Fully automatic algorithm for segmenting full human diaphragm in non-contrast CT Images

Elham Karami; Stewart Gaede; Ting-Yim Lee; Abbas Samani

doi:10.1117/12.2082634

17 March 2015 Fully automatic algorithm for segmenting full human diaphragm in non-contrast CT Images

Elham Karami, Stewart Gaede, Ting-Yim Lee, Abbas Samani

Proceedings Volume 9417, Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging; 941709 (2015) https://doi.org/10.1117/12.2082634
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

The diaphragm is a sheet of muscle which separates the thorax from the abdomen and it acts as the most important muscle of the respiratory system. As such, an accurate segmentation of the diaphragm, not only provides key information for functional analysis of the respiratory system, but also can be used for locating other abdominal organs such as the liver. However, diaphragm segmentation is extremely challenging in non-contrast CT images due to the diaphragm's similar appearance to other abdominal organs. In this paper, we present a fully automatic algorithm for diaphragm segmentation in non-contrast CT images. The method is mainly based on a priori knowledge about the human diaphragm anatomy. The diaphragm domes are in contact with the lungs and the heart while its circumference runs along the lumbar vertebrae of the spine as well as the inferior border of the ribs and sternum. As such, the diaphragm can be delineated by segmentation of these organs followed by connecting relevant parts of their outline properly. More specifically, the bottom surface of the lungs and heart, the spine borders and the ribs are delineated, leading to a set of scattered points which represent the diaphragm's geometry. Next, a B-spline filter is used to find the smoothest surface which pass through these points. This algorithm was tested on a noncontrast CT image of a lung cancer patient. The results indicate that there is an average Hausdorff distance of 2.96 mm between the automatic and manually segmented diaphragms which implies a favourable accuracy.

Citation Download Citation

Elham Karami, Stewart Gaede, Ting-Yim Lee, and Abbas Samani "Fully automatic algorithm for segmenting full human diaphragm in non-contrast CT Images", Proc. SPIE 9417, Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging, 941709 (17 March 2015); https://doi.org/10.1117/12.2082634

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available