Optimal Co-Segmentation of Tumor in PET-CT Images With Context Information

Abstract

PET-CT images have been widely used in clinical practice for radiotherapy treatment planning of the radiotherapy. Many existing segmentation approaches only work for a single imaging modality, which suffer from the low spatial resolution in PET or low contrast in CT. In this work we propose a novel method for the co-segmentation of the tumor in both PET and CT images, which makes use of advantages from each modality: the functionality information from PET and the anatomical structure information from CT. The approach formulates the segmentation problem as a minimization problem of a Markov Random Field (MRF) model, which encodes the information from both modalities. The optimization is solved using a graph-cut based method. Two sub-graphs are constructed for the segmentation of the PET and the CT images, respectively. To achieve consistent results in two modalities, an adaptive context cost is enforced by adding context arcs between the two subgraphs. An optimal solution can be obtained by solving a single maximum flow problem, which leads to simultaneous segmentation of the tumor volumes in both modalities. The proposed algorithm was validated in robust delineation of lung tumors on 23 PET-CT datasets and two head-and-neck cancer subjects. Both qualitative and quantitative results show significant improvement compared to the graph cut methods solely using PET or CT.

Globally optimal co-segmentation of three-dimensional pulmonary 1H and hyperpolarized 3He MRI with spatial consistence prior

“…Recently a graph-cut based method was developed to segment CT (anatomical) and PET (functional) images simultaneously over two discrete image graphs by enforcing an inter-surface context cost (Song et al, 2013). This approach made use of image information from both modalities and incorporated the introduced context cost to achieve spatial consistency.…”

Computerized PET/CT image analysis in the evaluation of tumour response to therapy

“…Han et al 42 developed a graph-based Markov random field segmentation with a regularized energy term that penalizes the segmentation difference between PET and CT. In recognition of possible differences of tumour volume defined in PET from that defined in CT, Song et al 43 extended the work of Han et al to generate tumour volumes in both modalities, rather than a compromised identical one (Figure 2). Both works required empirical determination of multiple parameters.…”

Efficient Segmentation of Lung Using FIDRM Filter and Successive Peak Quantization Transform

“…The location and the size of the organs are not contained in the structure information. Because of this structure information the identification of the foreground seeds are not facilitated and produced the ambiguity This work is motivated by Song's method [16] in which the labelling of Markov Random Field (MRF) is formulated on the graph on PET and CT images. The main advantage of this method is its good performance in capturing the fuzzy boundary of the tumour on PET images.…”