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Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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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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Published on: February 18, 2015

A framework based on hidden Markov trees for multimodal PET/CT image co-segmentation.

Houda Hanzouli-Ben Salah¹, Jerome Lapuyade-Lahorgue¹, Julien Bert¹

¹INSERM, UMR 1101, LaTIM, IBSAM, UBO, UBL, Brest, France.

Medical Physics

|August 25, 2017

Summary

This summary is machine-generated.

This study introduces a hidden Markov tree (HMT) framework for accurate PET/CT image segmentation. The HMT method, particularly with contourlet enhancement, significantly improves gross tumor target delineation in lung cancer patients.

Keywords:

Bayesian inference computed tomography (CT)hidden Markov trees (HMT)positron emission tomography (PET)segmentation wavelet and contourlet analysis

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Area of Science:

Medical Imaging
Computational Biology
Radiology

Background:

Accurate segmentation of tumors in medical images is crucial for effective cancer treatment planning.
Multimodality imaging, combining Positron Emission Tomography (PET) and Computed Tomography (CT), offers complementary information for improved tumor delineation.
Existing segmentation methods often face challenges in computational speed, robustness, and interpretability.

Purpose of the Study:

To investigate a probabilistic quad-tree graph, the hidden Markov tree (HMT), for efficient and robust multimodality image processing.
To evaluate the HMT framework's performance in delineating single gross tumor targets (GTV) from PET/CT images.
To establish an interpretable framework for PET/CT image analysis.

Main Methods:

Utilized a multi-observation, multi-resolution HMT with Bayesian inference to exploit joint statistical dependencies between hidden states.
Applied the HMT framework to segment lung tumors in PET/CT datasets, incorporating both CT and PET image information.
Assessed performance using Dice Similarity Coefficient (DSC), sensitivity (SE), and positive predictive value (PPV) on simulated and clinical datasets, comparing against Fuzzy C-Means (FCM).

Main Results:

The HMT framework demonstrated high agreement with expert manual delineations when using both PET and CT images.
Contourlet-enhanced HMT achieved the highest performance with a DSC of 0.92 ± 0.11.
Using PET or CT alone resulted in significantly lower accuracy, and HMT outperformed standard and improved FCM methods.

Conclusions:

The HMT-based framework provides accurate segmentation for PET/CT images, particularly for gross tumor target delineation.
Pre-processing images in the contourlet domain enhances the accuracy of the HMT segmentation method.
The proposed HMT approach offers a robust and efficient solution for multimodality medical image analysis.