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Updated: Oct 25, 2025

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

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Deep negative volume segmentation.

Kristina Belikova¹, Oleg Y Rogov¹, Aleksandr Rybakov²

¹Skolkovo Institute of Science and Technology, Bolshoy Blvd., 30/1, Moscow, Russia, 121205.

Scientific Reports

|August 12, 2021

Summary

This study introduces negative volume segmentation for 3D temporomandibular joint (TMJ) analysis, automating segmentation and providing a novel metric for joint health assessment.

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Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

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Area of Science:

Medical imaging analysis
Computational anatomy
Biomedical engineering

Background:

Manual 3D segmentation of complex anatomical structures like the temporomandibular joint (TMJ) is time-consuming and requires specialized expertise.
Existing methods struggle with the TMJ's intricate 3D shape, varied textures, surrounding skull irregularities, and extensive jaw motion, leading to over an hour of manual annotation per patient.

Purpose of the Study:

To develop an automated and efficient workflow for 3D segmentation of the temporomandibular joint (TMJ).
To introduce a novel 'negative volume segmentation' approach for assessing joint health.
To provide a geometrical/topological metric for evaluating joint health and asymmetry.

Main Methods:

Proposed an end-to-end pipeline utilizing a V-Net for bone segmentation.
Implemented a 3D volume construction technique involving inflation of the reconstructed bone head along normal vectors.
Segmented the empty spaces (negative volume) between tissues surrounding the TMJ.

Main Results:

Validated the negative volume segmentation approach on CT scans from a 50-patient dataset.
Quantitatively compared left and right negative volumes to assess joint asymmetry.
Demonstrated the potential for automating the entire framework for clinical use.

Conclusions:

Negative volume segmentation offers an effective and automated solution for 3D TMJ analysis.
The method provides a novel metric for evaluating joint health and asymmetry.
The automated framework shows promise for clinical adoption in maxillofacial medicine.