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Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

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Globally optimal tumor segmentation in PET-CT images: a graph-based co-segmentation method.

Dongfeng Han¹, John Bayouth, Qi Song

¹Department of Radiation Oncology, The University of Iowa, Iowa City, IA, USA. handongfeng@gmail.com

Information Processing in Medical Imaging : Proceedings of the ... Conference

|July 19, 2011

Summary

This study introduces a novel framework for simultaneous tumor segmentation using PET and CT scans. The method improves accuracy by leveraging the strengths of both imaging modalities, enhancing diagnostic precision.

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Area of Science:

Medical Imaging
Computational Biology
Radiology

Background:

Tumor segmentation in PET and CT images presents challenges due to low PET resolution and low CT contrast.
Accurate tumor delineation is crucial for effective cancer diagnosis and treatment planning.

Purpose of the Study:

To develop a general framework for simultaneous tumor segmentation using both PET and CT images.
To leverage the complementary strengths of PET (contrast) and CT (spatial resolution) for improved segmentation accuracy.

Main Methods:

A Markov Random Field (MRF) based segmentation framework was developed for image pairs.
A regularized term was incorporated to penalize segmentation differences between PET and CT.
The method employs a single maximum flow computation for globally optimal solutions.

Main Results:

The proposed method achieved a Dice similarity coefficient of 0.85.
The average median Hausdorff distance was 6.4 mm.
Demonstrated a 10% improvement over PET-only and 16% over CT-only segmentation using graph cuts.

Conclusions:

The framework effectively integrates PET and CT image information for superior tumor segmentation.
The method offers a significant improvement in segmentation accuracy compared to single-modality approaches.
This approach simulates clinical practice for concurrent tumor delineation, yielding optimal results efficiently.