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Updated: Jun 28, 2025

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

Improving Image Segmentation with Contextual and Structural Similarity.

Xiaoyang Chen¹, Qin Liu², Hannah H Deng³

¹Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, 27599, NC, USA.

Pattern Recognition

|April 22, 2024

Summary

This summary is machine-generated.

New deep learning losses, contextual similarity loss (CSL) and structural similarity loss (SSL), improve medical image segmentation by considering relationships between voxels, leading to more consistent predictions.

Keywords:

Cone-beam computed tomography Image segmentation Inter-voxel relationships Pancreas segmentation

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Deep learning models for medical image segmentation typically use voxel-wise losses, neglecting inter-voxel relationships.
This can result in semantically inconsistent segmentation predictions.

Purpose of the Study:

To introduce novel loss functions, contextual similarity loss (CSL) and structural similarity loss (SSL), to enhance medical image segmentation.
To explicitly incorporate inter-voxel relationships for improved semantic consistency.

Main Methods:

CSL promotes consistent object category predictions across image sub-regions.
SSL enforces compatibility between voxel pair predictions using pair-wise distances in a distribution space.
Evaluated on cone-beam computed tomography (CBCT) for craniomaxillofacial (CMF) deformities and a public pancreas dataset.

Main Results:

CSL and SSL demonstrated superior performance compared to existing regional loss functions.
The proposed losses effectively preserve segmentation semantics.

Conclusions:

CSL and SSL offer a promising approach to improve the semantic consistency of deep learning-based medical image segmentation.
Incorporating inter-voxel relationships is crucial for robust segmentation performance.