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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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SEGMENTATION OF ORGANS AT RISK IN THORACIC CT IMAGES USING A SHARPMASK ARCHITECTURE AND CONDITIONAL RANDOM FIELDS.

R Trullo^1,2, C Petitjean¹, S Ruan¹

¹Normandie Univ, UNIROUEN, UNIHAVRE, INSA Rouen, LITIS, 76000 Rouen, France.

Proceedings. IEEE International Symposium on Biomedical Imaging

|October 25, 2017

Summary

This summary is machine-generated.

This study introduces a deep learning framework for joint segmentation of organs at risk (OAR) in thoracic CT scans. The method improves accuracy by combining local and global information and considering organ relationships for better radiotherapy planning.

Keywords:

CRF CRFasRNN CT Segmentation Fully Convolutional Networks (FCN)

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiotherapy

Background:

Cancer treatment relies heavily on radiotherapy, a process requiring precise identification of target volumes and organs at risk (OAR).
Current automatic OAR segmentation methods often use local information and segment organs individually, limiting accuracy and efficiency.
Accurate segmentation of thoracic OARs like the heart, esophagus, trachea, and aorta is crucial for effective radiotherapy planning.

Purpose of the Study:

To develop and evaluate a novel deep learning framework for the joint segmentation of multiple OARs in thoracic CT images.
To improve the accuracy and efficiency of OAR segmentation by integrating local and global information and modeling inter-organ relationships.

Main Methods:

A deep learning framework utilizing Fully Convolutional Networks (FCN) was proposed for joint OAR segmentation.
A new architecture was developed to effectively combine low-level and high-level features, integrating local and global contextual information.
Conditional Random Fields (CRF) as a Recurrent Neural Network model were employed to leverage relationships between segmented organs.

Main Results:

The proposed deep learning framework demonstrated competitive performance in the joint segmentation of thoracic OARs.
The integration of local and global information enhanced localization accuracy.
Accounting for inter-organ relationships using CRF further improved segmentation outcomes.

Conclusions:

The developed deep learning framework offers a promising approach for accurate and efficient joint segmentation of thoracic organs at risk.
This method has the potential to enhance radiotherapy planning by providing more precise delineation of OARs.
The framework's ability to combine diverse information sources and model organ dependencies represents a significant advancement in medical image segmentation.