Hybrid positron emission tomography segmentation of heterogeneous lung tumors using 3D Slicer: improved GrowCut algorithm with threshold initialization

Hannah Mary T. Thomas; Devadhas Devakumar; Balukrishna Sasidharan; Danie Kingslin Heck; Stephen R. Bowen; E. James Jebaseelan Samuel

doi:10.1117/1.JMI.4.1.011009

23 January 2017 Hybrid positron emission tomography segmentation of heterogeneous lung tumors using 3D Slicer: improved GrowCut algorithm with threshold initialization

Hannah Mary T. Thomas, Devadhas Devakumar, Balukrishna Sasidharan, Danie Kingslin Heck, Stephen R. Bowen, E. James Jebaseelan Samuel

Author Affiliations +

Journal of Medical Imaging, Vol. 4, Issue 1, 011009 (January 2017). https://doi.org/10.1117/1.JMI.4.1.011009

Abstract

This paper presents an improved GrowCut (IGC), a positron emission tomography-based segmentation algorithm, and tests its clinical applicability. Contrary to the traditional method that requires the user to provide the initial seeds, the IGC algorithm starts with a threshold-based estimate of the tumor and a three-dimensional morphologically grown shell around the tumor as the foreground and background seeds, respectively. The repeatability of IGC from the same observer at multiple time points was compared with the traditional GrowCut algorithm. The algorithm was tested in 11 nonsmall cell lung cancer lesions and validated against the clinician-defined manual contour and compared against the clinically used 25% of the maximum standardized uptake value [SUV-(max)], 40% SUVmax, and adaptive threshold methods. The time to edit IGC-defined functional volume to arrive at the gross tumor volume (GTV) was compared with that of manual contouring. The repeatability of the IGC algorithm was very high compared with the traditional GrowCut (p=0.003) and demonstrated higher agreement with the manual contour with respect to threshold-based methods. Compared with manual contouring, editing the IGC achieved the GTV in significantly less time (p=0.11). The IGC algorithm offers a highly repeatable functional volume and serves as an effective initial guess that can well minimize the time spent on labor-intensive manual contouring.

Citation Download Citation

Hannah Mary T. Thomas, Devadhas Devakumar, Balukrishna Sasidharan, Danie Kingslin Heck, Stephen R. Bowen, and E. James Jebaseelan Samuel "Hybrid positron emission tomography segmentation of heterogeneous lung tumors using 3D Slicer: improved GrowCut algorithm with threshold initialization," Journal of Medical Imaging 4(1), 011009 (23 January 2017). https://doi.org/10.1117/1.JMI.4.1.011009

Received: 10 June 2016; Accepted: 20 December 2016; Published: 23 January 2017

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