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Related Experiment Video

Updated: Jan 27, 2026

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

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Voxel Deconvolutional Networks for 3D Brain Image Labeling.

Yongjun Chen¹, Min Shi², Hongyang Gao³

¹Washington State University, Pullman, WA, USA, yongjun.chen@wsu.edu.

KDD : Proceedings. International Conference on Knowledge Discovery & Data Mining

|March 26, 2019

Summary

This study introduces the voxel deconvolutional layer (VoxelDCL) to address checkerboard artifacts in 3D deep learning for brain image labeling. VoxelDCL-based networks significantly improve prediction accuracy on volumetric brain datasets.

Keywords:

Deep learning volumetric brain image labeling voxel deconvolutional layer voxel deconvolutional networks

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Area of Science:

Medical Imaging
Computer Vision
Machine Learning

Background:

Deep learning excels at pixel-wise prediction, often using encoder-decoder networks with deconvolutional layers.
Deconvolutional layers in 2D face checkerboard artifacts, impacting accuracy.
Extending solutions to 3D is challenging due to exponential growth in feature maps.

Purpose of the Study:

To introduce a novel voxel deconvolutional layer (VoxelDCL) for mitigating checkerboard artifacts in 3D deep learning.
To develop and evaluate 3D convolutional networks incorporating VoxelDCL for volumetric brain image analysis.

Main Methods:

Proposed the voxel deconvolutional layer (VoxelDCL) to resolve checkerboard artifacts in 3D.
Developed four voxel deconvolutional networks (VoxelDCN) based on the U-Net architecture.
Applied VoxelDCNs to volumetric brain image labeling tasks on ADNI and LONI LPBA40 datasets.

Main Results:

The proposed iVoxelDCNa achieved improved performance, reaching 83.34% dice ratio on ADNI and 79.12% on LONI LPBA40.
VoxelDCN variations outperformed baseline methods on both datasets.
Demonstrated significant increases of 1.39% and 2.21% in dice ratio compared to the baseline.

Conclusions:

VoxelDCL effectively solves the checkerboard artifact problem in 3D deconvolutional layers.
VoxelDCNs show superior performance in volumetric brain image labeling tasks.
The proposed method offers an effective solution for 3D medical image analysis using deep learning.