Segmentation of Thoracic Organs

Overview

We present a novel method for the automatic segmentation of the thoracic cavity and the detection of human lungs and the major thoracic organs, as a necessary pre-processing step for a subsequent deformable registration scheme. Our method is divided into two parts. In the first part, a coarse separation of the body into two sub-regions, one with the skeleton, fat and skin and another with the lungs, the heart and the rest soft tissues is achieved by

approximating the rib cage with a c-spline curve. The second part employs curve-fitting methods in order to detect the boundaries of the lungs and the heart with better accuracy, based on the estimation of the first part.

Motivation

For a fast and efficient radiotherapy treatment planning of lung cancer, a successful registration procedure, which aligns the CT data and tracks accurately the thoracic volume during breathing cycle, is extremely essential for the radiologists.

Breathing motion changes dramatically the positions of the thoracic biological structures. During radiotherapy the position of the soft tissues and the tumor have to be defined as the x-rays beam direction has to be accurate. Registration procedure intends to capture the tissues deformations and displacements during breath cycle and to give information about the magnitude and the direction of these changes which will be used and evaluated from the clinician during planning process.

Currently deformable registration formulations fail to give accurate results when it is applied in the whole thoracic cavity. However, segmentation of the thoracic region into two subregions and then application of the registration algorithm in each subregion separately can give a solution.

In this project our aim was to segment the thoracic cavity as a pre-step for the registration process.

This work was a contribution to the image registration project for the tracking of lung tumors which is held at CMIC group -UCL (http://cmic.cs.ucl.ac.uk/).

Description

In the first part we have a coarse distinction of the body into two sub-regions.

The one sub-region consists the outer thoracic part

with the bones and the fat tissues and the other sub-region

(the inner thoracic part) with the lungs, the heart and some

other tissues and structures like the aorta.

For the initialization of the zero level function which will extract the heart we also have to define an initial small region inside the rib cage where the heart is positioned. For this purpose, a naive Bayes classifier is used to define which of the gray value pixels inside the estimated rib cage are most probable to belong to the heart. The pixels with the highest probability are used for the heart zero level function initialization. In the case of the lungs, the zero level function is initialized using the rib cage curve and the heart zerolevel set function.