Multi-shape graph-cuts and its application to lung segmentation from a chest CT volume
نویسندگان
چکیده
This paper presents a novel graph-cuts algorithm that can take into account a multiple-shape constraint and reports a lung segmentation process from a three-dimensional computed tomography (CT) image, based on the graph-cuts algorithm. A major contribution of this paper is the proposal of a segmentation algorithm that can consider multiple shapes in a graph-cuts framework. Using experiments, we demonstrate the effectiveness of the proposed multi-shape graph-cuts by comparing them to conventional singleshape graph-cuts using a synthetic image and clinical thoracic CT volumes.
منابع مشابه
Evaluation of methods of co-segmentation on PET/CT images of lung tumor: simulation study
Introduction: Lung cancer is one of the most common causes of cancer-related deaths worldwide. Nowadays PET/CT plays an essential role in radiotherapy planning specially for lung tumors as it provides anatomical and functional information simultaneously that is effective in accurate tumor delineation. The optimal segmentation method has not been introduced yet, however several ...
متن کاملA proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging.
This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multi-physician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STA...
متن کاملAutomatic Lung Segmentation of Volumetric Low-Dose CT Scans Using Graph Cuts
We propose a new technique for unsupervised segmentation of the lung region from low dose computed tomography (LDCT) images. We follow the most conventional approaches such that initial images and desired maps of regions are described by a joint Markov-Gibbs random field (MGRF) model of independent image signals and interdependent region labels. But our focus is on more accurate model identific...
متن کاملFast 4D segmentation of large datasets using graph cuts
In this paper, we propose to use 4D graph cuts for the segmentation of large spatio-temporal (4D) datasets. Indeed, as 4D datasets grow in popularity in many clinical areas, so will the demand for efficient general segmentation algorithms. The graph cuts method has become a leading method for complex 2D and 3D image segmentation in many applications. Despite a few attempts in 4D, the use of gra...
متن کاملReducing radiation doses in female breast and lung during CT examinations of thorax: A new technique in two scanners
Background: Chest CT is a commonly used examination for the diagnosis of lung diseases, but a breast within the scanned field is nearly never the organ of interest.Objective: The purpose of this study is to compare the female breast and lung doses using split and standard protocols in chest CT scanning.Materials and Methods: The sliced chest and breast female phantoms were used. CT exams were p...
متن کامل