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Tubularity flow field--a technique for automatic neuron segmentation.

Suvadip Mukherjee, Barry Condron, Scott T Acton

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|December 11, 2014

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We developed a new automated method, tubularity flow field (TuFF), for tracing neurons in microscopy images. TuFF significantly improves neuron segmentation accuracy, outperforming existing algorithms.

Area of Science:

Neuroscience
Biomedical Imaging
Computer Vision

Background:

High-throughput neuronal image analysis is crucial for neuroscience research.
Automated neuron segmentation from confocal microscopy images presents significant challenges due to signal variations and fragmentation.

Purpose of the Study:

To propose an automated segmentation framework for tracing neurons from confocal microscopy images.
To address signal variations and gaps in neurite structures for improved segmentation accuracy.

Main Methods:

Developed a novel segmentation technique named tubularity flow field (TuFF).
Employed a variational framework integrating directional region growing and an attraction force field using level sets.
TuFF utilizes the direction of tubularity for guided region growing and a local attraction force to bridge signal gaps.

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Main Results:

Achieved automated neuron segmentation without manual seed point selection.
Demonstrated superior performance on 2D and 3D confocal microscopy images.
Reported >75% improvement in mean absolute error compared to three existing neuron segmentation algorithms.

Conclusions:

The proposed tubularity flow field (TuFF) offers an effective automated solution for neuron segmentation.
The integrated variational approach successfully handles signal variations and neurite fragmentation.
Novel features like evolution and attraction forces show potential for broader image analysis applications.