artifacts and prevents end-to-end training. In this paper, we therefore introduce a novel architecture that goes directly from 3D image volumes to 3D surfaces without post-processing and with better accuracy than current methods. We evaluate it on Electron Microscopy and MRI brain images as well as CT liver scans. We will show that it outperforms state-of-the-art segmentation methods.

• The paper introduces Voxel2Mesh, a novel end-to-end architecture that generates 3D surface meshes directly from volumetric data without relying on artifact-prone post-processing like Marching Cubes.
• Key innovations include Learned Neighborhood Sampling (LNS) and Adaptive Mesh Unpooling (AMU), which enable efficient and adaptive feature extraction and resolution adjustment for complex shapes.
• Voxel2Mesh achieves significant performance improvements over state-of-the-art methods on biomedical datasets, demonstrating superior accuracy and efficiency, especially with limited training data.

Insights into "Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data"

The paper "Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data" introduces a novel computational methodology designed to generate 3D surface meshes directly from volumetric imaging data, optimizing accuracy and efficiency in segmentation processes. The proposed architecture showcases significant improvements over existing methods, particularly addressing artifacts commonly induced by post-processing steps in volumetric-labeling approaches.

Technical Foundation

This research builds upon the convolutional neural network (CNN)-dominated scene of volumetric segmentation by offering an end-to-end trainable architecture for producing 3D surface meshes. Unlike traditional pipelines relying on algorithms like Marching Cubes for surface generation from labeled volumes, which are notorious for artifacts and non-differentiability causing ineffective end-to-end training, the presented approach circumvents these issues through direct computation from image volumes to surface meshes.

Architectural Advances

The Voxel2Mesh framework stands out with its two-stream encoder/decoder structure, comprising a voxel encoder, and two decoders: a voxel decoder, and a mesh decoder. The novel aspects at the core of Voxel2Mesh that bolster its performance include:

• Learned Neighborhood Sampling (LNS): This mechanism enables adaptive feature sampling around mesh vertices, learning optimal locations for feature extraction during training, enhancing surface detail where needed, while ensuring computational tractability.
• Adaptive Mesh Unpooling (AMU): By dynamically adjusting the mesh resolution specifically in regions of high curvature, this module ensures accurate representation without an exponential increase in computational resources—solving a key challenge in meshing large domains.

Empirical Evaluation

The efficacy of Voxel2Mesh was rigorously evaluated across multiple biomedical imaging datasets including Electron Microscopy, MRI, and CT scans, demonstrating notable performance improvements over state-of-the-art volumetric CNN architectures such as U-Net, TernausNet, and others. The architecture excelled particularly in settings with limited training data, outperforming baseline methods in intersection-over-union (IoU) scores significantly, showing precision without detriment to accuracy.

Implications and Future Directions

This paper asserts Voxel2Mesh as a transformative approach, specifically pertinent for biomedical applications where accurate surface morphology is crucial. The architecture's capacity to seamlessly generate accurate mesh representations promises enhancements in areas reliant on 3D modeling, such as in analyzing anatomical structures and progressing towards AI-driven diagnostics and surgery planning.

Looking ahead, the paper hints at potential developments such as extending capabilities to handle complex topologies beyond genus 0—essential for intricate biological structures. It opens avenues for further integration with diverse imaging modalities, and likely improvements in computational efficiency, leading toward real-time applications and broader adoption in clinical settings.

Conclusion

In sum, the methodological advancements brought forth by Voxel2Mesh represent significant strides in mesh modeling from volumetric data. This work sets a benchmark by facilitating a smoother pipeline from volumetric data to accurate 3D mesh representations, enhancing both the theoretical depth and application breadth in computer vision and biomedical imaging domains.