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

Abstract

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 threedimensional 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% SUV max , 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 ¼ 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 ¼ 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.

Segmenting lung parenchyma from CT images with gray correlation‐based clustering

“…With the features extracted from both PET and CT images, tumors were concurrently segmented from both modalities. Hm et al [16] improved an interactive semiautomatic grow cut algorithm with a threshold-based initialization. Most of the graph cuts-based image segmentation methods are interactive, and users need to mark certain pixels as foreground or background.…”

Framework for machine learning of ct and pet radiomics to predict local failure after radiotherapy in locally advanced head and neck cancers

“…The final threshold value was calculated using the Growcut algorithm as described in the article by Thomas et al . [ 17 ] The PET contour was propagated to the planning CT. Minor edits were made if required to include any of the regions not included in the PET to generate the gross tumor volume (GTV), which is the extent of the tumor as seen on the CT. The display protocol was a fixed window/level setting for the CT scan (brain window).…”

3D printing of cardiac structures from medical images: an overview of methods and interactive tools

“…Interactive medical image segmentation employs software tools such as, for instance, Seg3D [52] and 3D-Slicer [53] for applying algorithms and, at the same time, visualizing the results (see Fig. 5).…”